Cell division patterns shape developing organs, but their systematic analysis remains limited by the technical difficulty of existing detection methods. Here, we present a practical method for inferring recent daughter-cell pairs from static cell shapes. Although cell divisions have long been inferred by expert judgment, the accuracy of this approach has rarely been quantified. Using live imaging of Arabidopsis leaf primordia, we evaluated 49 features from 7,573 neighboring cell pairs, including 1,114 direct daughter pairs and 6,459 non-daughter pairs. A simple junction-angle criterion correctly inferred 1,030 daughter pairs with 94.1% precision and 92.5% recall, whereas symmetry-based indices performed poorly. Temporal analysis revealed that junction angles were initially high after division, decreased during growth, and were altered by subsequent divisions, thus, separating daughter pairs from non-daughter pairs. This method also achieved 98.4% precision and 97.2% recall in an independent shoot apical meristem live-imaging dataset. Application to the pointed-tip Arabidopsis mutant rpl4d-3 and two non-model species further supported its potential applicability for analyzing division patterns in diverse leaf morphologies. For community use, we provide FIJI/ImageJ and Qt-based graphical user interface implementations. Overall, this study quantitatively validates empirical geometric cues and provides a practical framework for inferring daughter pairs from static tissue images.
Premise Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested two hypotheses: (H1) whether higher tissue water content leads to greater measurement changes after herborization and (H2) whether fresh trait values can be reliably predicted from measurements of herborized specimens. Methods We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least squares models and machine learning regressions were used to test H1 and H2. Results Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from measurements of herborized specimens. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion These results partially support H1 and support H2, indicating that herborized specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Reproduction assets foundThe paper's trait measurements (Appendix S6 species mean data) and analysis code are openly available in the authors' GitHub repository, explicitly stated in the Data Availability Statement.Code · publice also acknowledge the Sociedade Botânica do Brasil (SBB) for financial support through the Scientia Amabilis 2024 grant.
DATA AVAILABILITY STATEMENT
The data supporting the findings of this study are available in the Supporting Information of this article (Appendix S6 ). Additionally, all data and code are openly accessible at https://github.com/ykilsztajn/fresh_dry_myrtaceae .
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AguiOpen asset ↗ykilsztajn/fresh_dry_myrtaceaelines:268-513Plant phenotyping relevance matchCrossref · checked 17 Sept 2026
Published15 Sept 2026Black Sea Journal of Agriculture
Leaf area (LA) is one of the most important morphological traits for the assessment of plant growth, biomass production and physiological performance. For forage crops, rapid, accurate and non-destructive methods of LA estimation are particularly important to allow repeated measurements throughout the growing season. The aim of this study was to compare the leaf area estimation of sainfoin (Onobrychis viciifolia Scop.) by four regression models (linear, logarithmic, polynomial and multiple linear regression) based on simple plant morphological measurements. Ninety sainfoin plants were evaluated at the flowering stage under controlled conditions. Plant height and canopy width were measured manually and actual leaf area was measured with a LI-COR LI-3100C Leaf Area Meter. Regression analyses were performed using SPSS software to construct prediction models and compare the performance of the models. Of the models tested, the multiple linear regression model had the highest prediction accuracy (R²=0.981), which was significantly better than the polynomial (R²=0.413), linear (R²=0.399) and logarithmic (R²=0.361) models. The results demonstrated that the combination of plant height and canopy width greatly improved the leaf area estimation over the use of a single predictor. The predictive performance of simple regression models was low. Multiple linear regression was a reliable, rapid and non-destructive method to estimate the leaf area of sainfoin. These results pave the way for practical morphological models that could assist plant growth monitoring and agronomic studies while limiting the number of destructive samplings.
Nadeem Fareed · Carlos Alberto Silva · Alexander J. Gaskins · Susan J. Prichard · Andrew T. Hudak · Jinyi Xia · Cesar Ivan Alvites Alvites Diaz
Field / plotLiDAR / point cloudLeafStem / branchMorphology / geometry measurementSegmentationArchitecture / morphology / geometry
Terrestrial Light Detection and Ranging (LiDAR) provides detailed three-dimensional (3D) observations of forest structure, yet transferable wood–foliar semantic segmentation remains challenging because of forest structural heterogeneity, occlusion, and variability across forest ecosystems and terrestrial LiDAR platforms. Existing Deep Learning (DL) approaches are commonly developed for localized forest conditions or complex multi-class semantic taxonomies, often limiting their transferability to structurally distinct forests. Here, we introduce points2SBL, a geometry-driven, terrestrial LiDAR platform-agnostic DL framework that reduces heterogeneous forest vegetation to a transferable wood–foliar representation and subsequently uses these semantics as the foundation for 3D forest fuel characterization. The framework was developed using globally harmonized benchmark datasets acquired across multiple terrestrial LiDAR systems and broadleaf, coniferous, mixed, regenerating, and structurally complex forests. Three point-based architectures (PointNet++, PointNeXt, and Point Transformer) were benchmarked under identical training and evaluation protocols. Point Transformer provided the most consistent cross-dataset performance, achieving overall accuracy (OA) of 92–94%, mean Intersection-over-Union (mIoU) of 0.78–0.87, macro F1-score of 0.87–0.93, and Matthews correlation coefficient (MCC) of 0.74–0.86 across five independent benchmark datasets. Species-level, vertical-profile, and qualitative cross-ecosystem assessments further demonstrated consistent preservation of wood–foliar semantics throughout the 3D forest fuel continuum, including previously unseen forest point clouds, while revealing that some apparent semantic disagreements originated from manual annotation of benchmarks. Finally, the points2SBL wood–foliar semantics were geometrically decomposed using a geometric algorithm named “Points2woodyseg” into fuel-related components: stem, branches, leaf, surface wood, and surface foliage. Collectively, these results demonstrate that a simplified wood–foliar abstraction can provide a transferable semantic foundation for characterizing structurally heterogeneous terrestrial LiDAR point clouds and deriving essential 3D forest fuel information across diverse forest ecosystems.
Introduction Accurate extraction of maize seedling phenotypes is essential for early growth assessment, yet field-grown seedlings exhibit complex backgrounds, non-rigid leaf deformation, self-occlusion, and adhered leaves that challenge three-dimensional point-cloud analysis. Methods Here, we developed a workflow integrating PMSPA-Net, an enhanced PointNet++ model, with geometric region decomposition for semantic and organ-level segmentation of field-grown maize seedlings. Multi-view RGB images of 65 complete plants were reconstructed with OpenMVS, and all data were partitioned at the whole-plant level before point-block sampling. Fifteen plants were retained as a fixed independent test set, while the remaining samples were used for training and validation across three predefined random seeds. Results Under a common protocol, PMSPA-Net outperformed PointNet, PointNet++, DGCNN, and RandLA-Net, achieving an mIoU of 0.8286 ± 0.0149, a macro-F1 of 0.8981 ± 0.0238, and a balanced accuracy of 0.9561 ± 0.0223. A four-configuration ablation study showed complementary gains from the multi-scale spatial pyramid attention module and IOA-based hyperparameter optimization. Geometric region decomposition also achieved more accurate and complete adhered-leaf separation than DBSCAN in three annotated cases. Point-cloud-derived leaf length and width agreed well with manual measurements. Discussion This workflow demonstrates the feasibility of integrated semantic and organ-level phenotyping under the evaluated field conditions; validation across additional sites, developmental stages, and acquisition conditions is required before broader generalization.
Diaz-Garcia, L. · Munoz, J. R. · Torres-Lomas, E. · McElrone, A. J. · Sharma, S. · Lupo, Y.
GrapevineRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architectureWater status / transpiration
Root system architecture shapes how grapevine rootstocks take up water and nutrients, yet roots remain the least phenotyped grapevine organ because they are hidden and hard to image. We present a low-cost phenotyping pipeline that pairs custom acrylic rhizotrons (about US$30 each) with a consumer flatbed scanner and BiRefNet, a general-purpose deep-learning model used without training on root images, followed by automated mask cleaning, skeleton-based trait extraction, and soil moisture mapping. We tested it on nine commercial rootstocks scanned 16 times over 42 days after transplanting (DAT), with half under a ten-day water deficit. From 1,108 images we extracted 21 whole-root, depth-resolved, and topological traits. Genotypes differed in nearly every trait and in how they changed over time. Heritability of size and branching traits peaked at 0.92-0.93 between 21 and 31 DAT and fell for width, depth, and convex hull once roots reached the rhizotron walls, defining the best measurement window. The image-derived soil moisture map accurately tracked the deficit and its recovery. Deficit plants shifted new root growth to deeper soil without growing less overall, and the substrate dried fastest around older and denser roots. Root brightness decreased with root age and local moisture, and transport segments (axes serving several tips) were brighter than terminal laterals in every genotype. Root system size was associated with stomatal conductance in well-watered plants, and stomatal recovery after re-watering correlated with new root growth. The pipeline turns simple hardware into a quantitative, time-resolved root phenotyping platform suitable for breeding.
This study aimed to evaluate the growth responses of kenaf (Hibiscus cannabinus L.) under nitrogen and compost treatments and to develop a UAV-based plant height estimation model. Ground-measured plant height differences were not significant at harvest (110 days after sowing, DAS), highlighting the need for multitemporal monitoring. Multispectral drone imagery was acquired at five growth stages (20–110 DAS). Object-based image segmentation was applied to extract pure vegetation areas, and digital surface model differencing (ΔDSMt) was used to reduce micro-topographic effects. A UAV-based multiple linear regression (UAV-MLR) model was developed using ΔDSMt, NDVI, GNDVI, and NGRDI to integrate complementary structural and spectral information. Evaluated on the calibration dataset, the UAV-MLR model demonstrated high fitting performance (adjusted R2 = 0.9858, RMSE = 14.95 cm, MAE = 11.31 cm), outperforming the ground-based simple linear regression (G-SLR) model based on stem diameter (adjusted R2 = 0.9615, RMSE = 39.68 cm, MAE = 29.16 cm). By integrating structural and spectral information, the proposed approach reduced RMSE by 62.3%, offering a highly accurate, non-destructive tool for crop monitoring and precision agriculture.
Background and Aims: Pollen germination and tube growth are critical stages of plant reproduction that are highly sensitive to temperature but remain labor-intensive to quantify. This study aimed to develop a deep learning-based approach for pollen phenotyping and to characterize the temperature dependence of pollen germination and elongation in Douglas-fir (Pseudotsuga menziesii). Methods: A convolutional neural network (CNN) was trained to segment pollen grains and classify germination status from microscopic images. Germination percentage and pollen length were quantified across a range of incubation temperatures from 5 to 40°C. Gamma functions were fitted to temperature response curves to estimate the optimal temperatures for pollen germination and elongation among Douglas-fir populations collected across an elevational gradient. Key Results: The CNN achieved high segmentation accuracy (intersection over union = 0.846), accurately distinguishing pollen grains from the background but showing moderate accuracy in separating germinated from ungerminated pollen during early elongation. Both pollen germination and elongation exhibited bell-shaped temperature response curves with distinct thermal optima. Pollen elongation consistently reached its optimum at higher temperatures than pollen germination. Estimated optimal temperatures fell within a narrow range, approximately 19 to 23°C. No significant relationship was detected between elevation and thermal optima, although some higher-elevation populations exhibited lower optimal temperatures. Comparisons with previous analyses of three western North American conifers showed that each species occupied a distinct reproductive thermal niche corresponding to the spring temperatures of its native habitat. Conclusions: Deep learning provides an efficient approach for high-throughput quantification of pollen germination and elongation from microscopic images. The narrow thermal range for reproductive performance suggests that Douglas-fir pollen is sensitive to temperature variation and that warming climates may alter reproductive success. These findings improve our understanding of the thermal sensitivity of conifer reproduction and provide a scalable framework for assessing impacts of climate warming on forest regeneration.
Many labor-intensive tasks in fruit production such as pruning require physically interacting with the plant (e.g., pushing, pulling, bending limbs, etc.). Due to increasing labor shortages, there is widespread interest in the adoption of robotics in this area. When the robot must physically interact with the system, a deformable model of the plant is beneficial. Coupled, spring-loaded beams have been used in prior work to build deformable plant models in simulation for learning, planning, and control, but this approach has rarely been validated against the mechanical properties of real-world plants. In this paper, we use this rigid-body model, grounded in real material properties and beam bending theory, to simulate the deformation of blueberry canes under loading. To validate the model, we simulate the canes in MuJoCo and compare their behavior with real-world data collected from probing canes at a commercial farm with a custom testbed. We perform sensitivity analysis on several key modeling variables and show that this approach is highly sensitive to the plant diameter calculations and a priori flexural modulus estimation, which is dependent on season and blueberry variety. We also share our dataset of live blueberry cane deformation, including RGB-D images and measured forces and displacements.
Reproduction assets foundThe paper shares its in-field blueberry cane deformation dataset (RGB-D images, measured forces and displacements) via a figshare link provided in a footnote. Other allowed URLs (CloudCompare, SALib references) are generic tools/citations, not paper-specific assets.Dataset · publicionally, we performed sensitivity analysis on several modeling variables to identify potential significant sources of error.
In summary, the contributions of this paper are:
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A dataset capturing live blueberry cane deformation (visual deformation under load, plus measured forces and displacements) 1 1
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Data can be accessed at https://figshare.com/s/f82e41c7115187e42df8 .
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Evaluation of the physics model against real-world data collected from a commercial blueberry farm with a custom testbed.
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Sensitivity analysis of several variables for lumped-parameter rigid-body models.
This paper explores the impact of season, age, and variety on the flexural modulus of blueberry plants, and its impaOpen asset ↗figsharelines:63-82Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Kawai T, Teramoto S, Ma X, Fukushima D, Hmwe KK, Kimani SM, Tokida T, Uga Y.
RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Published10 Sept 2026ISPRS Journal of Photogrammetry and Remote SensingCited by 0 · OpenAlex ↗
Accurate and dynamic monitoring of wheat phenotypes is essential for breeding decision-making and crop management. However, RGB image-based phenotyping still suffers from expensive pixel-level annotation, unstable organ-level segmentation across growth stages, and limited multi-trait extraction under complex field conditions. To address these issues, a high-throughput phenotyping framework (WheatScoper) was proposed, enabling organ-level segmentation and plot-level multi-trait extraction. To reduce annotation cost, a structure-aware geometry-assisted annotation (SAGA) algorithm was developed, yielding an approximately 7.6-fold improvement in annotation efficiency over fully manual annotation. To enable efficient organ-level segmentation, a lightweight semantic segmentation network (WheatScopeNet) was developed by integrating parallel hybrid spatial modeling with cross-scale feature fusion. On the held-out test set from the same site and growing season, WheatScopeNet achieved an mIoU of 0.869 and an mDice of 0.930. Leveraging the segmentation results, an automated system was established to extract 41 multi-view image-derived traits (I-traits) across four core phenotypic dimensions. The extracted I-traits supported the estimation of eight manually measured agronomic traits, with R 2 values ranging from 0.477 to 0.697. Notably, the correlations between stay-green-related-traits and yield varied distinctly with viewing-position. Only upper side-view indicators remained significantly correlated with yield, with Side-up final GPAR showing the strongest association, whereas top-view GPAR-derived indicators showed weak associations. Finally, a web-based platform integrating cascaded inference, segmentation visualization, and automatic I-trait extraction was developed. The platform provides an end-to-end solution for field wheat phenotyping and supports breeding decision-making and crop management.
Abstract Background : Seed size, shape, and colour are key determinants of Bambara groundnut (BGN) seed quality, processing efficiency, and market value. However, conventional assessment methods are slow, subjective, and poorly suited to large-scale characterisation. There is a need for standardised, non-destructive, high-throughput phenotyping methods to support breeding, seed quality assessment, and germplasm evaluation for this resilient yet underutilised crop. Methods : Videometer multispectral imaging (MSI) was used to generate reproducible seed morphometric descriptors for 106 BGN accessions. Twenty seed traits were quantified, including size (area, length, width, perimeter, and volume), shape indices (rectangularity, eccentricity, compactness, form factor, pointness, beta shape, diameter area, width of blob end, and compactness ellipse), and colour metrics (hue, saturation, lightness, and CIELab-A1). Data were analysed using analysis of variance, agglomerative hierarchical clustering, and principal component analysis to assess phenotypic variation. Results : Significant genotypic effects were detected across all measured traits (p < 0.05), indicating substantial phenotypic diversity among the 106 accessions. Seed size and shape traits exhibited wide variation (area ≈ 35–148 mm 2 ; length ≈ 7.3–15.5 mm; and width ≈ 6.5–12.7 mm). Multivariate analyses identified exploratory, dataset-specific phenotypic groupings that broadly reflected relative differences in seed size, shape, and colour among accessions, supporting germplasm characterisation and the identification of contrasting parental material for pre-breeding. Conclusions : MSI-based phenotyping provides a rapid, non-destructive, and high-throughput approach for quantifying seed morphometric variation in BGN. The generated seed trait data provide a standardised basis for germplasm characterisation, the identification of contrasting parental material, and early-stage screening, establishing MSI as a valuable phenotyping foundation for future BGN improvement.
Zea mays (maize) is a critical global crop, generating more than 1 billion metric tons of grain annually. Plant morphology is tightly associated with final yield and is determined by the process of development. Accurate assessment and recording of mature plant morphology is essential for linking genotype to phenotype and for assessing environmental influences on plant development and yield. In maize, imaging of fully emerged aerial organs provides an accessible and informative developmental end point for comparative phenotypic analyses. Here, we provide a protocol for capturing high-quality images of mature maize plants and detached leaf blades, suitable for publication and downstream quantitative trait extraction. We emphasize standardized imaging conditions, including high-contrast backgrounds, diffuse lighting, fixed camera positioning, and consistent scale calibration, to minimize technical variation and to facilitate automated image processing. Guidance is provided for both laboratory- and field-based imaging setups and uses widely available equipment. Images generated using this protocol can be analyzed using platforms such as FIJI to extract morphometric traits, including length, width, and outline-based shape descriptors. By prioritizing standardization at the point of acquisition, this protocol supports reproducible, scalable phenotyping of mature maize morphology for developmental studies in maize.
In plants, final organ shape and position influence function and whole plant productivity. In Zea mays (maize), leaf, ear, and tassel morphology are all key agronomic features influencing yield. These aerial structures originate from stem cell populations within meristems. Understanding how meristems are regulated and how stem cells differentiate over developmental time to form mature leaves, ears, and tassels is important for identifying novel routes for maize improvement. High-resolution imaging of meristem morphology is a key component of developmental studies. Scanning electron microscopy (SEM) is widely used to visualize meristem topology; however, conventional SEM preparation requires fixation, dehydration, drying, and conductive coating. These steps are time-consuming, technically demanding, and can introduce damage. Here, we present a rapid protocol for imaging freshly dissected meristems from maize using a benchtop scanning electron microscope. Fresh tissues are mounted directly onto conductive adhesive and imaged under high vacuum at low accelerating voltage, enabling high-quality micrographs to be captured within minutes of dissection. Although dehydration and tissue collapse can occur under vacuum, rapid sample handling and image acquisition minimize these effects and produce images comparable to those obtained from conventionally prepared specimens. This streamlined workflow substantially reduces preparation time, lowers material requirements, and facilitates medium-throughput morphological analysis. The method provides a practical and reproducible approach for rapid developmental phenotyping and is readily adaptable to other plant species and other tissues.
The final morphology of a plant is determined by much earlier events that occur during development. Meristems are critical for the development of the aerial parts of plants, such as leaves and stems. The morphology of these meristems, which can vary significantly across genetic backgrounds, correlates with phenotypic traits in Zea mays (maize) that influence yield, such as leaf initiation rate and kernel row number. Traditional methods for morphological analysis of meristems, including histological sectioning and fine dissection, often require considerable technical expertise. To address these challenges, we present a detailed protocol employing methyl-salicylate for tissue clearing, combined with Differential Interference Contrast (DIC) microscopy to visualize maize shoot apical meristems. This method enables the intact visualization of vegetative meristems without the need for extensive dissection, thereby preserving the natural morphology of the meristem. Using this approach, the meristem is coarsely dissected, fixed, optionally stained, and cleared to enable visualization through leaf layers. By improving the accessibility and quality of meristem imaging, this protocol facilitates accurate quantification of meristem morphology, critical for genetic and developmental studies. Optimized primarily for maize, the outlined techniques can also be adapted for other plant species, providing a versatile tool in the field of plant developmental biology.
Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.
Abstract Background Root architecture determines the capacity of crops for spatial exploration under stress conditions; however, existing studies on salt tolerance screening have mostly been confined to single traits such as root length or biomass, overlooking the overall spatial configuration of the root system and its intrinsic linkage with aboveground physiological functions. On this basis, the present study aimed to determine whether root convex hull area can characterize root–shoot synergistic adaptability under salt stress, and whether this synergy involves a physiological mechanism of resource conservation through cortical tissue remodeling. Methods Using a paper-based root phenotyping platform, we screened 28 spring wheat varieties originating from the arid regions of northwest China under 200 mM NaCl stress, and compared the differences between large-convex hull area and small-convex hull area varieties in root architecture, root cortical anatomy, stomatal traits, leaf water status, photosystem II efficiency, canopy temperature, and transpiration rate under salt stress. Results The results showed that large-convex hull area varieties maintained total root length, maximum depth, and convex hull area under salt stress, whereas small-convex hull area varieties exhibited significant reductions in all these parameters. Meanwhile, compared with small-convex hull area varieties, large-convex hull area varieties possessed greater cortical lacunar tissue area and cortex/stele ratio, as well as higher stomatal density, leaf relative water content, F v/ F m, and transpiration rate, but lower canopy temperature and smaller stomatal aperture. Convex hull area was positively correlated with leaf water status, photochemical efficiency, cortical lacunar area, and stomatal density, while negatively correlated with canopy temperature and stomatal aperture, indicating that root spatial maintenance, moderate cortical senescence, and stomatal regulation together constitute a functionally coordinated response module under salt stress. Conclusion In summary, convex hull area is not merely a descriptive indicator of root morphology, but rather a functional trait that reflects the synergistic integration of belowground exploration capacity and aboveground physiological resilience. This study proposes that convex hull area can serve as a candidate high-throughput phenotypic indicator for salt tolerance screening in wheat at the seedling stage; nevertheless, its predictive capacity for field yield performance still requires further validation under soil conditions, across the full growth cycle, and under interactions with multiple environmental factors.
Background and aims Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. Methods We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Results Dimorphic species exhibited higher discrimination capacity (average accuracy of 80-83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed considerable overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral covariation yielded R2 = 0.72 (P = 0.003) when sterile and fertile fronds were combined, although PGLS analyses detected no significant phylogenetic signal. The association differed between frond types and is interpreted here as exploratory evidence of morpho-spectral covariation. Conclusions Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Abstract Context: Accurate leaf area index (LAI) estimation is essential for UAV-based winter wheat growth monitoring, but optical saturation effects under dense canopies remain a major limitation. Aims: This study aims to develop a lightweight dual-stream network (HFI-Net) that improves LAI estimation under high-LAI conditions where saturation effects commonly occur in conventional vegetation indices for efficient UAV-based LAI mapping. Methods: A field dataset containing 637 paired UAV image patches and ground LAI measurements was collected from 21 winter wheat cultivars across seven phenological stages. HFI-Net was proposed, integrating RGB texture and vegetation-index (VI) features derived from multispectral imagery through attention-guided star-shaped multiplicative feature interactions, and five-fold cross-validation with data augmentation was applied. Key Results: HFI-Net achieved a coefficient of determination (\((R^2)\)) of 0.9023 and an RMSE of 0.4822 on the test set. Compared with ResNet50, the proposed model reduced parameters by 98.3% to only 0.40 M, while providing improved prediction performance under saturation-prone high-LAI conditions (\((>4.0)\)). Conclusion: HFI-Net enables reliable winter wheat growth monitoring throughout the growing season with reduced performance degradation under high-LAI conditions and low computational cost. Implications and Impacts: These results indicate the potential of HFI-Net for efficient UAV-based LAI mapping and precision agriculture applications.
Jie Zhou · Victor Hugo Moura de Souza · Lei Ju · Sebastian Eves‐van den Akker · Ji Zhou · Olaf Prosper Kranse
ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.
Reproduction assets foundThe paper's own phenotyping tool BioIMA (source code, documentation, example datasets, and user manual) is publicly available on the authors' GitHub repository, directly supporting the paper's image-based trait extraction and validation analyses.Code · publicis powered by embedded models
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preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
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https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Miho H, Barranco Navero D, Trujillo Navas I, Morello Parra P, Koubouris G, Trapero Ramirez C, López-Bernal Á, Oueslati A, Flores Mesas D, García López MT, Valverde Caballero P, Deger RE, Meca E, Perri E, Santilli E, Moral Moral J, Estudillo Cazorla C, Cabello Pozo D, Yousef Yousef M, Acar S, Gurbuz-Veral M, Priego Capote F, Zahiri A, Oulbi S, Márquez Pérez MI, M. Díez C.
Background Olive ( Olea europaea L.) breeding initiatives rely heavily on the extensive and correct characterisation of genetic resources to successfully achieve their goals, such as addressing climate change and emerging diseases challenges. However, the historical lack of standardised phenotyping protocols across multi-environment trials has severely hindered data interoperability and large-scale comparative analyses. Methods Within the Horizon 2020 GEN4OLIVE project, five international olive germplasm banks established a consensus-based methodological framework to systematically evaluate over 500 olive cultivars. We harmonised 14 evaluation protocols covering six fundamental dimensions: phenological and agronomic traits, abiotic stress resilience, biotic stress resilience, olive oil yield and chemical quality, table olive quality assessment, and morphological characterisation and photography. While most protocols were adapted from previously published literature to ensure ease of implementation across different facilities, novel methodologies for frost tolerance and standardised photography were developed de novo. Results The implementation of these consensus methods across five countries proved highly successful. This methodological framework enabled the generation of the largest harmonised, publicly available dataset on olive genetic resources to date, effectively making possible the correct comparation and ranking of the olive cultivars based on their specific characteristics. Conclusions This compendium of methods provides a robust, highly replicable reference point for the standardisation of olive germplasm characterisation and use of shared benchmark cultivars as an effective way for data normalization and comparation. It facilitates future global pre-breeding efforts, ensures international data interoperability, and supports the discovery of resilient cultivars to secure the future of the olive sector.
Reproduction assets foundThe article declares two paper-specific public assets: the GEN4OLIVE phenotypic dataset from evaluating over 500 olive accessions across five germplasm banks, hosted on the project's Olive Varieties Database, and a Zenodo-deposited methodological handbook (Extended Data) containing the 14 protocols, visual assessment, Dataset · publicData and software availability
The phenotypic dataset generated from the evaluation of over 500 olive varieties across the five Mediterranean
germplasm banks using this compendium of protocols and methodologies, is publicly available via the GEN4OLIVE
project repository.
• Repository: GEN4OLIVE Olive Varieties Database.
• Link: https://www.uco.es/ucolivo/gen4olive/olivevarieties (GEN4OLIVE Database, 2025).
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Open Research Europe 2026, 6:322 Last updated: 14 SEP 2026Open asset ↗GEN4OLIVE Olive Varieties Databasepdf-raw-page:8 lines:1-44Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Fruit quality is a critical determinant of economic returns in pear production, and maintaining an appropriate fruit load (FL) is essential for achieving high yield and quality. As a direct indicator of canopy photosynthetic capacity and assimilate supply, leaf number constitutes the key biological basis of reasonable FL determination under the leaf-to-fruit ratio concept. However, accurate and efficient estimation of leaf number in mature pear trees remains technically challenging, limiting its practical use in precision FL regulation. Here, we propose a data-driven framework for leaf number and reasonable FL estimation by integrating 3D point cloud-derived canopy structure with machine learning. A pipeline for extracting 3D architectural traits was developed and implemented in the software tool FTPCT, enabling rapid and standardized trait acquisition. Through correlation analysis, multicollinearity diagnosis, and variance inflation factor screening, five key traits strongly associated with leaf number were identified and incorporated into five machine learning models optimized using Bayesian optimization. Among them, the optimized random forest regression model achieved the highest and most stable performance, with R 2 of 0.85, RMSE of 239.74, and MAE of 149.26 for test dataset. SHAP analysis identified tree crown volume as the dominant contributor to leaf number estimation. Field validation demonstrated that FL regulation guided by the proposed framework significantly improved fruit weight and size without reducing yield compared with conventional practices. Notably, the proposed approach avoids explicit leaf-level reconstruction and relies on less canopy-scale traits, substantially reducing data requirements and computational cost, and thereby offering strong potential for rapid, field-deployable FL regulation in large-scale orchards.
Reproduction assets foundThe paper's phenotyping analysis assets are the authors' publicly released LeafNumPred source code and trained models, and the FTPCT software for 3D trait extraction from pear tree point clouds. Phenotype/point-cloud datasets are only available on request.Code · public. Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
mmc1.docx (1.6MB, docx)
Data availability
Data will be made available on request. Anyone who wants to obtain other public data can contact us at taost@njau.edu.cn. The source codes and models have been made publicly available at https://github.com/Zhang-Fanhang/LeafNumPred, and the FTPCT software has been released at https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package.
References
1.Tao S., Khanizadeh S., Zhang H., Zhang S. Anatomy, ultrastructure and lignin distribution of stone cells in two Pyrus species. Plant Sci. 2009;176:413–419. [Google Scholar]
2.Zhang F., Wang Q., Yuan K.Open asset ↗Zhang-Fanhang/LeafNumPredhtml-lines:284-315Code · publical variations [34,35]. The method for calculating these traits are shown in the Supplementary information 1.
2.5. Software implementation for 3D trait extraction (FTPCT)
To facilitate efficient and standardized extraction of canopy structural traits from point cloud data, we used a standalone software tool, FTPCT (available at: https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package), which integrates the trait extraction procedures applied in this study. The software provides a graphical user interface, enabling users to process tree-level point cloud data and extract key 3D structural traits without requiring advanced programming skills.
FTPCT implements a series of predefined proOpen asset ↗Zhang-Fanhang/FTPCThtml-lines:138-149Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
UAV-based multi-view reconstruction is an important approach for high-precision, non-destructive 3D crop phenotyping. However, in greenhouse environments, UAV image acquisition is often restricted to sparse viewpoints because of UAV-induced airflow disturbances and the structural complexity of the greenhouse, which severely hinders accurate 3D phenotyping. To address this challenge, this study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments, integrating a vision-triggered flight planning strategy with an improved sparse-view 3DGS pipeline, termed SparseBerry-3DGS, for multi-view image acquisition and 3D phenotyping of greenhouse strawberries in GNSS-denied environments. Specifically, 3D Gaussian Splatting (3DGS) is improved by incorporating flow-guided initialization, depth supervision, and an adaptive pruning strategy, which effectively alleviate geometric collapse and floating artifacts under sparse-view conditions. Furthermore, sequential semantic masks generated by SAM2 are utilized to guide the segmentation of strawberry point clouds, thereby reducing background interference and segmentation errors. Experimental results show that the vision-triggered flight strategy enables stable capture of 16 surrounding images for each target fruit. Under sparse-view conditions, SparseBerry-3DGS improves reconstruction stability, with the average peak signal-to-noise ratio (PSNR) reaching 18.25 dB, corresponding to an 18% improvement. The SAM2-based segmentation module achieves high accuracy, with the mean intersection over union (mIoU) above 0.95. Geometric evaluation based on strawberry longitudinal diameter yielded an of 0.88, supporting the accuracy of fruit-scale geometric reconstruction. For weight estimation, five-fold cross-validation yielded an of 0.90 and an RMSE of 3.62 g, showing better predictive performance than models based on 2D projected area and standard 3DGS point clouds. This study provides a new approach for high-throughput, non-invasive digital crop phenotyping in greenhouse horticulture.
Background Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture. Results We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships. Conclusions This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.
Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.
Abstract Tree crowns are complex, three‐dimensional structures whose morphology varies among species, individuals and environments. Although light detection and ranging (LiDAR) provides high‐resolution, single‐tree point clouds that advance species discrimination and the assessment of intraspecific variation in situ, crown shape is still commonly reduced to low‐dimensional metrics (e.g. crown diameter or crown base height), losing much of its three‐dimensional geometric complexity. We introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels. Pre‐segmented LiDAR single‐tree point clouds of eight temperate forest species were converted into three‐dimensional shape representations using radial bounding volumes (RBVs), which partitioned each crown into a standardized set of vertical layers and radial sectors. Surface points automatically digitized from each RBV formed geospatially aligned, 3D pseudolandmark configurations representing geometric morphometric crown shapes. These configurations served as the input data for multivariate analyses of crown shape variation within and between species. Twelve structural traits, including crown and stem dimensions, were extracted from the same RBVs and integrated into analyses of trait–shape associations. The morphospace of crown shape was structured along different axes of variation in broadleaf species than in conifers. Within these groups, species pairs—such as Fagus versus Quercus and Picea versus Pinus —exhibited contrasting intraspecific morphological gradients, with different structural traits driving shape variation in each. Crown base height and total crown height emerged as the strongest predictors of crown shape. Differences in crown shape among species were primarily captured by symmetric components, with asymmetry providing a negligible signal. Interspecific differentiation was largely driven by architectural variation rather than pure size differences. Morphological differences derived from pseudolandmarks and convolutional neural network features exhibited stronger correlations in conifers than in broadleaf species. We present a reproducible, LiDAR‐native framework for quantifying and comparing 3D crown morphology within and across species. Using the RBV approach, geospatially aligned pseudolandmarks can be derived from any pre‐segmented, single‐tree LiDAR point cloud, enabling scalable, multi‐regional analyses of intraspecific variability. This framework provides a robust foundation for integrating crown shape into ecological, evolutionary, silvicultural and modelling studies, including assessments of environmental effects and architectural constraints.
Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.
Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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hereOpen asset ↗Zenodo · 10.5281/zenodo.22167598pdf-raw-page:35 lines:1-52Plant phenotyping relevance matchOpenAlex · Europe PMC · bioRxiv · Crossref · checked 11 Sept 2026
Published30 Aug 2026bioRxiv (Cold Spring Harbor Laboratory)
Sebastián Varela · Jeremy Ruhter · Erik J. Sacks · Xuying Zheng · Dylan Allen · Anna Hale · Cory Landry · Xianyan Kuang · B. J. Long · Ernst Cebert · Yan Zhu · Shatabdi D. Proma · Sehijpreet Kaur · Diego Jarquin · Jesse Morrison · Andrew D. B. Leakey
Abstract The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery–based phenotyping, from data acquisition to plot-and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (G×E) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams—including remote sensing, environmental, and management data—toward data-driven decision making in agriculture.
Abstract Purpose Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results Soil-to-canopy relationships were weak to moderate but consistent within blocks ( r = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( R ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( R ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( r = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Plant phenotyping relevance matchOpenAlex · Europe PMC · Crossref · bioRxiv · checked 5 Sept 2026
Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.
Introduction Leaf shape is a genetically determined crop phenotype, and its accurate classification underpins soybean germplasm assessment and genetic improvement. Manual classification is highly subjective and struggles to distinguish morphologically similar leaves, while mainstream supervised classification demands large labeled datasets and incurs high development costs. Efficient feature frameworks for soybean leaf categorization are still insufficient. Methods In this study, 581 biologically replicated terminal leaflets sampled from 194 soybean varieties were analyzed at the single-leaflet level using traditional morphological indices and novel leaf contour angular features. Unsupervised K-means clustering was used to classify soybean leaflet morphological phenotypes; t-SNE was applied exclusively for dimensional reduction visualization, while Welch's ANOVA combined with Games-Howell post-hoc tests was adopted to detect inter-cluster phenotypic differences. Clustering stability and external consistency against manual visual labeling were further quantified via Adjusted Rand Index to comprehensively verify the reliability of grouping outputs. Results The results revealed no significant difference in leaflet edge complexity (p = 0.41) between two manually divided leaf groups distinguished by overall leaf outline similarity; these two morphologically similar leaf clusters failed to be fully separated even though the first two principal components accounted for 90.2% of total variance. For K-means clustering, k = 3 achieved better overall performance with a Calinski–Harabasz (CH) index of 395.55, Davies–Bouldin (DB) index of 1.03, and silhouette coefficient (SC) of 0.38, compared with k = 4. Nevertheless, the angular feature attained an F-value of 951.62 in driving sample reallocation across clusters, serving as the core indicator for fine subdivision at k = 4. Under k = 4 clustering, all six morphological indices differed significantly among the four groups (p < 0.05). Additionally, the number of cross-clustered samples increased from 66 to 119 as k rose from 3 to 4, with 96.6% of cross-clustering attributed to the leaflet contour angular feature. Discussion This research provides a novel reference and technical support for the automated identification and fine classification of soybean leaf morphology.
Main conclusion We established a protocol for reliable nuclear visualization in Charophyceae, revealed diverse nuclear organization across cell types and species, and identified suitable cells for genome size estimation via flow cytometry. Charophyceae are multicellular green algae closely related to land plants and are established model systems for understanding plant evolution. Yet key cellular parameters like genome size remain poorly characterized. We combined fluorescence microscopy, transmission electron microscopy (TEM), and flow cytometry to characterize nuclear diversity across cell types and species of Characeae and to identify a cell type suitable for genome size estimation. Among three DNA-intercalating fluorochromes, propidium iodide labeled nuclei most reliably. Nuclear morphology varied widely across cell types: mononucleated cells were found in vegetative apical cells, the coronula of oogonia, and spermatogenous filaments of antheridia, whereas multinucleation predominated in other tissues, e.g., cortical cells, spine cells, stipulodes and rhizoids. Nuclei in Chara hispida showed a significant gradient in cross-sectional area along the thallus axis. In the apical internodes, nuclei were larger and more heterogeneous, whereas in the basal internodes they were smaller and more uniform, which is consistent with possible endopolyploidy. TEM confirmed the nuclear identity of crescent-shaped structures. Relatively large nuclei were found in rhizoids and spine cells. Only Sphaerochara canadensis showed an organized nuclei pattern. Whole-thallus preparations did not yield a defined nuclear peak by flow cytometry, but antheridia of Chara tomentosa produced a sharp peak, from which a genome size of 5.32 pg (1C) was estimated. The protocol established here provides a simple, reproducible framework for visualizing nuclei and estimating genome size in Charophyceae, and helps address longstanding questions in this group, such as the mechanisms and functions of multinucleation, the site of meiosis, and genome evolution.
Sebastian Varela · Jeremy Ruhter · Erik Sacks · Xu-ying Zheng · Dylan Allen · Anna Hale · Cory Landry · Xian-yan Kuang · Blake Long · Ernst Cebert · Yan Zhu · Shatabdi D. Proma · Sehijpreet Kaur · Diego Jarquin · Jesse Morrison · Andrew D. B. Leakey
Abstract The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (G×E) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams-including remote sensing, environmental, and management data - toward data-driven decision making in agriculture.
High-resolution monitoring of forest structure and productivity is essential for effective natural resource management. However, monitoring approaches such as field-based forest inventories or extensive lidar campaigns are costly, time-intensive, and spatially limited. Therefore, inexpensive and accessible methods are needed. SatCHM (Satellite Canopy Height Model) was developed to be an accessible and open-source tool for researchers, allowing for site-specific and temporally flexible predictions of canopy height with limited computational resources. SatCHM requires four inputs: panchromatic satellite imagery, solar and sensor angle metadata of satellite imagery, digital elevation models (DEMs), and lidar-produced CHMs for an area of interest. After SatCHM pre-processes inputs, data is loaded into a collection of convolutional neural networks (CNNs) for image-to-image regression. This ensemble cooperates to yield high-resolution predictions (up to 0.5-meter) of three-dimensional tree structure with discernible tree crowns across a broader defined area of interest. After calculating the mean absolute error for each prediction output, the median of these mean absolute errors was 6.06 meters.
ABSTRACT Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.
Reproduction assets foundThe paper's authors publicly release their ShapeComplexity analysis code (Rust) on GitHub, used to compute the paper's leaf edge- and macro-shape complexity metrics. Supplementary data/analysis code are on Dryad, but that URL is not in the allowed list. RMBG is a generic third-party background-removal model, not a phenCode · publicThe complete, open-source Rust-code (The Rust Team, 2025 ) is publicly available on GitHub ( https://github.com/Thornbach/ShapeComplexity ), ensuring transparency and reproducibilityOpen asset ↗Thornbach/ShapeComplexitylines:86-94Plant phenotyping relevance matchOpenAlex · Europe PMC · checked 14 Sept 2026
Raj Nirmal Rajendran · M. Timothy Rabanus‐Wallace · Jinghan Lu · Geoffrey B. Fincher · Caterina Selva · Matthew R. Tucker · James Hunt · David Moody · Mohammad Pourkheirandish
Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.
Adam Ziółkowski · Franciszek Błaś · Luiza Tymińska-Czabańska
Field / plotLiDAR / point cloudRootMorphology / geometry measurementRoot system architecture
Coarse root systems govern tree anchorage, yet remain among the least documented components of tree architecture: excavation is irreversible, and established 3D methods rely on specialist scanners and lengthy post-processing. We evaluated whether a consumer smartphone records exposed coarse root architecture metrically, and which traits agree most closely with manual measurement. Four fully exposed Scots pine (Pinus sylvestris L.) root systems in northwestern Poland were scanned with an iPhone 17 Pro running Scaniverse, at about 30 min of acquisition and 5 h of processing per tree. Clouds were registered, cleaned and oriented to magnetic north in CloudCompare; of eight architectural metrics, four were validated against manual references at 95 cross-sections on 44 roots, and four were exploratory. Visible root length (root-mean-square error, RMSE, 22.2 cm, 8.4%), azimuth (RMSE 3.58°, mean absolute error 2.47°) and depth (RMSE 3.18 cm, 14.9%) agreed most closely with the reference; 70 of 77 first-order roots were detected with no false positives. Diameter was the weakest metric and the only one dependent on the operator (RMSE 0.46 and 0.29 cm for two operators on the same clouds). Smartphone LiDAR thus turns an irreversible excavation into a permanent, measurable record of the traits relevant to anchorage, provided that centimetre-level diameters are not required.
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Plant phenotyping relevance matchOpenAlex · checked 8 Sept 2026
To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems.
Herbarium specimens are physical, verifiable records that form the basis of taxonomic knowledge and biodiversity research. Their large-scale digitization has produced extensive collections of high-resolution images and associated specimen metadata, creating conditions in which artificial intelligence (AI) can play an important role in plant taxonomy, collection management, and ecological research. Early AI applications have primarily focused on automated species identification based on individual specimen images. Although increasingly accurate, such approaches remain limited by their emphasis on single-specimen label prediction and by treating identification outputs as final analytical decisions. Recent methodological advances-including segmentation-based preprocessing, automated trait extraction, structured extraction of label data, detection of potentially misidentified specimens, and multimodal integration of visual, textual, and genetic information-extend AI applications beyond species identification toward broader analytical frameworks, encompassing taxonomic interpretation as well as ecological and biodiversity research. In these approaches, specimens are placed within a shared analytical space, and identification results are used to support comparisons across multiple specimens rather than being treated as final decisions for single individuals. This multi-specimen perspective enables quantitative examination of species boundaries, morphological variation, data inconsistencies, and taxonomic stability within curated collections. In this context, AI serves not as an ultimate decision-maker but as a decision-support tool embedded in expert-guided workflows and biodiversity knowledge infrastructures. These developments can be summarized as Integrative Taxonomic AI, an approach that employs learned morphospaces to interpret and refine taxonomic categories by integrating multimodal evidence and curated specimen data under expert guidance.
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton.
Accurate estimation of the cotton seedling Leaf Area Index (LAI) is essential for yield prediction and precision crop management. Traditional manual methods are limited by low throughput, while two-dimensional remote sensing techniques often struggle with sparse canopy cover and soil background interference. Although three-dimensional LiDAR presents a promising alternative, existing deep learning approaches typically depend on costly fully supervised point-wise annotations. To address this challenge, this study proposes an end-to-end framework that integrates UAV-based LiDAR, weakly supervised segmentation, and physical parameter inversion. A weakly supervised network, termed CogNet, was developed—incorporating self-distillation and structure-aware label propagation—to achieve precise segmentation of cotton plants using only 10% sparse annotations. Following instance segmentation via Density-Based Spatial Clustering of Applications with Noise (DBSCAN), individual plant phenotypic traits were extracted. A nonlinear Extreme Gradient Boosting (XGBoost) model was then constructed to invert LAI by leveraging allometric relationships between 3D structural parameters and leaf area. Experimental results showed that CogNet achieved an Intersection over Union (IoU) of 85.34%, effectively mitigating overfitting to label noise and achieving performance competitive with the fully supervised RandLA-Net (82.13%). Notably, under the specific conditions of this cotton seedling dataset characterized by strong geometric priors, the weakly supervised model demonstrated enhanced robustness against annotation inconsistencies. The framework attained a plant detection rate of 96.2%, and the XGBoost model delivered high estimation accuracy (R² = 0.879, RMSE = 0.138). This study demonstrates that weakly supervised learning can substantially reduce annotation costs while maintaining model performance, providing an efficient and cost-effective solution for field-scale crop phenotyping.
Andrew Trlica · Rachel L. Cook · Matthew J. Sumnall
Field / plotLiDAR / point cloudMultispectral / hyperspectralLeafMorphology / geometry measurementLeaf traits
Canopy Leaf Area Index (CLAI) is a stand attribute containing information on the real-time health and growth potential of managed pine plantations. Current remote sensing techniques for quantifying CLAI rely on simple linear models applied to satellite multispectral imagery, or on techniques based on light detection and ranging (LiDAR) data that are costly and less frequently collected. This study demonstrates a convolutional neural network (CNN) approach to retrieving CLAI from 10 m Sentinel-2 multispectral imagery with a model trained on gridded LiDAR-based CLAI estimates. We demonstrate large gains in accuracy with the CNN compared to traditional linear models based on vegetation indices (e.g., Simple Ratio), but also clear shortfalls in model skill when predicting “blind” in some spatial domains that were completely excluded during model training. Pixel-scale root mean squared error ranged from 0.34 to 0.64 by domain when exposed to CLAI training data from all available spatial domains, but rose to 0.58–1.74 when predicting without prior domain-specific training. Prediction accuracy was consistently lower when applied to completely unobserved USGS LiDAR-based CLAI estimates. Traditional linear models, in contrast, had the advantage of usually lower prediction error across unobserved spatial domains (0.43–1.98), but with lower maximum accuracy. These results demonstrate a potential route for deploying more complex models for LiDAR “mimicry”, e.g., between data acquisitions widely separated in time, but advocate for the development and use of more stable generalized approaches for use in unobserved managed pine stands.
Cell polarity and tip growth rely on the dynamic spatial organization of signaling and structural components. Quantitative characterization of these spatiotemporal dynamics is critical for understanding polarized cell growth, yet manual quantification is labor-intensive and existing computational tools often lack the flexibility and robustness needed to analyze molecular and structural dynamics in tip-growing cells. Tip Quantification (TipQuant) identifies the cell apex by detecting the site of maximum expansion and automatically quantifies fluorescence distribution along the plasma membrane and within the apical cytoplasm from live-cell imaging data, enabling analysis of the spatiotemporal dynamics of molecular and structural components in tip-growing cells. TipQuant accurately identified cell apices and quantified the spatiotemporal behavior of fluorescently labeled proteins and cellular structures in Arabidopsis thaliana pollen tubes and Fusarium graminearum hyphae, reproducing manual measurements while reducing user bias and improving efficiency, consistency, and analytical flexibility. The tool also revealed a strong positive correlation between rho-like GTPase from plants activity and apical Ca 2+ influx in Arabidopsis pollen tubes, demonstrating its utility for analyzing dynamic cellular processes. TipQuant is a robust analytical tool for quantifying spatiotemporal dynamics in tip-growing cells, providing a flexible alternative to manual image analysis and enabling studies of the molecular mechanisms underlying polarized growth.
Plant secondary metabolites are mainly synthesized and stored in secretory tissues. Secretory canal development has been mainly characterized in Apiaceae. The secretory canals of Peucedanum praeruptorum contain pharmacologically active coumarins, but their organ-specific distribution and developmental dynamics remain poorly understood. This study integrated light microscopy (LM), transmission electron microscopy (TEM), X-ray microcomputed tomography (µ-CT), and high-performance liquid chromatography (HPLC) to investigate canal development, distribution, ultrastructure, 3D architecture, and coumarin accumulation in P. praeruptorum roots. Histological analysis showed that canals adjacent to the periderm originate from pericycle cells, whereas those in secondary phloem arise from parenchyma differentiation; both develop schizogenously. Canal quantity and dimensions varied temporally. Canals located in phloem showed density increasing toward the cambial zone, where cross-sectional areas were smaller. The canal density index increased from September to November, peaking on November 15, then declined. HPLC revealed dynamic accumulation of five major coumarins: content increased from September, peaked on November 15, then gradually decreased. TEM showed that epithelial cells surrounding the canal lumen were rich in Golgi, ER, mitochondria, plastids, starch grains, and osmiophilic droplets. µ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm). These dimensional characteristics aligned with developmental progression. This study characterizes the ontogeny, distribution, ultrastructure, and 3D architecture of secretory canals, providing a structural foundation for investigating correlations between secretory tissues and compound synthesis.
Abstract Understanding the allometric relationships between leaf area and other plant traits is essential for non-destructive growth monitoring and efficient crop management. However, no comprehensive study has yet modeled leaf area in quinoa ( Chenopodium quinoa Willd.) using simple morphological traits across different sowing dates. This study aimed to quantify allometric relationships between leaf area and plant height, leaf dry weight, stem dry weight, panicle dry weight, and total dry matter, and to evaluate whether these relationships are modified by sowing date. A two-year field experiment was conducted with 12 sowing dates under a randomized complete block design with three replications. Leaf area index (LAI) dynamics were described using a logistic model, and allometric relationships were fitted using power-law equations. The results showed that LAI followed a logistic trend across all sowing dates, with maximum values ranging from 2.7 to 7.9. Plant height provided the most reliable prediction of leaf area (R² = 0.83, b = 1.2), followed by leaf dry weight (R² = 0.72, b = 0.97). The allometric coefficients for stem dry weight (b = 1.54, R² = 0.74) and panicle dry weight (b = 1.95, R² = 0.71) showed greater variability. A striking finding was the exceptionally high allometric coefficient (b = 4.95) recorded on May 6 of the second year, indicating a pronounced shift in resource allocation toward leaf area expansion. Total dry matter was a weak predictor (R² = 0.54), likely due to leaf fall during the growing season. The hypothesis that sowing date modifies allometric relationships was confirmed, as evidenced by considerable variation in allometric coefficients across sowing dates. This study provides, for the first time, a comprehensive set of allometric models for quinoa across multiple sowing dates. Plant height and leaf dry weight are recommended as simple, rapid, and non-destructive indicators for leaf area estimation, facilitating improved crop monitoring and management under diverse environmental conditions.
Soil salinity is a major constraint on rice ( Oryza sativa L.) production, particularly during the yield-determining reproductive stage. Utilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years. By extracting 60 dynamic image-based traits (i-traits) reflecting canopy architecture and stay-green capacity, and four seed-setting rate-related traits, our genome-wide association study (GWAS) identified 95 significant loci, 35.8% of which precisely co-localized with previously reported QTLs. We further prioritized OsSLT1 ( LOC_Os01g05790 ) as a candidate gene at a reproducible suggestive locus associated with leaf-rolling-related image traits. Transgenic evaluations confirmed that it acts as a positive regulator of salt tolerance at the seedling stage. OsSLT1 was mainly detected in the nucleus, and no significant changes in Na + or K + accumulation were observed in flag leaves under the tested salt-stress condition, suggesting that OsSLT1 may regulate salt tolerance through mechanisms beyond classical shoot ion accumulation. Natural variations in the OsSLT1 promoter were associated with transcriptional divergence. The Hap2 promoter haplotype showed significantly higher stress-induced transcriptional activity. Hap2 was rare in modern indica accessions, suggesting that it may represent a potentially useful genetic resource for future salt-tolerance improvement. Supplementary information The online version contains supplementary material available at 10.1007/s11032-026-01704-2.
Antonio Samudio Oggero · Daisy Leticia Ramírez Monzón · Héctor D. Nakayama · Luis Felipe Medeiro Alves · Valter Arthur · Oscar Vega Alvarenga · G. Resquín-Romero · Wilson Romero Vergara · Juan D. Avalos Añazco
Calibrating the mutagenic dose is the first practical step of any radiation mutation-breeding programme, and it is usually summarised by the median lethal dose (LD50) or the median growth-reduction dose (GR50). We asked whether an accessible, image-based phenotyping pipeline can quantify the early radiation response of cowpea (Vigna unguiculata L. Walp.) seedlings finely enough to estimate GR50 and to rank organ- and pigment-level sensitivities. Seeds of the traditional Paraguayan landrace kumandá pyta’i were exposed to Cobalt-60 gamma rays at 0, 100, 200, 300, 400, 500, 600, and 700 Gy, grown in a greenhouse, and photographed at the early seedling stage. A single calibrated photograph (5.1 px mm−1) of 83 seedlings was segmented in Fiji/ImageJ 1.54p and analysed with Python to extract morphometric traits (total, root, and shoot length, root:shoot ratio, tortuosity, and a two-dimensional biomass proxy) and colorimetric traits (CIE L*a*b*, a normalised greenness index, and colour-class pixel fractions). Because the data departed from normality, dose effects were tested with Kruskal–Wallis, Spearman rank correlation, and Dunn post hoc tests, and GR50 was estimated by regression of each trait expressed as a percentage of the control. Total length, shoot length, and the biomass proxy declined significantly with dose (Spearman ρ = −0.40, −0.51, and −0.47; all p < 0.001), preceded by a low-dose stimulation at 100 Gy. Estimated GR50 values were ≈390 Gy for shoot length, ≈510 Gy for total length, and ≈550 Gy for the biomass proxy, within the range reported for other cowpea genotypes. Shoot elongation was more radiosensitive than root elongation, so the root:shoot ratio did not decline; tortuosity showed no dose response. Among pigment traits, the loss of greenness was the most robust signal (a* increased, ρ = +0.62, p = 5 × 10−10; green pixel fraction fell from 0.32 to near zero by 500 Gy). These results show that single-photograph phenotyping resolves a coherent, statistically supported dose response and yields a GR50 estimate usable for dose calibration. For kumandá pyta’i, doses of roughly 300–400 Gy (below GR50) are the most defensible starting window for mutation induction. The framework is reproducible and low-cost, but it is based on one greenhouse experiment and a single genotype, and should be validated across independent trials and cultivars.
Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.
Etukuri SP, Courtney CL, Rhoden C, Laufer M, Main R, Smith RA, Kenny D, Patel K, Johnson J, Thomas HB, Kothari N, Shumate B, Martin VB, Rife TW, Saski CA.
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Plant phenotyping relevance matchCrossref · checked 5 Sept 2026
The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production.
This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.
Accurate tree volume estimation is central to forest management and carbon accounting. Allometric equations are widely used but limited in transferability across species, regions, and environmental conditions. Mobile Laser Scanning (MLS) offers a promising alternative through direct measurement of tree geometry; however, the influence of tree shape on MLS accuracy remains poorly understood. This study evaluated MLS-derived estimates of stem diameters, total tree height, and merchantable stem volume against destructive reference measurements from 176 trees spanning eight species (four hardwood, four softwood) in Wallonia, Belgium. A Zeb Horizon RT scanner was used; tree architectural descriptors extracted from the point cloud were tested for associations with measurement error. Across 7,824 stem diameter measurements, MLS achieved a mean error of 0.46 cm, with precision declining above 15 m. MLS-derived height outperformed Vertex IV clinometer measurements for hardwood species (RMSE% = 6.88 vs. 8.78) but performed slightly less well for softwoods (RMSE% = 7.36 vs. 6.14). QSM-based volume estimates systematically underestimated reference values, while taper-based reconstruction produced nearly unbiased estimates with an RMSE of 15.72%. Correlation analyses and PCA showed that tree architectural variables explained only a small fraction of MLS error variability. Diameter and height errors were largely independent of structural attributes, while volume errors showed moderate associations with tree size and crown density. These findings indicate that tree architecture is not a primary source of MLS measurement uncertainty. Future MLS-based forest inventory efforts should prioritize acquisition and processing optimization, as scanning conditions and forest structure appear more influential than tree shape.
Automated plant identification based on leaf morphology has gained significant attention in recent years due to its wide range of applications in precision agriculture, biodiversity conservation, environmental monitoring, and botanical informatics. Advances in digital image processing and machine learning have enabled the development of intelligent systems capable of identifying plant species from leaf characteristics with minimal human intervention. Despite these advancements, achieving reliable and accurate classification remains challenging because leaf images are often affected by variations in illumination, complex backgrounds, image noise, differences in orientation and scale, as well as natural leaf deformation. These factors can obscure important morphological features, reduce the effectiveness of feature extraction, and ultimately decrease the accuracy and robustness of automated plant classification systems. Consequently, there is a growing need for intelligent frameworks that can effectively handle these challenges while preserving critical leaf morphology and venation information for reliable plant identification. This study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification. The proposed framework integrates multiple digital image processing and machine learning techniques to enable accurate and automated leaf venation analysis and plant classification. Initially, leaf images undergo preprocessing using grayscale conversion, histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian filtering, Laplacian sharpening, Gabor filtering, and homomorphic filtering to improve image quality and enhance venation and structural details. The enhanced images are then processed through threshold-based segmentation followed by morphological operations, including erosion, dilation, opening, closing, convex hull generation, and skeletonization, to accurately isolate leaf regions while preserving their geometric structure.To characterize leaf morphology, the framework extracts a comprehensive set of features, including geometric descriptors such as area, perimeter, circularity, aspect ratio, solidity, eccentricity, and vein density, together with Hu invariant moments that provide rotation-, translation-, and scale-invariant shape representation. In addition, the framework investigates the influence of image compression by comparing lossless PNG and lossy JPEG formats to evaluate their impact on preserving morphological features and venation details. The extracted feature vectors are subsequently classified using a Random Forest classifier to categorize leaf venation patterns into parallel, reticulate-pinnate, and reticulate-palmate classes.Experimental evaluation demonstrates that the proposed framework achieves an overall classification accuracy of 93.2%, while effectively preserving important morphological characteristics and maintaining computational efficiency. The combination of adaptive image enhancement, morphology-preserving segmentation, comprehensive feature extraction, and robust machine learning classification makes the proposed approach reliable, interpretable, and scalable. Consequently, the framework has significant potential for applications in digital herbarium systems, automated plant identification, biodiversity monitoring, botanical informatics, and precision agriculture.
Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches.
Gu Y, Sanow S, Taylor T, Morimoto KW, Nemer A, Hadley DJ, Zafar SA, DeMello L, Chen Y, Knab H, Busch Castro A, Kumaravelu V, Bailey-Serres J, Carney R, Brady SM.
Root anatomical barriers, including the suberized and lignified walls of the endodermis and exodermis, and cortical aerenchyma, regulate water and nutrient transport, gas exchange, and rhizosphere interaction. Their adaptive function places them as an important target for breeding environmentally resilient plant species. Quantifying these structures at high resolution is a manual bottleneck that limits experimental scale. We present RADIX (Root Anatomy Deep- learning Image segmentation across species and platforms), a framework that adapts a large self-supervised vision-transformer foundation encoder (DINOv3), pre-trained on billions of natural images, to root anatomy by fine-tuning its encoder with a dense-prediction-transformer decoder. Transferring these general-purpose vision encoders to a specialized biological domain with a high-quality annotated dataset is what allows RADIX to generalize across species and imaging platforms. We train and evaluate it on the first expert-annotated benchmark of root anatomical structures at scale, comprising 1,695 high-quality fluorescence images spanning 17 monocot and dicot species, six anatomical structures, and three imaging platforms. RADIX segments all six structures at inter-annotator-level accuracy and generalizes to unseen species, genotypes, growth conditions, and an imaging platform from an independent laboratory. A single unified model surpasses monocot- and dicot-specialist models without sacrificing in-group accuracy. Predicted masks yield aerenchyma and suberin/lignin measurements matching expert annotation at ∼1.2 s per image with a single GPU, reducing weeks of manual analysis to minutes. Applying RADIX across genotypes, microbial treatments, and growth systems, we show that these cell type features form a coordinated, multidimensional, and context-dependent system shaped by genetic and environmental factors.
Abstract Depth cameras measure the distance to a fruit's surface, whereas converting its silhouette into physical dimensions requires the depth of its center; corrections for this offset have so far been empirical, and therefore bound to the crop, sensor, and dataset they were fitted on. This paper derives the correction analytically. For a spherical fruit, integrating the surface-depth distribution over the visible hemisphere yields a closed-form sphere correction whose coefficient follows from sampling geometry, together with a theoretical justification of the median mask depth as the representative statistic. Combined with deep instance segmentation on RGB-D imagery of hydroponic melons, the empirically optimal coefficient coincided with the derived value, and the pipeline reached R 2 of 0.966 for fruit length (MAE 1.43 mm), 0.959 for width (1.84 mm), and 0.861 for end-to-end fresh weight (MAPE 4.6%). The analytical form made the measurement transferable. Applied unchanged to cylindrical mini-cucumbers, the pipeline held mm-level accuracy (width MAE 0.52 mm; fresh weight R 2 0.955 after coe cient refitting), with the correction's negligibility predicted in advance by an R / Z corollary; across active-stereo and time-of-flight cameras, the optimal coefficients proved non-interchangeable, identifying the coefficient as a physical parameter that absorbs geometry, sensor physics, and fruit shape. A field system that fuses and cross-verifies the two sensors, with an error-propagation confidence gate and parameterized grading logic, reproduced 2-3% fresh-weight error and 90.9% confirmed-judgment grading accuracy over four validation sessions in a commercial greenhouse unseen during development. Throughout, geometric components transferred unchanged while learned and regression components required recalibration - a boundary the model predicts and the system itself monitors.
Field / plotMultimodalNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleStereoFruitWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection
Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.
Starch, a key biological macromolecule accounting for 50-80% of dry weight in sweetpotato (Ipomoea batatas [L.] Lam.) storage roots, underpins food and industrial applications. However, sweetpotato starch characterization is limited by local-sectioning approaches that fail to capture the whole-root granule dynamics. Here, we established a new morphological observation system covering three key root regions based on two representative cultivars: Okinawa 100 (V100), and Yanshu25 (Y25). It was effective and convenient for in situ starch observation and analysis in sweetpotato roots. The whole-root in situ microscopy, starch physicochemical profiling, and transcriptomic correlation were integrated to resolve starch dynamics in Y25 and V100. We identified widespread simple starch granules (SSGs)-compound starch granule (CSG) coexistence across the whole root tissues, with Y25 exhibiting programmed CSG fragmentation driven by ARCs/FtsZ-mediated amyloplast envelope destabilization and concomitant AMY/BMY upregulation. Y25 had a higher amylose content and a higher proportion of medium/long chains, but the average degree of polymerization was slightly lower. Transcriptomic analyses revealed that the differentially expressed genes were annotated in pathways of carbohydrate metabolism, and the differentially expressed genes in the starch metabolism pathway were analyzed. Weighted gene co-expression network analysis further identified the hub genes from different modules and analyzed the co-expression networks. This work will not only advance the understanding of starch granule assembly and remodeling in sweetpotato, but also provide a robust methodological and transcriptome-guided framework for starch-focused germplasm screening and quality improvement.
LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture.
Background The quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. Results We demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10⁶ spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense , a model for Pseudocercospora fijiensis , and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. Conclusions MIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.
Monitoring continuous agricultural canopies is fundamentally limited by the geometric constraints and computational bottlenecks of traditional 3D reconstruction. This study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture. To drive component-specific optimization, we evaluated custom-trained convolutional networks (YOLO11) against a zero-shot foundation model (SAM3), determining that SAM3 provided the necessary boundary precision for accurate spatial isolation. The optimized 3DGS model outperformed implicit NeRF baselines, preserving fine-scale morphological details at real-time rendering speeds ( ≈ 48 FPS). To enable actionable measurement, a uniform voxelization protocol was applied to the point cloud, successfully neutralizing algorithmic densification bias. This technical framework yielded highly accurate physical geometry, achieving a Root Mean Square Error (RMSE) of ≤ 0.59 cm against in situ leaf measurements. Transitioning to agronomic interpretation, the pipeline was deployed to quantify complex canopy architecture. It mathematically mapped structural congestion zones and provided a temporal validation of a standard pruning intervention, explicitly capturing the geometric increase in lower-canopy porosity and the upward translation of biomass. This framework provides a robust, scale-accurate tool for monitoring plant architecture and guiding dynamic canopy management.
Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R2 = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R2 = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R2 = 0.473 and RMSE = 7.56 for SPAD and R2 = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages.
ArabidopsisMicroscopyCell / cellular structureRootMorphology / geometry measurementVisualization / data management
Super-resolution microscopy has transformed our ability to visualize subcellular structures, but its application in plant biology remains challenging due to the optical complexity of plant tissues. Here, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity. This method leverages time-correlated single-photon counting (TCSPC) to separate fluorescence signals based on their lifetimes, enhancing signal specificity and enabling the visualization of elusive subcellular compartments in Arabidopsis thaliana root tips. The protocol covers sample preparation, fluorophore selection, microscope configuration, image acquisition, and data analysis, providing a step-by-step guide to optimize tau-STED imaging for plant cell biology. By addressing the unique challenges of plant tissue imaging, such as autofluorescence, refractive index mismatches, and light scattering, this approach facilitates super-resolution imaging of intracellular structures, including the plant endoplasmic reticulum-Golgi intermediate compartment (ERGIC). This protocol is designed to be accessible to researchers with basic microscopy experience and offers a robust framework for exploring subcellular dynamics in plants with unprecedented detail. Key features • tau-STED integrates STED signals with fluorescence lifetime via phasor analysis at confocal speeds, enabling low-noise super-resolution imaging. • Morphometry analysis workflow at super resolution.
Norka M. Fortuny-Fernández · Emma A. Juárez-Acosta · Pablo Martínez · David García‐Callejas · Anne Damon · Natalia Y. Labrín-Sotomayor · Yuri J. Peña–Ramírez
Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods.
The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.
TomatoLiDAR / point cloudLeafMorphology / geometry measurementSegmentationLeaf traits
Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best F 1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09 cm, 1.78 cm and 8.98 cm 2 respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.
Reproduction assets foundThe paper's tomato point cloud dataset (with semantic and leaf instance annotations used for the phenotyping pipeline) is publicly available on Kaggle via a footnote. NPEC website is a facility page, and Open3D is a generic library, so neither qualifies.Dataset · public2. ^ The dataset used in this study is available at: https://www.kaggle.com/datasets/xinbolai/vtc-tomatoOpen asset ↗Kaggle · xinbolai/vtc-tomatolines:545-624Plant phenotyping relevance matchOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.
Introduction This study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations. Conventional morphometric analyses of olive endocarps largely rely on manual measurements, limiting reproducibility and quantitative comparison. Methods Ten archaeological endocarps were selected from previously published archaeological assemblages based on the integrity of their outlines, apex-base morphology and overall preservation quality. Quantitative descriptors describing endocarp size, symmetry, curvature and contour geometry were extracted using the OliveID software and compared with a modern morphometric reference database comprising Greek and international olive cultivars. Results Reliable contour extraction and quantitative descriptor computation were successfully achieved for all archaeological specimens despite carbonization. Preliminary comparison of representative morphometric descriptors showed that the archaeological specimens were positioned within the morphometric variation observed among the modern reference collection. Hierarchical clustering consistently associated the archaeological endocarps with the modern Throumbolia morphotype, while distinguishing them from elongated, globular and mucro-bearing cultivars. Discussion These findings demonstrate the feasibility of applying digital image-based morphometric analysis to sufficiently preserved archaeological carbonized olive endocarps and indicate a similar morphometric affinity between the analyzed archaeological material and the modern Throumbolia cultivar. This proof-of-concept study highlights the potential of digital morphometric approaches for quantitative archaeobotanical investigations of archaeological olive remains, while emphasizing the need for larger archaeological datasets and standardized image acquisition to further validate the observed morphometric similarity.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe quantitative measurements of the archaeological specimens are presented in Supplementary Table 2 , whereas the corresponding mean values and standard errors for the modern cultivars are provided in Supplementary Table 3 .Open asset ↗lines:311-320Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable traits are needed for identification of tea ( Camellia sinensis ) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD-RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15151283/s1 , Table S1: Original data of leaf morphological traits, RGB values, and SPAD values from six tea cultivars in this study.Open asset ↗lines:368-409Plant phenotyping relevance matchOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods, this study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging. As part of our contributions, a synthetic–real coupled multimodal dataset was constructed by integrating Blender virtual agricultural scenes (Blender VAS) with real field images. Building upon the existing Depth Anything V2 foundation model, we developed a novel module called Transfer-based Agricultural Metric Depth Anything V2 (TAM-Depth V2) for absolute metric depth estimation through parameter-efficient fine-tuning, depth decoder reconstruction, and joint loss optimization. Furthermore, we designed a novel multi-source prompt-based segmentation framework, MSP-SAM2, to generate positive and negative prompts for zero-shot crop instance segmentation. Finally, a new inverse physical plant height estimation algorithm, RANSAC-Per, was introduced to estimate plant height by combining truncated percentile statistics with local RANSAC micro-plane fitting, thereby reducing the effects of depth noise and field microtopographic variation. The result showed that TAM-Depth V2 achieved stable absolute depth estimation, with an RMSE of 0.1162 m and an AbsRel of 4.25%. Compared to the original box-prompted SAM 2, MSP-SAM2 achieved a 4.4% improvement in mIoU, reaching 91.6% and a recall of 93.2%. On a 350-plant multi-crop test set, Depth4PH achieved R 2 = 0.948, RMSE = 12.23 cm, and MAE = 8.82 cm, and MAPE =10.15%, with crop-specific RMSEs ranging from 3.99 cm (cucumber) to 20.73 cm (maize), significantly outperforming the traditional Global-MinMax baseline (which had an RMSE of 22.62 cm). These results indicate that Depth4PH provides a promising foundational pathway for high-throughput crop phenotyping. With future optimization for edge deployment, it holds significant potential to support high-throughput monitoring in precision agriculture.
Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.
As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .
Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.
To address the challenges of insufficient sensor stability and poor consistency among multi-source data during crop phenotyping in unstructured field environments, this study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform. A multi-sensor stabilization device integrated with an ESO-LQR control strategy suppresses pitch disturbances during motion. A ROS-based hierarchical framework coordinates LiDAR, cameras, and inertial sensors, while spatial calibration and timestamp-based software synchronization ensure spatiotemporal consistency. A tightly coupled LiDAR-IMU SLAM algorithm enables centimeter-level 3D reconstruction of farmland. To mitigate terrain effects, a two-stage ground point extraction method integrating verticality and spatial distribution features improves canopy height estimation. Field experiments demonstrate stable platform operation at 1.5 m s⁻¹ while maintain high efficiency and data quality, with a coverage efficiency of 95.2%, retained-point ratio of 85.3%, and an MTF of 0.35 for image clarity. Phenotypic evaluation shows strong agreement between predicted wheat plant height and manual measurements, with R² values of 0.898 and 0.729 and RMSE values of 1.30 cm and 1.64 cm at the jointing and grain-filling stages, respectively. Moreover, at the jointing stage, both the 2D green area index (GAI) and the 3D point-cloud-based canopy coverage exhibit strong consistency with ImageJ-derived results (R² = 0.873 and 0.910). These findings demonstrate that the proposed approach enables stable and efficient acquisition of crop phenotypic information in complex field environments, providing a solid technical foundation for digital field monitoring, data-driven crop management, and intelligent agricultural systems.
Kai Zhou · Yuxin Jiao · Guangning Yu · Xin Wang · Weijun Zhang · Shaolong Zhu · Yuxiang Zhang · Wenyan Yang · Shujun Hu · Xinyi Wang · Yulin Yin · Ziqiu Wang · Xuecai Zhang · Zefeng Yang · Tao Liu · Chenwu Xu · Yang Xu
Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.
Lei Li · Jindong Liu · Guoliang Wan · Hongqing Wang · Mengjiao Yang · Shuaipeng Fei · Duoxia Wang · Yong Zhang · Xianchun Xia · Xin Ma · Yong He · Yonggui Xiao
The extent of canopy coverage (CC) prior to the booting stage is a useful indicator of environmental adaptation and may help anticipate key developmental events such as heading and flowering. We used UAV-based high-throughput phenotyping to monitor CC in a 262-line F 8 recombinant inbred line population grown under four irrigation-year environments across two seasons. To quantify CC dynamics, we fitted regression models using either days after sowing (DAS) or accumulated active temperature (AT). These models showed a high goodness-of-fit (overall coefficient of determination ( R 2 ) > 0.90 across environments; per-timepoint prediction R 2 = 0.60‒0.99 with root mean squared error (RMSE) = 0.00‒0.02) and were used to derive 31 CC-related traits. Principal component analysis (PCA) showed that the first two components explained 80.00% of total variance in CC traits, with PCA1 accounting for 48.88%‒62.51% of the variance for DAS-based traits and 50.26%‒54.93% for AT-based traits. PCA1 reflected early canopy vigor and rapid coverage increase, while PCA2 reflected canopy maintenance after jointing. Genotypes in the top 15% for PCA1 differed significantly in flowering time, heading time, and plant height from those in the bottom 15%. Cross-environment comparisons showed moderate to high consistency of CC traits (average correlation ( r ) = 0.41‒0.65 for DAS-standardized CC of 20‒160 DAS and 0.48‒0.67 for AT-standardized CC of 100‒1000 AT). Using CC features derived from both DAS and AT, we trained a random forest model to predict flowering time, highlighting the contribution of both temporal and thermal information to predictive accuracy. This model achieved an independent test set R 2 of 0.81 with an RMSE of 1.25 days within the current dataset. All 31 CC-derived traits were also used for QTL mapping. Inclusive composite interval mapping identified 156 QTL detection events across 15 chromosomes (0.80%‒27.70% phenotypic variance explained), which were consolidated into 26 QTL regions, including loci such as QCC.caas.7A (671.47‒680.09 Mb on 7A) and QCC.caas.5D2 (426.67‒459.42 Mb on 5D). Several of these regions co-localized with previously reported genes associated with tillering, flowering time, winter hardiness and plant height. These findings indicate that CC, characterized by DAS- and AT-based traits, is genetically tractable and predictive of flowering within the current dataset, supporting its potential as a phenology-related trait for wheat improvement. Further validation across broader environments, years, and genetic backgrounds will be needed before broader application.
Ripe tomato fruit display diverse 3D morphologies driven by genetics, environment, and management, yet these differences remain hard to quantify in the absence of precise point-cloud segmentation tools. This paper proposes the VMSNet to accurately segment tomato fruits and extract phenotypic traits based on the segmentation results, including horizontal and vertical diameters. Point clouds are obtained through depth cameras. After preprocessing and labeling the fruits, a dataset is established using global enhancement and local enhancement. On the base framework of PointNet++, the downsampling method was replaced, a multi-scale attention module (MS_A) was integrated, the combination scheduling strategy was optimized, and VMSNet was constructed. Following segmentation, the fruit growth direction is estimated by density-weighted method, and principal component analysis (PCA) is used to establish a rotation plane. By rotating according to the slicing angle, the fruit point cloud is completed and fitted into an ellipsoid. Random Sample Consensus (RANSAC) is used to smooth the outliers. The OBB is applied to extract the horizontal and vertical diameters, which are compared with measurement to verify the algorithm’s accuracy. The results indicate that the accuracy of VMSNet in segmenting ripe tomato fruits is 97.96%. The correlation coefficients R 2 between the calculated and measured values of the horizontal and vertical diameters reached 0.89 and 0.86, respectively. This proposed proposal provides robust point cloud segmentation and completion for phenotypic analysis for other same species greenhouse crop.
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Plant phenotyping relevance matchCrossref · OpenAlex · checked 14 Sept 2026
The efficacy of biological control agents is often inconsistent across pathogen isolate panels, yet conventional dual-culture screening often reduces antagonism to single endpoint measurements such as radial growth or colony area. Here, we developed a quantitative phenotyping framework to evaluate interactions between three Trichoderma antagonists and Colletotrichum isolates associated with coffee and cacao. Dual-culture assays were used to quantify antagonist and pathogen morphology after 96 h, and the combined morphology dataset was analyzed using machine learning to test whether host-associated isolate panels could be classified from colony-level interaction traits. To address potential information leakage and basal-growth confounding, we evaluated control-only pathogen morphology models and leave-one-pathogen-isolate-out validation. Under random 5-fold validation, Random Forest models achieved similar balanced accuracy using control-only pathogen morphology and full dual-culture interaction morphology, 0.840 and 0.864, respectively. Under the more conservative leave-one-pathogen-isolate-out validation, performance decreased but the full dual-culture model, balanced accuracy = 0.725, outperformed the control-only model, balanced accuracy = 0.578, indicating that basal pathogen morphology contributes to host-associated differences while interaction-level traits add information beyond basal growth alone. Hyperspectral imaging was then used as a proof-of-concept, non-invasive tool to characterize selected interaction interfaces. In the complete 11C-65-1 × P24-83/P24-192 subset, VNIR reflectance residuals showed interface-specific deviations from within-plate colony-side spectral mixing axes. These residual wavelength features are presented as candidate spectral correlates rather than validated biochemical mechanisms. Overall, morphology-based machine learning and hyperspectral interface phenotyping provide a scalable framework for controlled biocontrol screening, while emphasizing the importance of isolate-aware validation and cautious spectral interpretation.
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Plant phenotyping relevance matchCrossref · checked 5 Sept 2026
Chao Mou · Jiahua Fan · Ang Liu · Chang Liu · Kecheng Huang · Huiyu Ding · Jingchen Li · Haiyan Zhang
Field / plotLiDAR / point cloudRGB / grayscaleRootStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.
Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.
Purpose: This paper investigated automated physical quality assessment of harvested seeds. Design/Methodology/Approach: This study provides an extensive review of computer vision-based two-dimensional (2D) and three-dimensional (3D) deployments for the physical inspection of harvested seeds and grains. For this purpose, a total of 75 peer-reviewed articles published between 2022 and 2025 were identified from scientific databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. These articles were based on seed quality assessment, image processing, and artificial intelligence. The selected articles were systematically analysed according to different stages of the processing pipeline, including data acquisition, preprocessing, segmentation, feature extraction, and classification. Research Limitation: This review is restricted to physical quality assessment of harvested seeds, excluding chemical, biochemical, and nutritional parameters. It references 75 peer-reviewed articles published between 2022 and 2025. Findings: This study identified technical problems related to variations in seed samples, hardware setups, segmentation, feature selection, and classification. These problems significantly affect the performance of automated systems. Based on a critical examination of the present automated systems, this paper highlighted the scope for future research. Practical Implication: An advanced, future-ready system can address the need for integrated imaging methods and effective data processing. Social Implication: The adoption of automated seed inspection systems provides assurance of food security. Originality/ Value: This paper identified critical gaps such as the absence of a unified processing framework, the lack of cross-species generalisation, and the limited adoption of explainable AI.
Soybean pod-related traits are important for seed development, yield formation, and cultivar evaluation. However, conventional measurements are inefficient and have limited ability to characterize complex pod features such as curvature, local enlargement, and continuous color variation. In this study, mature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach. Eight pod-related traits related to size, morphology, and color were extracted from pod images. A genome-wide association study (GWAS) was performed using 61,541 high-quality SNP markers to dissect the genetic basis of these image-derived pod traits. A total of 16 stable loci associated with pod size, morphology, and color traits were identified across 11 chromosomes. Among these loci, eight were not reported in the previous image-based soybean pod GWAS study. Based on SoyBase gene annotation and Gene Ontology biological process information, 32 biologically relevant candidate gene records were prioritized within the corresponding candidate genomic intervals, while pod-related expression profiles and SoyBase association information were used as supporting evidence for candidate gene evaluation. These findings indicate that refined image-derived traits can provide complementary genetic information beyond conventional pod measurements and offer additional opportunities for dissecting soybean pod development, morphology, and mature pod color variation.
Introduction: Habitat restoration is necessary for the conservation and management of plant and animal species, especially in rare ecosystems. Drones may be well-suited to monitor changes in plant and animal communities in response to restoration efforts. The objective of the study was to examine whether drone imagery can detect differences in vegetation across multiple contexts. Materials and methods: Using a commercially available drone, I captured and processed aerial imagery with an open-source photogrammetric processing program. Point cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories. In addition, using automated radio tracking, I compared vegetation density between used and available locations for Eastern Whip-poor-wills during the day and at night. Results: In August 2024, a drone flight covering a 3.05 km2 area of pine barrens captured 3372 images. Vegetation density differed by cover type (p = 0.001) and was greater in recently disturbed sites (p = 0.002). Scrub oak and recently burned sites had ~30% and ~12% greater vegetation density than deciduous forests and plots > 2 years post-disturbance, respectively. Vegetation density was lower at Eastern Whip-poor-will used locations than at available locations (151.0 vs. 159.7 points/m2, p < 0.001). Conclusions: Analysis of fine-scale differences in vegetation structure was important in discriminating subtle differences in habitat selection for Eastern Whip-poor-wills. This study demonstrated that drones and relatively simple image processing can be practical tools for restoration when quantifying and monitoring vegetation differences in dynamic ecosystems.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's drone-derived vegetation density data and related measurements.Dataset · publicThe data supporting the findings of this publication has been made available within a publicly accessible
repository at https://doi.org/10.5281/zenodo.20398090.Open asset ↗Zenodo · 10.5281/zenodo.20398090pdf-page:11 lines:1-49Plant phenotyping relevance matchOpenAlex · checked 11 Sept 2026
Published30 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. The harvest mouse, Micromys minutus (Pallas, 1771) is the smallest rodent in Japan and now listed in the Red Data Books of Tokyo, 2 prefectural capitals, and 28 prefectures in Japan due to drastic decline of grasslands. For the harvest mouse, the height and density of the tall grass species where nesting occurs are considered particularly important. However, it has been difficult to continuously and extensively acquire information on the three-dimensional structure of herbaceous vegetation. With recent development of UAV technology, UAV data are beginning to be applied to the analysis of herbaceous vegetation. For acquiring three-dimensional information via UAV, methods include using LiDAR sensors or generating 3D point cloud data from aerial photographs using SfM. This study evaluates whether UAV LiDAR or UAV SfM is more suitable for estimating the height of tall grass species such as Japanese silver grass (Miscanthus sinensis), which serve as important nesting sites for the harvest mouse. As a result of analysis, the proposed method was found to be effective to estimate grass height regardless of whether UAV LiDAR or UAV SfM is used. However, when comparing the accuracy of canopy height estimation using UAV LiDAR data alone, UAV SfM data alone, and combined UAV LiDAR and SfM data, combined UAV LiDAR and SfM data found to perform best. Maximum canopy height was found to be best estimated using the combination of median of hand-measured five maximum canopy height values and maximum height calculated using the combined UAV LiDAR and SfM data.
Abstract. Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.
Accurate estimation of crop plant height using unmanned aerial vehicles (UAVs) is essential for field-scale crop monitoring and phenotyping. Most previous studies using UAV-based structure-from-motion (SfM) photogrammetry have relied on raster-based crop surface models (CSMs) and have evaluated their performance using accuracy metrics such as the coefficient of determination ( R 2 ) and root mean square error (RMSE). However, such evaluations provide limited insight into how estimation behavior varies across space and time, particularly during dynamic crop growth stages. To address this gap, this study conducted a time-series comparison of rice plant height estimates derived from UAV-SfM-generated dense point clouds (DPCs) and raster-based CSMs in farmer-managed paddy fields in Cambodia, which are characterized by heterogeneous micro-environmental conditions. Rice plant height was measured throughout the growing season and UAV-derived estimates were evaluated using regression analysis, analysis of covariance, and canopy cover dynamics. In the pooled analysis, both approaches achieved high overall accuracy, with R 2 = 0.92 and RMSE = 7.2 cm for the CSM-based approach and R 2 = 0.90 and RMSE = 8.8 cm for the DPC-based approach. However, time-series analyses revealed that CSM-derived plant height estimates exhibited strong location-dependent variability and sensitivity to early-stage canopy development, whereas DPC-based estimates showed more consistent performance across locations and growth stages. Regression coefficients derived from CSM-based estimates varied significantly among locations, whereas those from DPC-based estimates did not, suggesting that point-based representations may provide more spatially consistent estimation behavior under heterogeneous field conditions. By explicitly considering temporal dynamics, canopy development, and data representation, this study highlights the limitations of current raster-based UAV-SfM workflows for structurally complex crop canopies and suggests that DPC-based approaches may offer a useful complementary representation for crop monitoring and phenotyping, particularly when spatial consistency across heterogeneous field conditions is important.
The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.
Jianguo Li · Xiaoying Zhu · Zesheng Wei · Xiaoti Huang · Mingchong Yang · Dandan He · Jiahua Shi · Haoran Li · Huan Wang · Jiada Huang · Jun Shen · Lingqiang Wang
Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at −log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10–30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, “Rice_Stem_Pre_V1.1.exe,” for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.
Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels. Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm 3 , with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study. This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.
Pod shattering is a major domestication-related trait and one of the principal causes of pre-harvest yield losses in cowpea (Vigna unguiculata (L.) Walp.), particularly under hot and dry conditions. Rapid and reliable identification of shattering-resistant genotypes is essential for improving breeding efficiency. The present study evaluated a national core collection of 245 diverse cowpea genotypes over three consecutive years (2023–2025) to identify a simple phenotypic marker associated with pod-shattering resistance. Genotypes were screened using a modified Random Impact Method (RIM), and pod physical traits, including pod length, pod breadth, pod thickness, pod wall weight, seed-to-pod ratio, and dorsal suture morphology, were examined for their association with shattering response. A distinct and consistent morphological marker was identified in the dorsal suture of mature pods. Shattering-resistant genotypes exhibited a single, prominent dorsal ridge positioned above the dehiscence zone, whereas susceptible genotypes consistently displayed a two-ridged dorsal suture separated by a central depression that appeared to reduce tissue integrity and facilitate pod rupture under mechanical impact. The observed marker remained stable across years and environmental conditions, indicating its reliability as a rapid visual indicator of shattering resistance. The modified RIM provided a standardised, reproducible, and cost-effective approach for evaluating pod shattering while minimising environmental variation associated with field phenotyping. The identified dorsal ridge morphology offers a simple, non-destructive, and efficient phenotypic marker for large-scale germplasm screening and the selection of resistant genotypes. This marker can accelerate breeding for pod-shattering resistance, improve yield stability, and facilitate the development of climate-resilient cowpea cultivars adapted to drought-prone environments.
Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.
Joane S. Elleouet · Sadeepa Jayathunga · Natalie G Graham · Mark Paget · Jaroslav Klápště · Robin J.L. Hartley
Field / plotLiDAR / point cloudStem / branchMorphology / geometry measurementBiomass / plant weightPlant / canopy height
Abstract Context : Radiata pine breeding programmes rely on stem volume as a key objective, but phenotyping constraints limit selection intensity. UAV-LiDAR offers a scalable alternative to labour-intensive field measurements. Aims : We evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials and quantified their utility relative to field-measured diameter at breast height (DBH). Methods: LiDAR metrics describing tree height and size were assessed against allometric stem volume (ASV) across 11 genetic trials (~27,000 trees, two trial series) using single-step genomic best linear unbiased prediction (ssGBLUP) with ~9,500 SNPs. Results : The 3D surface area of the individual tree convex hull (convexhull3D_area) had the highest correlation with ASV (up to r = 0.87) and similar heritability to DBH (mean h 2 = 0.26). LiDAR tree height had the highest heritability (mean h 2 = 0.37) and moderate to high genetic correlation with DBH. Selecting the top 100 genotypes by convexhull3D_area recovered 67-86% of potential ASV genetic gain, versus 88-96% for DBH. Including malformed trees in the genetic analyses of LiDAR traits marginally reduced their performance as stem volume proxies. Conclusion UAV-LiDAR-derived tree height and 3D convex hull surface present desirable properties to complement field phenotyping for stem volume selection in radiata pine. Their scalability, repeatability and high heritability support lower phenotyping costs, better early selection and accelerated genetic gain in radiata pine breeding.
Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g L−1). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions.
Abstract Architectural analysis provides a powerful analytical framework for understanding the ontogenetic trajectories of tree species and their adaptive responses to environmental constraints. Despite their ecological, economic, and cultural importance in West African agroforestry systems, the architectural development of Khaya senegalensis (Desr.) A. Juss. (Meliaceae) and Pterocarpus erinaceus Poir. (Fabaceae), two overexploited taxa classified as Vulnerable on the IUCN Red List, had never been formally described. This study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities along a south-north bioclimatic gradient in Côte d'Ivoire, covering contrasting vegetation zones ranging from dense humid forest to dry Sudanian savanna. Both species display well-defined ontogenetic trajectories comprising four phases: juvenile establishment, architectural construction, reproductive transition, and crown restructuring associated with ageing. Distinct architectural models were identified: K. senegalensis conforms to Rauh's model, characterized by a monopodial orthotropic trunk with indefinite growth and rhythmic acrotonic branching; P. erinaceus follows Troll's model, in which the orthotropic trunk progressively gives rise to a sympodial plagiotropic system with mixed terminal and lateral flowering. Architectural units, defined as the minimal structural organization enabling a species to reach reproductive maturity, were established at the adult stage: that of K. senegalensis comprises four axis categories and five branching orders, while that of P. erinaceus comprises three axis categories and up to six branching orders in old trees. Significant variation in growth-unit morphology among habitats and localities ( P ) revealed the architectural plasticity of both species in response to ecological gradients. The calculated Favourable Development indices ( FDi ) identified Bouaké and Katiola as optimal zones for K. senegalensis , and Bouaké and Toumodi for P. erinaceus , providing objective spatial criteria for reforestation planning. These findings demonstrate that architectural traits are robust indicators of development, adaptive strategies, and crown functioning. By linking structural organization to productivity, resilience, and regeneration potential, this study provides a scientific basis for integrating architectural analysis into reforestation programmes, sustainable forest management, and the design of agroforestry systems for threatened African tree species facing growing climatic and anthropogenic pressures. Complementary regression analyses further showed that phytomer number, rather than internode elongation, primarily governs growth-unit length in both species, and that growth unit diameter scales positively with growth unit length; a multivariate analysis of variance (MANOVA) confirmed that ontogenetic stage and locality, but not habitat alone, robustly structure growth-unit morphology.
Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects. Methods We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions. Results Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana . Discussion Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.
Reproduction assets foundThe paper's authors publicly released the Garryanalyzer ImageJ plug-in source code on GitHub, all original and modified leaf images used in the morphometric analyses on Zenodo, and the full leaf morphometric measurement dataset plus R Lasso analysis code in a second Zenodo repository. All are paper-specific, public,可直接Code · publicThe source code and installation instructions for Garryanalyzer can be accessed on GitHub at https://github.com/zxie8561/Garryanalyzer.Open asset ↗https://github.com/zxie8561/Garryanalyzer · zxie8561/Garryanalyzerhtml-lines:210-274Dataset · publicAll images used in the morphometric analyses, both original and modified, are available on Zenodo (https://doi.org/10.5281/zenodo.17462266).Open asset ↗https://doi.org/10.5281/zenodo.17462266 · 10.5281/zenodo.17462266html-lines:210-274Dataset · publicThe full dataset of leaf morphometric measurements of both GBIF and Portland samples, R code for Lasso analysis, and other miscellaneous files are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.17546152).Open asset ↗https://doi.org/10.5281/zenodo.17546152 · 10.5281/zenodo.17546152html-lines:210-274Plant phenotyping relevance matchOpenAlex · Crossref · checked 6 Sept 2026
Perilla ( Perilla frutescens ) is an important oilseed crop in East Asia with high nutritional and economic value. Seed size and seed coat color are key agronomic traits influencing yield, oil quality, and market preference. However, genetic studies in perilla remain limited by small population sizes and low-throughput phenotyping, restricting their application in breeding. A large-scale genome-wide association study (GWAS) was conducted by integrating high-throughput image-based phenotyping in a panel of 493 perilla accessions. Measurements of seed morphology, including area, perimeter, and length, and color traits (RGB components) were obtained. Genotyping-by-sequencing generated high-quality single-nucleotide polymorphism (SNP) datasets, and GWAS was performed using four statistical models (general linear model, mixed linear model, FarmCPU, and BLINK). A total of 44 significant trait-SNP associations were identified, corresponding to 20 unique SNPs, as several SNPs (including those on chromosomes 6, 10, 15, and 17) were associated with multiple correlated traits. Linkage disequilibrium-based analysis showed candidate genes involved in phenylpropanoid metabolism and carbohydrate pathways. Predicted protein-altering variants, including non-synonymous and stop-gained mutations, were detected in key genes. Derived cleaved amplified polymorphic sequence markers developed near peak SNPs distinguished phenotypic differences between allelic groups, demonstrating their effectiveness for trait differentiation. This study represents the first large-scale GWAS integrating image-based phenotyping for seed traits in perilla and provides candidate dCAPS markers with potential applicability to marker-assisted selection, pending validation in independent breeding populations. These findings offer valuable genetic resources and a practical framework for molecular breeding and crop improvement in perilla.
1. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. 2. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3's prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object count. 3. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R² = 0.935, n = 74) under simple-background conditions and (R² = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica. Object counts matched manual counts almost exactly for well-separated insects in an insect box (R² = 1.000, n = 12). 4. By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and count from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Published23 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Accurate estimation of tree volume is essential for precision forestry and sustainable forest management. Traditional forest inventory methods rely on manual measurements of tree height and diameter, which are time-consuming and costly to conduct over large areas, and difficult to perform efficiently in dense forest stands. This study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets. While the study relies on harvester production data (Sweden) and field-measured tree stem profiles (Brazil), the framework is designed to support the estimation of tree volume from close-range remote sensing techniques, such as terrestrial photogrammetry using handheld cameras. Three modelling approaches were evaluated, including two machine learning models (XGBoost and Random Forest) using partial tree stem profile measurements as predictors, and one baseline model (XGBoost) using diameter at breast height and tree height as predictors. The models were developed using two independent datasets: harvester production data of Norway spruce (Picea abies (L.) H. Karst.) from Sweden and field-measured tree stem profiles of Slash pine (Pinus elliottii Engelm.) and Loblolly pine (Pinus taeda L.) plantations from Brazil. The results show that tree volume can be predicted with reasonable accuracy using partial tree stem profiles, although models incorporating tree height achieved the lowest prediction errors. The findings demonstrate that partial tree stem profiles provide valuable structural information for machine learning-based tree volume estimation. This framework supports the future integration of close-range remote sensing techniques into modern forest inventory systems.
Abstract Plants live in a physical world governed by a multitude of mechanical processes which vary over time. The unique features of plant cells, which are turgor-inflated objects surrounded by the cell wall, present an intricate perception and response system for mechanical forces. A powerful tool to investigate how plants adapt and react to these cues is Atomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties. In the context of cell wall biomechanics, there remains some controversy on appropriate AFM measurement practices and suitable use of common terminologies. Specifically, the interpretation of plant cell indentation curves and derivation of the wall elasticity modulus can be challenging and continues to spark debate. In this Expert View, we discuss recent advances of AFM in plant science as well as best practices for the use of AFM and considerations for data interpretation with a focus on mechanical probing by indentation.
Precision agriculture increasingly relies on detailed structural information, such as canopy height and canopy volume, to enhance crop health monitoring and operational safety. However, existing methods based on costly LiDAR or RGB-D sensors are often impractical for large-scale deployment in dynamic and unstructured horticultural environments. Furthermore, conventional 2D segmentation and SLAM-based pipelines typically generate sparse, geometrically inconsistent semantic maps which are insufficient for actionable structural analysis in agricultural applications. To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion. At inference, the proposed model takes a single RGB image as input and predicts voxel-wise geometry and semantics, from which task-oriented structural maps, including canopy height, canopy volume, and obstacle-aware traversability layers, are derived. Specifically, we first introduce a Depth-Aware Decoder Module that explicitly recovers depth in the spatial domain and fuses 2D-to-3D features, thereby mitigating depth ambiguity and reducing reliance on accurate pose. Second, an NCS-Guided Geometry Encoder is designed to inject normalized depth into voxel positional embeddings, enabling self-attention to perform global relational modeling within a depth-aware geometric coordinate system. In addition, a Global Encoder is utilized to refine local structural details, while an occupancy head produces the final 3D semantic completion outputs. We construct a horticultural 3D semantic scene dataset using an RGB-D sensor, which serves as a benchmark for evaluating our method, while the deployed model remains RGB-only. Extensive quantitative and qualitative experiments are conducted on both the Semantic-KITTI dataset and our dataset. On our dataset, the method achieves 82.31% occupancy IoU, 84.26% mIoU, and 86.25% precision. Beyond voxel-level evaluation, manual field measurements further show canopy height MAE values of 0.019-0.026 m and canopy volume proxy relative errors of 8.4%-11.4%. These results demonstrate the effectiveness of our approach in real-world agricultural scenarios, providing actionable structural insights for crop monitoring and autonomous robotic operations.
ABSTRACT Phenomic selection (PS) offers a cost‐effective , breeder‐friendly approach for public breeding programmes with limited access to genotyping or restricted financial resources for laboratory infrastructure. Since PS relies on high‐throughput phenotyping data, which is often derived from near‐infrared spectroscopy (NIRS) of harvested seeds, prediction is challenged by the high dimensionality and strong intercorrelation of NIRS data, which means that only a subset of wavelengths is informative. This study evaluates spectral variable selection models for predicting key morpho‐agronomic and quality traits in malt barley ( Hordeum vulgare L.) and assesses their performance under realistic breeding scenarios. Four NIRS‐based regularized regression models (Lasso, Enet, Ridge and a heritability‐filtered Ridge model) were tested to predict 10 morphological, agronomic and quality traits measured in two malt barley trials conducted during the 2022 and 2024 cropping seasons at three locations in Ethiopia using 100 genotypes in each trial. Model performance was evaluated across four practical breeding scenarios: within‐location unseen genotype prediction (WL‐uG), leave‐one‐location‐out prediction (LOLO), target environment unseen genotype prediction (TargetEnv) and across‐location wide adaptability (RuG). Accordingly, Cross‐validation identified stable, informative spectral predictors for each trait, scenario and trial. Among the models tested, Lasso and Enet consistently outperformed Ridge regression, with Enet showing the best predictive performance across scenarios. Prediction ability (r) ranged from 0.15 to 0.85 for quality traits, 0.16 to 0.79 for agronomic traits and 0.07 to 0.89 for morphological traits across scenarios. Thus, these findings underscore the importance of spectral predictor selection in improving predictive ability and demonstrate the transferability of PS in barley breeding.
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Accurate identification of individual plants from unmanned aerial vehicle (UAV) imagery is essential for high-throughput phenotyping and data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction. The pipeline integrates UAV image processing, user-guided annotation of selected undistorted images, convolutional neural network–based object-detection training, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. This workflow preserves native image geometry during detection while maintaining coordinate traceability from source imagery to georeferenced outputs. Across five independent training runs using early-season maize imagery, MatchPlant achieved source-image-level detection performance of AP@0.5 = 90.3 ± 1.1% and mAP@0.5:0.95 = 43.4 ± 2.9%. Orthomosaic-level evaluation after forward projection showed AP@0.5 = 89.7 ± 0.8% and recall = 93.7 ± 1.2%, demonstrating the workflow’s ability to transfer plant detections into georeferenced outputs. Plant-level traits, including plant height derived from canopy height models and NDVI derived from vegetation index rasters, showed strong agreement with manual annotations ( r = 0.87–0.97). Detection outputs were reused across time points with minimal additional annotation, supporting temporal phenotyping during early growth. The framework was validated using maize imagery from a single site and growing season, where plant separation remained clear. By combining modular design, reproducibility, and coordinate traceability, MatchPlant provides an open-source workflow for UAV-based plant-level analysis, with broader applications requiring validation across additional crops, sensors, growth stages, GSDs, and field conditions.
Reproduction assets foundThe paper's MatchPlant analysis pipeline is publicly available on GitHub, and the maize case-study training dataset and pre-trained model are publicly available on Zenodo; both are paper-specific, public, and actionable.Dataset · publicThe public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Model / weights · publicThe training dataset and pre-trained model used in the maize case study presented in Section 3 are also publicly available via Zenodo ( Sangjan et al., 2025a ) at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:70-82Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Strawberry production in hydroponic systems requires precise and spatially explicit information on fruit load, maturity, and size to support harvest planning and quality control. This study presents an integrated computer vision and GNSS RTK framework that detects individual strawberries, estimates their maturity and physical dimensions, assigns fruits to operational size–maturity categories, and generates high-resolution yield maps in an open-field hydroponic system. RGB-D images are processed with YOLOv8-seg and a BoT-SORT-based tracking module to obtain instance-level masks, from which a color-based ripeness index and 3D point clouds are derived. After DBSCAN-based filtering, fruit length and width are computed in metric space and combined with ripeness percentage to assign each strawberry to one of nine operational size–maturity categories using predefined threshold rules. The system was evaluated in a commercial hydroponic crop in Arcabuco, Boyacá, Colombia, achieving accurate segmentation, with median IoU values up to 0.83 for bounding boxes and 0.71 for masks, and mean absolute errors of 2.51 mm in length and 1.85 mm in width with respect to Vernier caliper measurements. Yield maps aggregated in 1 m2 cells and by crop row revealed marked spatial variability in fruit density, maturity state, and size–maturity composition, allowing the identification of zones with a high concentration of fruits ready for harvest versus areas dominated by immature fruits. The proposed framework provides a practical and low-cost decision support tool for hydroponic strawberry management and may be adapted to other high-value horticultural crops after recalibration under different crop architectures, cultivars, and environmental conditions.
Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.
Dr. Abdul Qayyum · Muhammad Haris Malik · Muhammad Aqib Malik · Dr. Sami Ullah Khan · Zahid Mahmood · Bilal Khan
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Dr. Abdul Qayyum · Muhammad Haris Malik · Muhammad Aqib Malik · Dr. Sami Ullah Khan · Zahid Mahmood · Bilal Khan
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Whole plant leaf expansion (shoot expansion) under drought drives the trade-off between water saving for later grain production and canopy photosynthesis. Fine-tuning shoot expansion could therefore become a target of genetic progress for drought-prone environments. However, its components (axis production, i.e. tillering, leaf production on each axis, and individual leaf elongation) may have their own genotypic variability and plasticity under drought, making hard to calibrate crop simulation models and specify breeding targets. In this study, we focused on the genetic diversity of bread wheat and durum wheat to determine the links and trade-offs between the underlying processes of shoot expansion under drought and how it translates at the whole plant and canopy level. For that, we used non-destructive imaging both in the field and controlled condition platforms to determine their dynamics and analyze their relative contribution to the genotypic variability of whole-plant shoot expansion under drought. Results show that shoot expansion measured at plant level in controlled environment was associated with that measured at canopy level in the field, indicating that controlled phenotyping platforms can capture the genotypic variability of growth in the field. Both whole-plant and canopy expansion were associated with tillering rate. In addition, the sensitivity of shoot growth and tillering to soil water deficit were correlated, indicating that both tillering ability and sensitivity to water deficit drive the genotypic variability of shoot expansion. Overall, dissecting shoot- expansion dynamics allowed determining the links between shoot expansion traits under drought, and provides key targets in phenotyping, modelling and breeding for drought environments.
Accurate identification of crop varieties is essential for plant breeding programs and the protection of Plant Breeders' Rights (PBR), yet traditional morphological assessment methods remain subjective and time-consuming, particularly for species with complex morphological diversity such as Rubus crataegifolius . This study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties, addressing the limitations of subjective visual assessment while remaining compatible with molecular marker analysis. We employed three complementary morphometric approaches: landmark-based analysis (19 anatomical points capturing vein junctions and leaf margins), Elliptic Fourier Descriptors (EFD) for outline contours, and a hybrid landmark-EFD dataset. Using these approaches, we analyzed primocane and floricane leaves from 10 accessions of R. crataegifolius comprising 8 varieties and 2 landraces and performed principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation. As a result, among the three morphometric approaches applied to primocane and floricane leaves, landmark-based analysis of primocane leaves achieved the highest classification accuracy (87.2%), with an overall average accuracy of 72.0% (range: 51.1-87.2%) across all six analytical combinations. LDA visualization suggested the presence of four major morphological groups, and primocane leaves exhibited higher discriminatory power than floricane leaves, which may reflect greater morphological uniformity under normal growing conditions. Landmark analysis effectively detected subtle differences in leaf venation and leaflet architecture that are difficult to distinguish visually, highlighting the capacity of morphometrics for objective and multidimensional morphological analysis. These findings suggest that morphometric analysis provides a practical and cost-effective preliminary screening tool, complementary to molecular approaches, for supporting Distinctness, Uniformity, and Stability (DUS) examination in raspberry variety evaluation. This approach shows strong potential for offering a scalable solution for variety registration and protection and supporting sustainable horticultural development.
Abstract Moringa oleifera is widely used in dry tropical and subtropical regions due to its rapid growth and high nutritional value, yet its internal tissue organization has primarily been described using two-dimensional anatomical approaches. Here, we present a three-dimensional micro–X-ray computed tomography (micro-XCT) characterization of lumen space in stem, branch, and outer tissues (bark region) from a single M. oleifera individual. Samples were oven-dried prior to imaging; therefore, the quantified void fraction represents apparent lumen/void space in dried material and should not be interpreted as in vivo porosity. Micro-XCT datasets were segmented to quantify cross-sectional lumen area distributions and apparent void fraction from pooled reconstructed slices. All data originate from a single individual; reported metrics represent structural descriptors of pooled cross-sections and not replicated biological measurements. The stem dataset exhibited a dense arrangement of small lumen features and a high number of segmented objects, consistent with a compact woody tissue organization in the scanned region. The branch dataset showed a larger proportion of void space and a strongly right-skewed size distribution with a minority of large lumen features. The outer tissue dataset displayed heterogeneous void space organization, which likely reflects a mixture of cell lumens, intercellular spaces, and drying-related cracks, and therefore is reported descriptively without assigning xylem-vessel identity. This study provides a conservative 3D structural dataset and an image-analysis workflow for quantifying lumen space in dried M. oleifera tissues, complementing published anatomical descriptions. The results highlight strong within-plant heterogeneity across tissue types and underscore the importance of sample preparation and histological validation when interpreting micro-XCT measurements in woody plants.
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Plant phenotyping relevance matchCrossref · Europe PMC · checked 5 Sept 2026
Abstract This paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks. Unlike conventional antennas designed for free-space operation, the proposed antenna is explicitly engineered to operate in close proximity to a lossy, anisotropic, and dispersive cylindrical medium representing wood tissues. A physics-based electromagnetic model of the stem is incorporated into the design process, enabling accurate optimization under realistic dielectric loading conditions. The antenna consists of a compact quasi-planar dipole with blended arms integrated with a medium-adaptive dual-ring balun that ensures balanced current excitation and stable impedance matching under strong near-field loading. Both simulation and experimental measurements demonstrate wideband impedance matching to a 50 Ω source over the 2.0–3.0 GHz frequency range. Surface current distribution and specific absorption rate (SAR) analyses confirm efficient electromagnetic coupling into the stem tissues with minimal radiation leakage. To evaluate the sensing capability of the proposed design, a conceptual two-element antenna system is introduced as a feasibility study for cavity detection. The detection performance is assessed through a sensitivity-driven framework based on variations in both self- and mutual-scattering parameters. A comprehensive sensitivity analysis is conducted to quantify the response of the system to changes in cavity diameter, radial position, and angular location. The results demonstrate that while the reflection coefficient is primarily sensitive to near-surface inhomogeneities, the mutual coupling between antenna elements provides strong and reliable sensitivity to internal cavity characteristics. Based on the sensitivity analysis, an optimal operating frequency band centered at 2.76 GHz and an appropriate antenna clearance are identified to maximize detection performance. The proposed antenna and sensing methodology are further validated through experimental measurements, confirming the consistency with numerical results. Simulation results demonstrate cavity-detection sensitivity, while experimental measurements validate the antenna impedance matching and mutual-coupling characteristics. The compact geometry of the antenna enables scalable multi-element configurations, establishing a practical framework for non-destructive, microwave-based monitoring of internal tree degradation in agricultural and forestry applications.
Introduction: Cucumber diseases severely affect yield and quality. Deep learning-based analysis of microscopic pathogen images enables high-throughput identification and counting of pathogens, thereby facilitating early disease detection. However, most existing pathogen-recognition methods focus mainly on qualitative identification and cannot quantitatively characterize pathogen morphology, which limits their ability to reveal the developmental characteristics and functional differentiation of different infection structures from the perspective of pathogen morphology-function adaptability. Methods: To address this issue, this study focused on cucumber powdery mildew and downy mildew and achieved precise extraction and characterization of pathogen morphological features based on microscopic image instance segmentation. First, an in situ stained microscopic image dataset of cucumber pathogens was constructed. Second, an instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images. Finally, morphological analysis methods were used to quantitatively extract and characterize pathogen infection-structure features. Results: values greater than 0.90. In addition, category-wise morphological distribution analysis showed that different infection-structure types exhibited clear differentiation in size, contour complexity, and elongation. Discussion: This study provides an effective tool for high-throughput phenotyping of cucumber pathogen infection structures. The proposed method offers methodological support for disease diagnosis, pathogen morphological phenotyping, and precision disease management in horticultural production.
Here, we present a single-operator push-cart platform equipped with a 16-beam LiDAR. A push-button interface controls data acquisition, and the data processing pipeline removes ground points, filters noise, performs 5-cm voxelization, and produces plot-level canopy metrics. We validated biomass estimation in hairy vetch (Vicia villosa) and corn (Zea mays) leaf- and whole-plant thinning experiments. In vetch, voxelized estimation of plant volume correlated strongly with destructively measured biomass (r2 = 0.88), showing that the multi-beam LiDAR can produce biomass estimates comparable to previously reported methods. In corn, comparisons of perpendicular (0°) and multi-angle LiDAR beams showed significantly greater voxel counts in the upper canopy when angled beams were used (beam angle × height interaction, p < 0.001), demonstrating that multi-beam scanning provides greater penetration into the upper canopy than a single perpendicular scan plane. We also extended the suite of LiDAR-derived traits to include apparent leaf area index (LAI), mean tilt angle (MTA), persistent homology-based stand density, and plot-bounded foliage area density (FAD). The persistent homology algorithm distinguished between leaf-removal and plant-removal treatments (removal type × removal amount, p = 0.0039). LiDAR-derived LAI has been used to estimate canopy leaf area, but gap-fraction approaches do not fully exploit the ability of LiDAR to resolve distance. Plot-bounded FAD used ray length and interception distance within defined plot volumes and was more sensitive to plot-level treatments than apparent LAI or MTA, detecting differences associated with both the removal amount and removal type. These results show that a robust, portable, multi-beam LiDAR cart can reproduce plot-level canopy measurements and improve trait especially in research-sized plots.
Mabrouk M, Russell NJ, Alegria EV, Wang T, Liang J, Wu F, Huang Y, Wittkop B, Snowdon R, Förter L, Moritz A, Herzog E, Ganji E, Wehner G, Stahl A, Chen T.
Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO₂ diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.
Reproduction assets foundThe paper's Data and code availability section states that all data are publicly available in a Zenodo repository and that the stomatal identification and trait quantification code is in the authors' public GitLab repository. Both URLs appear verbatim in the supplied blocks and match allowed_urls. The Zenodo DOI in theCode · publicThe code for all the programs in this paper, including the stomatal identification and trait quantification, can be found in our GitLab repository, https://scm.cms.hu-berlin.de/intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotyping .Open asset ↗intensive-plant-food-systems-public/2026-mabrouk-stomatal-phenotypinglines:197-215Plant phenotyping relevance matchEurope PMC · checked 16 Sept 2026
BarleyLaboratory / benchtopRootMorphology / geometry measurementSegmentationGrowth / development / phenologyRoot system architecture
Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non-machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of barley root system architecture and accompanied visible projected root hair area (VP root hair area) over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hairs from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for projected root area and 0.65 for VP root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm -3 ). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a training-free method for integrated analysis of root traits e.g. projected root size, root growth, root diameter and root hairs under controlled physical conditions similar to soil, facilitating studies of root-soil interactions that require both spatial resolution and temporal continuity.
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Urban forest health may be monitored and supported with the implementation and maintenance of an accurate community tree inventory. The longitudinal recording of a tree’s attributes (i.e. diameter and height) may inform potential inputs and activities related to maintenance, health, and the establishment of a tree protection zone. Urban tree inventories feature barriers to implementation including resources (i.e. labour, time, and finances), competing priorities, and gaps in knowledge. The objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter (1) with oblique RGB imagery, (2) with and without GCP, and (3) during leaf-off and leaf-on conditions. Structure-from-Motion datasets were obtained using Unoccupied Aerial Systems (UAS) – “drones” and related components – to collect oblique aerial imagery, which was processed with photogrammetry software Agisoft Metashape. Tree height measurements using leaf-on imagery (R2 = 0.58, RMSE = 1.34 m) were more accurate when compared to leaf-off imagery with Ground Control Points (GCP) (R2 = 0.47, RMSE = 3.41 m) and leaf-off imagery without GCPs (R2 = 0.43, RMSE = 3.49 m). Tree height measurements during the leaf-off period had no significant difference when comparing imagery with and without GCPs. Trunk diameter measurements using leaf-off imagery were not significantly different with the use of GCPs (R2 = 0.68, RMSE = 6.39 cm) compared to those without (R2 = 0.68, RMSE = 7.85 cm). This case study highlights the applicability and accuracy of Unoccupied Aerial Systems and Structure-from-Motion methods when collecting important urban tree inventory parameters, and presents an accessible, reliable, and replicable workflow for urban forestry practitioners with limited photogrammetry-related experience.
Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.
Marcel M. El Hajj · Kasper Johansen · Oliver M. Lopez Valencia · Fabio Veiga de Camargo · Yu-Hsuan Tu · Samer K. Al Mashaharawi · Omar A. López Camargo · Victor Angulo · Dominique Courault · Matthew F. McCabe
Purpose In recent years, there has been a growing use of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards. However, using LiDAR time-series collected throughout the growing season to assess PAI variations in response to phenology, represents an understudied area of investigation. Furthermore, establishing the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point cloud data remains poorly defined. Here, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees. Methods A time-series of 14 point cloud acquisitions with a density of 3300 points/m2 was collected between February and December 2022, covering all phenological stages of a cherry orchard in southern France. A voxel-based method was applied to create a three-dimensional grid within which PAI was estimated for each voxel. PAI was mapped by accumulating the individual voxel-based PAI values within each vertical voxel column. Results The results demonstrate that a voxel size of at least 0.7 m is required to retrieve reliable PAI estimates (RMSE = 0.58 m2.m−2, MAE = 0.48 m2.m−2, bias = 0.19 m2.m−2, rRMSE = 23%, and R2 = 0.51), while a voxel size of 1 m produced the most accurate PAI estimates (RMSE = 0.5 m2.m−2, MAE = 0.41 m2.m−2, bias = 0.07 m2.m−2, R2 = 0.59), when assessed against field-based PAI measurements obtained with a LAI-2200 Plant Canopy Analyzer. The temporal variation of canopy PAI illustrated the progression of key phenological stages, including flowering, leaf development, ripening and senescence, as well as the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level, which provides valuable insight into tree condition. Conclusion This study confirms that seasonal UAV-LiDAR monitoring is a viable, informative approach for capturing orchard canopy dynamics at the individual tree and sub-canopy level, linking canopy structure to phenology, varietal differences, and stress responses across the growing season.
Mudrageda P. · Schultz T.J. · Fischer A. · Sheng H. · Lama S. · Pierik R. · Dimech A.M. · Bhatt P.K. · van Daalen T. · Luebbert C. · Gehan M.A. · Barbero F. · Jimenez K.M. · Ly N. · Ying K.R. · Braley J. · Cunningham S. · Jarolmasjed S. · Zare A. · Hernandez G.L. · Schuhl H. · Dilhara M. · Lohbihler M. · Robey C. · Kenney S. · Peery J.D. · Tovar J.C. · Watson M. · Wheeler J.J. · Ballenger J.G. · Teng C. · Manching H.K. · Srivastava D. · Gordon J.M. · Fahlgren N. · Meerdink S. · Kutschera A. · Gutierrez J. · Konkel G. · Miklave N.M. · Acosta-Gamboa L. · Hodge J.G. · Zhou Y. · Lorence A. · Schneider D. · Seigel E. · Marrano A. · Summerer S. · Hendrikse C. · Duenwald J.G. · Brown K.E. · Thompson A.E. · Murphy K.M. · Huber M. · Brown A. M. · Casto A.L. · Wilson M.C. · Polydore S. · Chavez L. · Sumner J. · Hurr B.M. · Rogers T.
PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Reproduction assets foundThe paper's data availability statement explicitly says that scripts used for the analyses in this paper are publicly available on GitHub (danforthcenter/plantcv-4-paper), and PlantCV source code is available via the PlantCV homepage. This is a paper-specific, public, actionable analysis-code asset.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-92Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 11 Sept 2026
A bstract Quantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The model’s generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R 2 of 0.90 and an RMSE of 2.9 mm for RL, and an R 2 of 0.88 and an RMSE of 4.2 mm 2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R 2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.
Introduction: Plant phenotyping requires accurate and repeatable three-dimensional structural information, but practical acquisition conditions in greenhouses, seedling rooms, and indoor pot experiments often include complex backgrounds, handheld motion blur, and thin leaf structures. These factors reduce the robustness of conventional three-dimensional reconstruction methods and limit their use in low-cost and automated phenotyping. Methods: To address this problem, this paper proposes F2DMAS, an automated three-dimensional plant phenotyping workflow using consumer-grade smartphone videos. The workflow first converts multiview RGB videos into image sequences and removes motion-blurred frames through frequency-domain quality filtering. A frequency-spatial plant segmentation module, termed FSAM3, is then introduced to separate plant structures from complex backgrounds without task-specific annotated training data. The segmented image sequences are further reconstructed using 2D Gaussian Splatting, followed by TSDF-based meshing, scale recovery, and virtual measurement for extracting plant height, canopy width, leaf length, and leaf width. Results: Experiments were conducted on 15 plant species under two acquisition scenarios. The proposed workflow achieved stable plant reconstruction under non-ideal background conditions, with PSNR, SSIM, and LPIPS values of 31.09, 0.9711, and 0.0365, respectively. Compared with the baseline reconstruction workflow, F2DMAS substantially reduced the processing time for mesh extraction while improving reconstruction quality. The extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99, RMSE values ranging from 0.64 to 1.21 cm, and MAPE values ranging from 4.50% to 9.73%. Discussion: These results indicate that F2DMAS can provide an end-to-end workflow from smartphone video acquisition and plant segmentation to three-dimensional reconstruction and phenotypic trait extraction. The proposed method offers a practical and deployable solution for greenhouse seedling cultivation, potted plant experiments, and low-cost three-dimensional plant phenotyping.
José Tiago Barroso Chagas · Mauricio S. Araújo · João P.S. Pavan · André O. Françani · Gérson N.C. Ferreira · André A. Stella · Marcela Martinez · Yasmim I. Retore · Bruno F. Fregonezi · Gyanlucca M. Martins · Tong Zhao · Jianye Zhou · Dayana R. Sousa · Ya Gao · Donghong Ji · Kaizhou Gao · Bo Li · Shouyang Liu · Liang Zhao · José B. Pinheiro
(L.) Merrill) is a highly important crop widely used for food, edible oil, animal feed, and microbial fermentation products. Traditional phenotypic measurement methods are often time-consuming, labor-intensive, destructive to plants, and prone to human error. High-Throughput Phenotyping (HTP) enables precise assessment of multiple soybean phenotypic features, including morphology, physiology, diseases, pests, and agronomic traits. Artificial Intelligence (AI) is a research field dedicated to developing algorithms for multiple tasks. This review highlights the application of HTP and AI in soybean breeding programs. We discuss the challenges of implementing HTP in soybean breeding and focus on the potential and limitations of Deep Learning (DL) to support soybean breeding goals. We demonstrate the application of HTP to key soybean traits, several HTP platforms, as well as DL applications across different datasets and strategies for developing large foundation models. While integrating AI into soybean breeding programs remains a challenge, leveraging HTP data and Large Language Models (LLMs) could reshape soybean breeding.
Abstract Background Accurate modeling of wildland fuelbeds requires knowledge of not only where fuels are in three-dimensional (3D) space but also what they are. In this study, we introduce an object-based scanning protocol designed to generate detailed three-dimensional mesh models of individual fuel particles (e.g., seedlings, shrubs, litter, and cones) using an industrial-grade laser scanner. While traditional terrestrial laser scanning (TLS) or photogrammetric approaches tend to require objects to be segmented from broader-scope environment-level point clouds, our approach begins with the object itself. Results By scanning discrete plant parts in controlled conditions and capturing their morphology, surface area, and volume at sub-millimeter precision, we create a methodological foundation for fuel characterization that is structurally explicit and ecologically specific. We also propose a flexible workflow to adapt the scanning process for the extensive natural range of variation in fuel object structures, classifying individual objects based on their structural complexity. Conclusions Digital twins of wildland fuel plants and particles serve as building blocks for future integration with machine learning techniques to improve wildland fuelbed classification and simulation. Our approach shifts the basis of 3D fuels modeling from environmental scanning toward object-driven understanding with implications for fire behavior, emissions, and ecological modeling.
An integrative principal component analysis-artificial neural network (PCA-ANN) framework was developed to characterize ecotypic variation among parsley landraces and predict quality-related traits in medicinal and aromatic plants. Fifteen Iranian parsley landraces collected from diverse agro-ecological regions, together with two commercial cultivars as reference genotypes, were analyzed to establish predictive links between easily measurable morphological traits and key biochemical, mineral, and essential-oil (EO) characteristics. Twenty-one independent morphological variables were recorded and used as model inputs. To minimize redundancy and multicollinearity, PCA was applied exclusively to the morphological dataset, reducing it to a smaller set of uncorrelated components that preserved most of the variance. These components served as input features for optimized ANN architectures developed to predict antioxidant properties, EO yield and composition, and mineral nutrient content. The resulting PCA-ANN framework achieved strong predictive performance, with R² up to 0.94. It accurately predicted antioxidant, mineral, and compositional profiles from morphological traits alone, demonstrating the potential of morphological phenotyping as a rapid, non-destructive proxy for complex chemical analyses. This integrative modeling approach reduces reliance on time-consuming and costly procedures such as GC-MS and offers a practical decision-support tool for genotype selection, breeding, and quality evaluation in medicinal and aromatic crops. The proposed framework provides a scalable, data-driven strategy for advancing precision agriculture and sustainable management of herbal plant resources.
Abstract Background Stomata and pavement cells are fundamental components of the leaf epidermis, jointly regulating gas exchange, water loss, and leaf surface expansion. Stomata size, aperture, density, and pavement cell morphology are critical parameters for assessing plant transpiration efficiency, epidermal growth dynamics, and adaptive responses to environmental constraints. Despite their biological importance, quantifying stomatal and pavement-cell traits remains seldom not generalized, simple, and fast enough . Manual or semi-automated approaches limit large-scale phenotyping and restrict the integration of epidermal morphology into crop-improvement pipelines aimed at developing climate-resilient varieties with optimised stomatal patterning. To address such limitations, we developed Stomatalia , a deep learning-based platform designed to automate and standardise the quantification of stomatal and pavement cell traits. The algorithm was trained on epidermal images of cultivated and wild potato and tomato genotypes grown under optimal and abiotic-stress conditions. Stomatalia automatically detects stomata and pavement cells and extracts a broad range of morphological and integrative epidermal parameters, enabling high-throughput phenotyping within a unified workflow. Results Prior to platform development, we optimised a rapid, minimally-destructive leaf-printing protocol that generates negative impressions of the leaf surface within 40–100 s. Transparent positive prints were subsequently produced and imaged under a light microscope at scale settings ranging from 20 to 200 μm. The resulting images are analysed using Stomatalia’s user-friendly web-based interface, which runs an instance-segmentation deep learning algorithm to detect, count, and calculate stomatal and pavement cell parameters. The platform outputs structured files containing raw measurements, derived integrative traits, and associated metadata, facilitating downstream statistical and physiological analyses. Algorithm evaluation on independent datasets demonstrated high performance within the validated dicot imaging domain, with F 1 -scores ranging from 0.86 to 0.94 depending on image scale, species, and resolution, and high segmentation overlap for both stomata and pavement cells. The generality of stomatal detection was also tested on spring onion, chickpea, balsam poplar, and wheat in cross-species feasibility tests, although performance was more variable in monocots, and pavement-cell segmentation remained species- and architecture-dependent. Benchmarking against another publicly available app further showed that, under the tested web interface settings and image types, Stomatalia exhibited closer agreement with manual counts and substantially faster processing times. The practical performance of Stomatalia was further tested in a proof-of-concept trial with potato plants subjected to optimal irrigation and a long, gradual drought. The platform reliably quantified epidermal traits despite variations in leaf morphology and image quality, supporting the integrated interpretation of stomatal and pavement-cell responses under stress. Conclusions We developed Stomatalia as a robust, user-friendly deep learning platform for automated, high-throughput analysis of bright-field leaf epidermal images across varying magnifications and resolutions. Stomatalia facilitates rapid, reproducible, and coordinated phenotyping of stomatal and pavement cells by integrating methodological standardisation, computational automation, and multi-trait extraction in a single analytical workflow. Its strongest current application is the analysis of high-quality dicot leaf-print images, particularly in species and imaging conditions similar to those used for model training and validation. Cross-species and benchmark analyses further define its current scope: stomatal detection can be transferred to some additional epidermal architectures, whereas robust pavement-cell segmentation in monocots or highly divergent species will require further annotation and model retraining. Within these defined boundaries, Stomatalia provides a flexible and extensible framework for studying stomatal and pavement cell morphology and environmental plasticity, while also supporting broader efforts to dissect and optimise plant responses to abiotic stress.
Reproduction assets foundThe authors publicly deposited the paper's test image datasets (raw/input leaf-print images, detection outputs, manual ground-truth counts, exported datasets) and the model file on Figshare, and provide a public Google Colab demo for running the Stomatalia algorithm. Both are paper-specific, public, and actionable.Dataset · publicThe test datasets and model file used in this work are available through the following link:
https://doi.org/10.6084/m9.figshare.32532672. The test_sets.zip archive contains the test image datasets
(cross-species and benchmark analysis), including raw/input images, detection output images, manual
ground-truth counts and exported datasets.Open asset ↗Figshare · 10.6084/m9.figshare.32532672pdf-page:25 lines:1-75Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Handheld Mobile Laser Scanning (HMLS) is increasingly used for high resolution 3D mapping in construction, mining and natural environments. This study evaluates the strengths and limitations of HMLS for vegetation assessment in diverse tropical eco-systems across north Queensland, Australia, including rangelands, grasslands, man-groves and estuarine wetland forests. We assessed the accuracy of HMLS-derived point clouds against ground-truth measurements and compared performance with UAV SfM–MVS surveying. HMLS achieved centimeter-level accuracy for vegetation metrics, with mean absolute errors of 8.5 cm for Diameter at Breast Height (DBH) in rangeland forests and 6.7 cm for tussock height. The system consistently produced high-density point clouds, enabling detailed characterization of vertical structure, particularly understory vegetation often obscured in aerial surveys. HMLS proved operationally flexible across closed-canopy wetlands, mangroves, rangeland forests and open grasslands. Key limi-tations included restricted horizontal point cloud penetration in dense vegetation, compounded by access constraints and environmental conditions, and point cloud drift in areas with few geometric features, such as grasslands, which introduced uncertainty in vegetation metrics. High computational demands further constrained workflow effi-ciency. Overall, HMLS demonstrates strong potential as an accurate and versatile tool for vegetation mapping and structural analysis in complex tropical ecosystems.
Abstract In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker–trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.
Hu Y, Cui S, Li H, Hou H, Xu Z, Hao B, Cai L, Zhu L, Wang J, Chang K, Li W, Shao W, Zhu S, Li C, Zhao Z, Jiang L, Tian Y, Liu X, Liu S, Chen L, Zhou S, Wan J.
Rice lodicules are specialized floral organs located at the base of the ovary that undergo dynamic morphological changes during the flowering period. Water uptake-driven swelling and subsequent dehydration-induced shrinkage of the lodicules trigger floret opening and closure, respectively. Although lodicules play a central role in floret movement, standardized methods for quantitatively monitoring their temporal morphological changes remain limited. Here, we describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis. Florets are collected at predefined clock time points during the flowering period, and lodicules are carefully isolated under a stereomicroscope. High-resolution imaging is performed under consistent acquisition settings, followed by precise measurement of lodicule length, width, and thickness using image analysis software. This protocol emphasizes positional consistency in sampling, uniform imaging parameters, and standardized data analysis to enhance reproducibility. This method is suitable for evaluating the effects of genetic background or environmental conditions on lodicule morphology. By providing a standardized analytical framework, this protocol enables accurate and quantitative morphometric analysis of rice lodicules during floret opening. Key features • Standardized time-point sampling minimizes variability caused by diurnal fluctuations and handling during lodicule morphometric analysis. • Enables reproducible isolation and imaging of rice lodicules while preserving native morphology and preventing dehydration-induced artifacts. • Time-resolved workflow enables analysis of rapid morphological changes associated with floret opening and closure. • Applicable for comparing genetic and environmental effects on lodicule morphology under controlled experimental conditions.
Abstract Agroforestry systems (AFS) offer a promising strategy to address environmental challenges while supporting rising food demands. However, the complex interactions between trees and crops complicate research, particularly regarding their effects on crop yields. This study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study. Growth parameters, namely the Normalized Difference Vegetation Index (NDVI) and plant height, were derived as proxies for yield and analyzed in relation to the distance from tree stripes. Additionally, direction-dependent regression analyses were conducted to assess whether spatial variations in the field could be attributed to the trees. Two distinct patterns emerged: first, a pronounced increase in NDVI was observed at close proximity to the trees, correlated with tree height and schematically illustrated for two representative tree stripes; second, at greater distances, fluctuations in NDVI were associated with the trees but lacked consistent directional trends. Considerable inconsistencies were also observed in plant height variations. The discussion highlights potential drivers of the close-range NDVI increase, the applicability of UAS for AFS research, and limitations in generalizing findings from a single case study. Overall, the results demonstrate that tree effects on crop growth and vitality are detectable but marginal in terms of their influence on maize yields at this site, while showcasing the utility of UAS-based approaches for field-scale analysis of AFS.
Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological factors associated with NSC accumulation remain unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. In addition, plant growth regulator treatments that altered VCR were accompanied by changes in culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving stem carbohydrate storage capacity in rice.
Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicOur codes are available at https://github.com/Petrichoror/CitrusGS .Open asset ↗Petrichoror/CitrusGSlines:325-387Plant phenotyping relevance matchOpenAlex · arXiv · checked 5 Sept 2026
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion. In this work, we show that the conventional 3D plant phenotyping pipeline could be streamlined and significantly accelerated with 3D Foundation Models (3DFMs), and particularly, present one of the first cross-crop 3D phenotyping frameworks powered by 3DFMs. The framework replaces COLMAP-style sparse initialization with 3DFM-based feed-forward geometric recovery, combines geometry-constrained 3D Gaussian Splatting for dense reconstruction, enables few-view reconstruction through iterative view synthesis and refinement, and converts reconstructed geometry into measurable organs through 2D-to-3D semantic transfer, metric scale recovery, and organ instance separation. We further construct a cross-crop dataset with smartphone-based image acquisition, diverse plant morphologies, and manual annotations for segmentation and phenotypic evaluation. Experiments across 26 plant sequences show that 3D Foundation Models reduce the average reconstruction time from 6.52 minutes to 1.58 seconds while maintaining high reconstruction quality and phenotyping accuracy. These results suggest a fresh technical route for high-throughput 3D plant phenotyping, from low-cost image acquisition to fast reconstruction, perception, scale recovery, and phenotypic measurement.
Braunmiller HM, Bitterlich M, Koebernick N, Jacob E, Heck AS, Schnepf A, Pausch J, Suuronen JP, Hesse B, Delzon S, Perrin J, King A, Jansa J, Ahmed MA.
Arbuscular mycorrhizal fungi (AMF) contribute to plant nutrient and water uptake via their extraradical hyphal networks. However, in situ methodologies to quantify architectural and morphological traits of these networks in soil are largely lacking, limiting our understanding of AMF-mediated resource transport. Using synchrotron-based X-ray computed microtomography (micro-CT), we established a workflow to cultivate, noninvasively image, and quantitatively analyze AMF hyphosphere and rhizosphere structures in the interaggregate space across two soil textures and biological contexts. We developed a pipeline for quantitative three-dimensional (3D) assessment of key architectural and morphological traits including structure counts, hyphal length, branching frequency, volume, and surface area. Our method further permits (1) measurement of AMF-soil and AMF-root interface areas and (2) microscale quantification of pore space occupancy by AMF. Micro-CT offers a tool for noninvasively visualizing AMF in air-filled soil pore space. We outline how such quantitative 3D information can be incorporated into image-based and functional-structural soil-plant models, thereby supporting a better mechanistic understanding of AMF-mediated processes in soils and plants.
ABSTRACT Vase life is a key determinant of cut flower quality and market value. Conventional vase life assessment relies on visual inspection and physiological monitoring over several days to weeks, making it labor‐ and time‐intensive. This study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers. Six chrysanthemum cultivars were grown under two supplemental lighting treatments (Hybrid and LED) and two planting densities (54 and 74 plants m −2 ). Acoustic monitoring was compared with optical microscopy for the assessment of xylem vessel diameter, while conventional vase‐life testing was performed in parallel. Optical microscopy validated the acoustic measurements, with both methods consistently identifying vessel radii around 10 μm. The acoustic radius (), derived from pulse settling time measurements, showed cultivar‐ and planting‐density‐specific variation. Linear mixed‐effects modelling demonstrated that the relationship between acoustic radius and vase life differed significantly among cultivars, indicating that a universal relationship across cultivars is not supported. These findings show that acoustic monitoring provides a meaningful noninvasive proxy for vase‐life‐associated stem traits and may serve as a useful cultivar‐calibrated tool for evaluating postharvest longevity in cut chrysanthemums.
Leaf orientation (posture) influences photosynthesis and plant responses to environmental cues. However, the existing methods for quantifying posture typically compress its inherently three-dimensional structure into scalar values or single vectors, leaving the 3D aspects of leaf movement poorly understood. For a more complete geometric description, we propose representing leaf-blade posture by an orthonormal basis (ONB), defined as three perpendicular unit vectors aligned with the developmental axes of the leaf. This ONB serves as a local coordinate system and corresponds to rotation matrices used to represent orientation in three-dimensional space, embedding leaf posture within the mathematical structure of the special orthogonal group SO(3). Using three-dimensional point-cloud data, we reconstructed an ONB aligned with the three axes of the leaf blade and quantified elevation and azimuth angles. When applied to diurnal posture changes, the resulting angular patterns were consistent with previous observations. We then visualized posture changes after gravitational perturbation as continuous rotational trajectories. These trajectories could be compared with mathematically defined geodesic shortest paths and used to simulate alternative reorientation routes that satisfy constraints. The rotational trajectories could also be separated into swing and twist components, which reflect distinct deformation modes. The ONB can be obtained not only from three-dimensional point clouds but also from other measurement tools, making the approach broadly applicable. Conceptually, ONB representation places leaf posture within the geometric structure of SO(3), enabling the use of well-established mathematical tools such as rotational distance and geodesics for analyzing leaf reorientation.
The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping
Accurate perception and 3D reconstruction of fruit tree branch structures are fundamental to smart orchard development, with broad applications in intelligent harvesting, crop phenotyping, and precision management. However, the slender and highly branched morphology, multi-scale distribution, weak surface texture, and severe occlusion inherent to fruit tree branches pose substantial challenges to high-fidelity modeling. This paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies. A structured literature search was conducted using the Web of Science, Scopus, and Google Scholar databases, with search terms including “fruit tree branch”, “point cloud reconstruction”, “3D canopy modeling”, “branch feature extraction”, and “agricultural robotics”. Studies published between 2000 and 2025 were considered, with inclusion criteria requiring relevance to branch structure perception, reconstruction accuracy, or orchard application; non-peer-reviewed sources and studies lacking quantitative evaluation were excluded. We trace the evolution of feature extraction from classical 2D image processing and geometric fitting, through point cloud segmentation and skeleton extraction, to modern deep learning approaches and multimodal perception techniques. For 3D reconstruction, we compare active and passive sensing strategies alongside both explicit and implicit scene representation methods, discussing their respective strengths and applicable scenarios. A five-dimensional evaluation framework is also proposed, encompassing geometric accuracy, structural consistency, feature stability, computational efficiency, and generalization capability. Finally, we identify key bottlenecks in fine-grained structure recovery, occlusion handling, and cross-scene generalization, and highlight future directions in structural prior integration, multimodal collaborative modeling, and lightweight neural representations—offering a structured reference for advancing 3D perception research in smart orchards.
Real-time strawberry volume estimation is a pivotal technology for automated harvesting and precision grading. However, conventional contact methods are prone to damaging fruits, while existing vision-based approaches struggle to balance high accuracy with low computational overhead. To address these challenges, this study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework. First, to precisely extract target contours in complex backgrounds, we designed Deformable Feature Aware-YOLO (DFA-YOLO) based on the YOLO11-seg architecture. This model enhances the geometric perception of irregular fruit edges and effectively overcomes the challenges of background noise and multi-scale variations, providing high-precision masks for subsequent spatial mapping. Subsequently, a principal-axis-slicing algorithm extracts the mask’s centroid and principal axis, perpendicularly slicing the mask into infinitesimal micro-slices. By computing and accumulating the pixel-space volume of these slices, the system converts them into precise 3D physical volumes based on RGB-D depth mapping. The entire system was deployed on an NVIDIA Jetson Orin edge computing platform and validated in a greenhouse. Experimental results demonstrate that the estimated volume highly agrees with the true volume, achieving a coefficient of determination (R2) of 0.945 and a mean absolute percentage error (MAPE) of 9.0%. Under typical operating conditions (1–5 targets per field of view), the system maintains an overall frame rate of 8–15 FPS, requiring only 55 ms for single-fruit estimation. This method exhibits favorable stability and lightweight efficiency under the tested greenhouse conditions, offering a reliable solution for real-time non-destructive crop phenotypic monitoring in computationally constrained agricultural environments.
Assessing tools for rapid evaluation of new cultivars across large fields is essential for improving crop yields and ensuring future food security. Current phenotyping approaches remain labor-intensive and imprecise at the field scale, particularly for traits determining light interception and the light extinction coefficient ( K ). While robotic phenotyping and three-dimensional (3D) models have gained interest in estimating light interception in plant canopies, primarily at the single-plant scale or using single-plant-derived virtual canopies, applications at the field-scale canopy level remain limited. In this study, a semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years. 3D canopy architecture revealed substantial genotype-specific variation in inclination angle and K , which influenced radiation use efficiency (RUE), contrasting with the constant K commonly assumed in conventional approaches. This architectural variation was classified as two distinct architectural phenotypes, erectophiles (median angle 65°) and planophiles (59°) at pre-anthesis stages, which converged toward being homogenous by the anthesis stage. Erectophile phenotypes exhibited higher RUE (33.1%) than planophile phenotypes, leading to a higher yield. In yield prediction analyses, 3D-derived architectural traits provided comparable predictive performance to conventional measurements, however, architectural phenotype information reduced prediction error. Together, these findings highlight the value of field-scale robotic canopy phenotyping for characterizing genotype-specific canopy architectural traits and their relationship with yield.
Zhigang Zheng · Junfei Huang · Manqing Tian · Haijun Liu · X Y Li · Youbao Sun · Qisheng Zhong · Taohong Huang · Qing Liu · Dexin Kong · Yongming Liu · Haiyang Wang
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
A dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Stomata of plants track the immediate demand for carbon dioxide for photosynthesis while limiting transpirational water loss. Solitary stomatal patterns are common, yet some land plants develop noncontiguous stomatal clustering, where 2 or more stomata occur in groups and overlay a single air cavity. Clustering improves stomatal efficiency, reduces plant water use, and increases resilience to environment stress. How cluster development and physiology interact and integrate with the environment are open questions. Here we used TESSERA, a deep-learning platform for stomatal detection with an interactive interface for data review. Tracking Begonia stomatal clustering patterns across various Begonias, we have uncovered correlations for stomatal clustering traits. The stomatal parameter data were applied to identify genetic loci involved in Begonia stomatal development using quantitative trait locus analysis. Combined with differential gene expression to refine the candidate list, our analysis reveals known and potential new Begonia candidates in stomatal development. As a test of this knowledge, we cloned Begonia SPEECHLESS (BegSPCH), a loci identified in this screen and an established development-related gene in Arabidopsis. Unexpectedly, Arabidopsis spch-3 mutants transformed to express BegSPCH developed stomatal clusters unlike the mutant plants expressing AtSPCH. Thus, various molecular and environmental factors likely overlay transcriptional regulation in stomatal development.
SUMMARY Plants display complex structural tissue arrangements and cell shapes that are intimately related to their functionality and whose precise geometry influences the metabolic and physical processes performed by different organs. Analyzing these structure–function relationships requires accurate information on the complex 3D anatomy and its changes over time at meaningful spatial resolution. A non‐invasive approach, micro‐CT imaging, can produce such 3D or 4D datasets and can be leveraged for finite element (FE) simulations of mechanical and physical processes. This combination of techniques has been employed to study biomechanical properties, gaseous diffusion, light propagation, hydraulics, and thermodynamic processes in plant organs. A deep understanding of structure–function relationships also paves the way to design bio‐inspired structures using plant anatomy as a reference. Here, we illustrate how the combination of micro‐CT‐based imaging and FE modeling can be leveraged in plant science for advanced investigation of structure–function relationships.
Published1 Jul 2026Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of CanadaCited by 0 · OpenAlex ↗
Fuzhou represents a critical center for tea genetic diversity, yet the micromorphological basis for differentiating its local landraces remains poorly understood. Scanning electron microscopy (SEM) was employed to investigate the foliar micromorphology of 28 tea landraces from Fuzhou and to characterize structural differences among them. Adaxial epidermal wax ornamentation, stomatal architecture, and nonglandular trichome patterns provided important taxonomic characters for germplasm classification. Our analysis reveals that stomata are consistently paracytic and randomly oriented on the abaxial surface. However, their dimensions exhibit high phenotypic plasticity, with mean areas ranging from 421.92 to 822.26 µm2. Leaf surface ornamentation showed high phenotypic variability, with three identifiable types: straight, wrinkled, and undulated. The length, width, and type of nonglandular trichomes varied among the landraces, with values of nonglandular trichome length ranging from 269.99 to 632.31 µm and diameter from 9.72 to 14.62 μm. The nonglandular trichome ornamentation was categorized as smooth, long-stripe, and short-stick. The study demonstrated that SEM-based analysis of foliar micromorphological traits provides a valuable tool for tea germplasm identification and cultivar improvement. Specifically, the combination of adaxial epidermal wax ornamentation and nonglandular trichome surface ornamentation provides stable and reliable diagnostic micromorphological markers for accurate differentiation and identification of Fuzhou tea landraces, filling a critical micromorphological gap in the systematic study of local tea germplasm.
Hani Atef · Laura Fierro-Dominguez · Paula Andrea Lozano-Montaña · Sergi Navarro Sanz · Julie Bals · Benoît Clerget · Christophe Périn · Maria Camila Rebolledo · Romain Fernandez
RiceRootTissueMorphology / geometry measurementSegmentationRoot system architecture
Abstract Background Quantification of rice root anatomical traits such as cortical aerenchyma lacunae is key to understanding rice adaptation to diverse water regimes and to support climate-smart breeding. Aerenchyma lacunae contributes to rice internal gas transport and influences methane emissions from flooded systems and can also limit rice water conductivity. It could be an interesting anatomical trait for breeding, however, large-scale anatomical phenotyping remains limited because manual analysis of root cross-sections is labor-intensive, subjective, and difficult to scale across heterogeneous imaging conditions. Existing pipelines often require parameter tuning and do not generalize well across environments. Results We developed a deep learning pipeline based on a vision transformer architecture to automatically segment rice root cross-sections and quantify cortical aerenchyma lacunae. The model was trained on 1,760 annotated images collected across multiple countries, growth stages, cultivation systems, and experimental contexts, using a collaboratively defined annotation protocol. The final model achieved high segmentation accuracy, with mean intersection over union values exceeding 0.92 for cortical tissues and lacunae. Quantification of the lacuna-to-cortex ratio showed strong agreement with manual annotations, with a coefficient of determination of 0.98 on an independent test set. An independent expert review indicated that model predictions were at least as consistent as manual annotations and reduced large annotation inconsistencies. The pipeline is released as open-source software and includes an interactive online demonstrator, and is accompanied by an online test dataset to support testing and reproducibility. Application across six experimental use cases revealed reproducible differences in aerenchyma lacunae across genotypes, water regimes, environments, and developmental stages. Conclusions This work provides a robust, scalable, and transferable tool for automated root anatomical phenotyping under heterogeneous experimental conditions. Transformer-based segmentation enables consistent and high-throughput quantification of lacunae, facilitating integration of these anatomical traits into breeding, physiological studies, and climate-smart crop improvement programs.
Reproduction assets foundThe paper releases its authors' phenotyping pipeline (preprocessing/training code archived on Zenodo and an interactive Hugging Face Space demonstrator with a test dataset subset) as public assets. The full multi-environment training image dataset is only available upon reasonable request, so it is not a public asset.Code · publicall code used for preprocessing and
training is released under an open-source licence on GitHub, tagged v1.0.2, and
archived with a Zenodo DOI (Atef, 2025).Open asset ↗Zenodopdf-page:46 lines:1-65Plant phenotyping relevance matchOpenAlex · arXiv · checked 5 Sept 2026
High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them. At Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory (APPL), automated stations image hundreds of plants daily across multiple remote sensing modalities; yet, trait extraction and interpretation remain manual, expert-bound, and strictly post-hoc, making analysis, not acquisition, the binding constraint on discovery. We present an end-to-end agentic AI framework that turns the facility from a data factory into an interactive autonomous, discovery platform, where scientists partner with AI agents to accelerate time to insight. A conversational Co-Scientist Agent translates a scientist's natural-language question into a structured analysis plan, and a headless Compute Agent dispatches Vision Transformer segmentation and trait extraction on the Frontier exascale supercomputer. The two agents run in separate security and resource domains and communicate over a secure, token-authenticated streaming channel, a design that accounts for the federation, data-movement, and provenance realities cloud-native agentic frameworks ignore, ensuring end-to-end provenance is captured for every interaction. The framework turns a days- to weeks-long analysis process into an interactive loop where agents reason over results, recommend next analyses, and respond to follow-up questions in seconds.
Tomato (Solanum lycopersicum) is an important horticultural crop worldwide, and its productivity is significantly limited by the widespread occurrence of foliar diseases that gradually modify leaf morphology and physiological function. Disease progression in the leaves entails intricate spatial transformations like lesion development, vein proliferation and disruption in venation, tissue necrosis, and disruption of normal laminar architecture. Often, these structural alterations may be challenging to express in a conventional visual or categorical scoring system, which is often subjective and lacks granularity. In the present study, a spatial multi-scale fractal analysis framework has been proposed to quantitatively capture structural degeneration in tomato leaves during disease progression. Leaf images from sources with known disease severity levels were analyzed through fractal geometry-based metrics, focusing on two key descriptors: fractal dimension (FD) and lacunarity.to express structural complexity and heterogeneity. Fractal dimension serves as a description of overall morphological complexity in this context, whereas lacunarity is widely used to quantify spatial irregularity and gap distributions within leaf tissues. Multi-scale analysis was employed to examine structural changes at different spatial resolutions. This helped us better understand both global shape changes and localized tissue damage. The findings demonstrate a steady reduction in fractal dimension as disease severity escalates, indicating a gradual deterioration of structural complexity. On the other hand, lacunarity values increase as the disease gets worse, which means that the leaf architecture becomes more varied and broken up. The results show that fractal-based spatial analysis is a strong, non-destructive, and objective way to measure how disease damages the structure of plant leaves. This technique holds considerable promise for utilization in automated plant disease diagnostic systems, precision agriculture, and digital plant phenotyping platforms.
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Plant phenotyping relevance matchOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput plant phenotyping captures development at scale, yet image-rich screens are still often reduced to static trait summaries. We tested whether nutrient availability, polyamine priming, concentration, and their transport reshape Arabidopsis rosette development by generating distinct morphologies or by changing residence along a common trajectory. We analyzed 138,223 time-resolved rosette images from Col-0 and five mutants involved in polyamine transport ( put1-5 ) primed to putrescine, spermidine, spermine, dose, and nutrient regimes using a self-supervised vision backbone, Poincaré embedding, hyperbolic Mapper, and manifold straightening. The data form a single connected developmental manifold with 410 nodes and 746 edges, organized from an early, low-nutrient-biased hub through high-betweenness transition corridors to two late, nutrient-enriched terminal regions. Polyamine identity stratifies this manifold by developmental phase: putrescine enriches early states, spermidine occupies transition corridors, and spermine marks late compact rosettes. Nutrient richness and dose change distal occupancy, whereas put genotypes alter dwell time within shared regions rather than producing separate topologies. Manifold straightening resolves these effects into a short early lateral deflection followed by convergence, yielding two scalar readouts, early transverse offset and distal occupancy, that summarize treatment action on a common morphodynamic scale. The framework converts large image screens into interpretable developmental geometry for image-based phenomics.
ABSTRACT Premise Seed size and morphology are critical traits in agriculture, ecology, and genetics, but high-throughput quantification of these traits is often limited by labor-intensive manual measurements or expensive, platform-specific imaging software. Methods and Results We developed SeedMeasure, a lightweight, open-source, and cross-platform command-line tool written in Python that automates the measurement of seed area, length, and width from images. Using a simple imaging setup, the program processes images by correcting for perspective skew, filtering debris, and exports quantitative data alongside quality-check images. We validated SeedMeasure across nine diverse species, ranging from small Arabidopsis thaliana seeds to large Zea mays kernels. The tool quickly handles images using multithreading and demonstrates high reproducibility, yielding low coefficients of variation across repeated runs. Conclusions Compared to existing software, SeedMeasure is free, offers faster processing through parallel computing, and provides standalone executables that require no programming dependencies. SeedMeasure offers an accessible, cost-effective, and high-throughput approach for rapid phenotypic profiling, making advanced seed morphological analysis available to researchers without specialized laboratory hardware.
Abstract The leaf area index (LAI) is a key determinant of canopy architecture and yield potential in maize, primarily through its influence on photosynthetic efficiency. Although unmanned aerial vehicle (UAV) technology has greatly advanced field-based phenotyping, its potential for deciphering the genetic mechanisms underlying dynamic and complex trait development remains underexplored. In this study, multispectral UAV images were collected from a diverse maize panel across eight developmental stages in four environments over two consecutive years. Using multi-temporal data, a random forest model accurately predicted LAI (R² = 0.82–0.83), significantly outperforming models based on single time-point data. By integrating high-throughput phenotypic predictions with time-series genome-wide association studies (GWAS), 36 dynamic SNPs associated with LAI variation were identified. Principal component analysis (PCA) of temporal LAI data revealed two principal components that together explained 84.2–86.5% of the total phenotypic variance. GWAS based on these components identified an additional 51 SNPs, seven of which overlapped between the two analytical approaches. Among the 72 candidate genes identified, Zm00001d048615 exhibited significant variation in both phenotype and expression among different inbred lines. The heterologous overexpression of Zm00001d048615 in Arabidopsis induced leaf curling and a significant reduction in leaf size, indicating its potential role in regulating leaf development. Collectively, these findings establish a robust framework that integrates UAV-based phenomics with temporal GWAS to identify key genes regulating complex dynamic traits. This approach provides valuable insights and genetic targets for improving maize canopy architecture and yield potential through molecular breeding.
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionSegmentationStress / disease detectionArchitecture / morphology / geometryGrowth / development / phenology
Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support.
Understanding root system architecture (RSA) is critical for improving crop productivity and resilience, yet phenotyping root traits such as root growth angle and rooting depth remains technically challenging, especially at high throughput. Here, we present ClearDepthIAS, a high-throughput imaging and analysis platform that enables non-destructive, automated quantification of root architecture traits in taproot system crops. By capturing and stitching 360° images of roots growing along the transparent walls of pots and applying deep learning-based segmentation (ClearDepth-WRT), we measured wall root shallowness (WRS)-a proxy for root growth angle-with high precision. We demonstrated for the tap root systems of soybean and canola that the system accurately detects root tips, quantifies their vertical distribution, and extracts biologically meaningful traits such as root area, distribution indices, and growth angles. Validation experiments in canola and soybean demonstrated that WRS can correlate with root crown architecture in mature plants, both in greenhouse and field settings. Furthermore, WRS and root distribution indices derived from ClearDepthIAS are predictors of early root architecture and can be correlated with root biomass distribution across soil depths under field conditions; however, environmental interactions may influence these relationships and weaken or even negate such correlations, as observed when comparing field to field variation in root system architecture. Our system enables efficient phenotyping of genetically diverse populations, with medium to high trait heritability, supporting its utility for genome-wide association studies and breeding. ClearDepthIAS accelerates the development of root ideotypes for improved resource acquisition and carbon sequestration, offering a scalable tool for supporting climate-resilient agriculture.
Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
R ! .
Because the fitted plane passes through the centroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Plant phenotyping relevance matchCrossref · Europe PMC · checked 5 Sept 2026
Crop ideotype breeding aims to design plant architectures that enhance yield and resource use efficiency. Accelerating this process demands a framework for assembling accurate three-dimensional (3D) architecture. This Perspective synthesizes advances in technologies and methodologies in 3D architectural phenotyping. Rapid progress has enabled translating these advances into tangible gains in breeding efficiency. To this end, we propose integrating functional-structural plant models as an overarching framework that optimizes plant architecture combinations, shifting breeding from experience-driven to predictive ideotype design. Convergence of 3D phenotyping, plant modeling and artificial intelligence holds transformative potential to accelerate breeding cycles, enhancing productivity, sustainability, and food security.
Accurate and rapid detection of maize seedling growth is critical in early breeding decisionmaking, smart management, and yield improvement. Traditional leaf age detection still relies heavily on labor-intensive and low-efficiency manual field surveys, underscoring the urgent need for high-throughput phenotyping. Integrating multisource sensor data from unmanned aerial vehicle (UAV) with measured information such as crop height can further enhance the estimation accuracy of crop phenotypic parameters. Accurate field plot segmentation is critical for field-scale phenotypic analysis. However, current approaches remain largely dependent on slow, manual segmentation. Automating this step would greatly reduce the workload of agronomists. This study used UAV RGB and multispectral imagery collected over maize inbred line population plots before the canopy closure stage to perform automatic plot segmentation on field orthophotos and combined measured plant height with relative flight dates to achieve high-throughput detection of leaf age during the maize seedling stage. First, this study proposed a maize plot automatic segmentation method based on orthophotos. Then, it extracted texture features, RGB, and multispectral vegetation indices of each plot. Combined with relative flight date and plant height, four datasets were constructed. Support vector regression (SVR), random forest regression (RFR), and automatic machine learning (AutoML) regression algorithms were used to build the leaf age detection model. The results showed that the orthomosaic from March 23 achieved the best plot-segmentation performance, with minimum intersection over union (IoU), mean IoU, and IoU standard deviation of 14.67%, 96.47%, and 8.65%, respectively. Incorporating relative flight dates and plant-height measurements improved model performance, and the AutoML demonstrated the greatest robustness, achieving a validation R2 of up to 0.862 and an RMSE as low as 0.715. This study proposed a leaf age estimation method that offers practical technical support for field-based maize seedling assessment and reduces manual labor demands.
Abstract Forest biometrics has evolved from a measurement-driven discipline focused on field efficiency and statistical rigor to a data-rich, technology-enabled science integrating multisensor information and advanced modeling approaches. This special issue, inspired by the Second North American Forest Mensurationists Conference held in 2022, highlights this transformation through nine studies that collectively span scales from individual branches to regional forest dynamics. Together, they emphasize a shift from identifying single optimal models to developing integrated, uncertainty-aware model systems that support operational decision-making. At the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume. At the tree level, extensive benchmarking of height–diameter relationships demonstrates that model form and stand origin strongly influence predictive performance, with generalized additive models often outperforming traditional approaches. Complementary work shows that calibration strategies are not universally transferable across model forms, underscoring the need for careful alignment of function choice and calibration design. Addressing biases in young stands, Bayesian model averaging offers a practical interim solution where traditional volume models trained on mature cohorts fail. At broader scales, studies demonstrate the operational potential of integrating public and low-cost remote sensing data. Freely available USGS 3DEP LiDAR supports highly accurate dominant height and site index estimation, while bias-corrected digital aerial photogrammetry provides a viable alternative in areas lacking LiDAR coverage. Landscape-level analyses using Landsat time series and permanent plots enable mapping of basal area growth, revealing spatial variability and temporal trends driven largely by stand dynamics. Collectively, these studies define a cohesive framework for modern forest biometrics: combining multiple data sources, selecting model families deliberately, applying light but effective calibration, and explicitly quantifying uncertainty. This integrated approach supports scalable, reliable predictions tailored to the needs of forest managers and policymakers. The special issue thus outlines a forward-looking research agenda that prioritizes resilient modeling systems over isolated solutions, enabling forestry to meet contemporary challenges across scales from tree components to landscapes.
Yaxin Li · Liying Cao · Limin Hu · Xian Li · Ziyi Yang · Xiaoqian Fan · Zhongchen Zhang · Qi Zhang · S W Wang · Shen Yuan · Qingshan Chen · Qiaorong Wei · Weibo Han · Panli Zhang · Le Xu
Soybean leaf morphology is an important breeding trait that requires large-scale phenotyping in commercial breeding programs. Conventional leaf phenotyping still relies on manual destructive measurements, which are labor-intensive and inefficient. Low-altitude unmanned aerial vehicles (UAVs) have emerged as a high-throughput phenotyping platform, but the retrieval of leaf morphology in densely occluded canopies remains challenging due to complex canopy backgrounds, illumination heterogeneity, and leaf overlap under field conditions. To address this issue, this study developed an integrated UAV framework that couples a YOLOv10-based leaf detection module, a novel structure-adaptive segmentation network (DynamicU), and a regression-based trait prediction model for retrieving a set of leaf morphological parameters across 273 soybean genotypes under field conditions. The YOLOv10 detector reliably localized individual leaves under complex canopy conditions, achieving a mean average precision (mAP@50) of 0.84. Subsequently, the DynamicU network, whose architecture was automatically optimized via Emperor Penguin Optimization, achieved a segmentation accuracy of 97.2% and a mean Intersection over Union of 93.8%, substantially outperforming conventional models. Using random forest regression, the framework retrieved relative leaf shape traits, including length-to-width ratio and dissection index, with markedly higher accuracy (R 2 =0.97), compared to absolute morphological traits, including leaf length, width, perimeter, and area (R 2 : 0.76–0.84). Notably, relative leaf shape traits showed positive associations with oil yield per plant and protein yield per plant, supporting their potential as complementary indicators for screening soybean germplasm with differential industrial product output. This end-to-end framework establishes a reliable bridge between UAV remote sensing and leaf-level morphological quantification, advancing high-throughput phenotyping capabilities to support precision breeding in soybean.
Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications.
Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.
L.) is increasingly being recognized for the production of functional food ingredients and nutraceutical products with broad applications in human nutrition. Its nutrient-rich seeds are of particular interest for their nutritional profile. Moreover, its inflorescences and trichomes provide sources of nutrient-rich proteins, bioactive compounds, and functional substances for food formulations. Agronomic practices, environmental factors, and genotype considerably influence the hemp nutritional profile; thus, continued interdisciplinary research is needed to standardize quality across supply chains. X-ray micro-computed tomography (micro-CT) combined with 3D image analysis is an emerging non-destructive technique in high-resolution plant phenotyping. The aim of this work was to show the contribution of X-ray micro-CT to the quantitative characterization of the internal hemp seed structure and of the trichomes. The 3D image analysis approach used allowed us to determine many morphometric traits of the different seed parts and of the trichomes. Among them, volume ratios of the different seed parts and the density and morphological characteristics of the trichomes of two cultivars were accurately quantified. Overall, this work showed the contribution of X-ray micro-CT in 3D morphometric characterization of the hemp achene structure and trichomes. The obtained seed morphometric traits could be correlated in future applications with nutritional and/or physiological properties of different hemp varieties in order to support different aspects of the whole hemp supply chain such as the dehulling process, oil and protein recovery, seed quality evaluation, and genotype screening, to which trichome characterization could also contribute.
The architecture of the root system is a primary factor in determining rootstock performance, affecting water and nutrient uptake, biomass accumulation, and overall vigor. However, direct root phenotyping is destructive, labor-intensive, and difficult to do routinely in breeding programs. The present study investigated early root morphological variation among developed interspecific tomato rootstock candidates (Solanum lycopersicum x S. habrochaites). The ability of linear regression and machine learning models to predict root traits from easily measured plant growth parameters was assessed. Nineteen interspecific hybrid rootstock candidates, two commercial rootstocks, and one scion were grown under optimal greenhouse conditions and evaluated at 0, 10, 20, and 30 days after planting. Root length, root surface area, root diameter, and root volume were determined by digital image analysis. In contrast, genotype, plant length, and stem diameter were used as input variables. Significant genotype x sampling date effects were observed for most morphological and biomass traits, indicating dynamic changes in root and shoot development during the first 30 days of growth. The rootstock candidates RSH-17 and RSH-6 generally showed relatively higher root length, surface area, root volume, and biomass accumulation than the commercial rootstocks and scion. XGBoost and OLR were the best predictive models, with R 2 values as high as 0.95 for root length, surface area, and volume. Root diameter was predicted less accurately than root length, surface area, and volume, suggesting that it might be a more independent or less variable root trait during early development. Overall, results suggest that vigor-related traits can serve as useful proxies for estimating major root architectural traits in early-stage tomato rootstock selection. Both XGBoost and OLR performed well, suggesting that root and shoot development were highly coordinated under optimal (non-stress) conditions. Hence, predictive modeling may help prioritize promising rootstock candidates before destructive root analysis. However, more validation under stress conditions and for longer periods of development is needed to determine the greater applicability of these models.
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Accurate retrieval of leaf area index (LAI) is vital for crop monitoring and genetic breeding. Although multi-modal unmanned aerial vehicle (UAV) remote sensing has advanced LAI estimation, conventional empirical models often overfit on small breeding populations and cannot disentangle the true causal effects of genetic backgrounds from confounding factors within a statistically rigorous framework. This study presents a robust framework for plot-scale maize LAI estimation across 800 breeding plots from four genetic subgroups: doubled haploid (DH), mixed, temperate (TEM), and tropical/subtropical (TST). From UAV RGB and multispectral imagery acquired at three phenological stages, we extracted 76 multi-modal features comprising point-cloud structural metrics, spectral vegetation indices, and texture features. Following a dual-criterion mutual information and multicollinearity filter, four algorithms, including traditional tree-ensembles and the Tabular Prior-data Fitted Network (TabPFN), were evaluated using nested validation. TabPFN achieved superior generalization performance, yielding a mean test R2 of 0.778 ± 0.031, an RMSE of 0.264 ± 0.024, and an MAE of 0.203 ± 0.022, significantly outperforming tree-ensemble models (p < 0.01). Across growth stages, retrieval accuracy peaked at the expanded bell-mouth stage (R2 = 0.802) and successfully captured the unimodal trajectory of canopy development. SHAP-based attribution showed that spectral indices contributed most to the predictions (55.9%), followed by canopy texture (26.3%), with the spatial heterogeneity metric Tex_Entropy being the most influential single feature (22.8%). When embedded as the nuisance estimator within a Double Machine Learning framework for causal inference, TabPFN confirmed that, relative to the TEM subgroup, only the DH genetic background exerted a consistent and significant negative causal effect on LAI (ATE = −0.070, p = 0.030). These results establish TabPFN as a reliable and extensible tool for non-invasive, high-throughput phenotyping in precision breeding.
Accurate measurement of carbon is stored in plants is essential for evaluating strategies to reduce greenhouse gas emissions. The macauba palm (Acrocomia aculeata) is a native species of South America with high potential for oil production, ecological restoration, and carbon sequestration. Traditional methods to estimate carbon stocks, such as cutting down trees and performing laboratory analysis, are destructive, expensive, and impractical on a large scale. This study tested an alternative approach that combines UAV imagery, computer vision, and artificial intelligence to estimate carbon in macauba palms without damaging the plants. High-resolution aerial images were collected at four sites in Brazil, and a deep learning model (YOLOv8s-seg) was trained to automatically detect and measure palm crowns, achieving a mean Average Precision (mAP@50) of 0.956, precision of 0.946, and recall of 0.944. Plant height was estimated from canopy height models derived from photogrammetric processing of the UAV imagery. Crown diameter and height estimates showed strong correlations with field measurements (r = 0.81 and r = 0.82, respectively), with mean absolute errors of 0.26 m for diameter and 0.61 m for height. Both parameters were used in allometric equations to calculate carbon stock. Carbon estimates ranged from less than 1 ton per hectare in young plantations to more than 190 tonnes per hectare in older stands. These results demonstrate that artificial intelligence and UAV imagery provide a fast and scalable approach for estimating structural variables of macauba palms. However, carbon stock estimates remain sensitive to the allometric equations used and should be interpreted with caution, particularly at the individual level. Therefore, the proposed approach should be understood as a non-destructive monitoring framework rather than a fully generalized carbon prediction model, while still offering a valuable tool to support sustainable agriculture, carbon markets, and land-use policies.
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance matcharXiv · OpenAlex · checked 11 Sept 2026
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale. This paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera. A YOLO26n instance segmentation model locates cucumbers, and SAM (ViT-B backbone) refines each detection to a pixel-precise mask. Five methods are evaluated under matched conditions: (M1) a dominant-axis skeleton scan-line baseline; (M2) PCA on the bounding-box depth point cloud; (M3) SAM mask with medial-axis skeletonisation; (M4) a hybrid keypoint-guided approach using a YOLO26-pose model predicting five anatomical landmarks (KP0--KP4) with piecewise 3D arc-length; and (M5) a novel medial arc spline method fitting a cubic spline through the 3D medial axis of the SAM mask and computing arc length by trapezoidal integration -- the first such application to elongated vegetable measurement. All methods share five-frame burst depth averaging, colour-stream intrinsic alignment, and adaptive method selection with cascading fallbacks ensuring 100% coverage. A benchmark of 48 captures across seven cucumbers in three size categories (small ~8 cm, medium ~13 cm, large ~25 cm) with thread-based ground truth establishes a significant accuracy hierarchy: M1 (MAPE 9.68%) > M2 (5.31%) > M4 (5.51%) > M3 (5.82%) > M5 (4.13%). M5 significantly outperforms all competitors at Bonferroni-corrected alpha=0.0125. A secondary contribution is identifying a 12--18% length underestimation caused by using depth-stream rather than colour-stream intrinsics after rs.align(rs.stream.color) -- an under-reported error source. The complete system is released open source and runs in real time on a single consumer-grade GPU.
The objective of this study is to propose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees, while simultaneously reducing the cost of collecting this data. Firstly, a low-cost information acquisition platform is constructed for the purpose of shooting a multi-view video around a fruit tree. The video is then extracted and framed using a motion recovery structural algorithm, thereby obtaining a multi-view image sequence of the fruit tree with positional data. Secondly, the the image sequence is employed to train the neural radiation field, thereby obtaining a converged three-dimensional scene of the fruit tree. Ultimately, the three-dimensional scene is derived in the form of a point cloud, thus yielding a high-phenotypic detail point cloud model of the fruit tree. A multi-period point cloud model of an apple tree in an orchard environment, encompassing the flowering, fruiting, and dormant periods, was reconstructed using the aforementioned method. The experimental results demonstrated that the error associated with the tree shape data recorded by the multi-period point cloud model established by the aforementioned method was, on average, below 5%. Furthermore, the average error across all periods was 2.69%, representing a 75.50% reduction compared to traditional reconstruction methods. The dimensional accuracy of the point cloud model at the organ scale can reach the millimetre level, with an average error of 3.10% for fruit diameter, which is 66.19% lower than that of the traditional reconstruction method. The reconstruction method is robust to all periods of fruit trees and can meet the majority of cases of digital fruit tree reconstruction.
Stomatal trait analysis is essential for optimizing crop photosynthesis and transpiration, yet deep learning studies have focused mainly on monocotyledons, leaving dicotyledonous crops such as melon (Cucumis melo L.) understudied. To bridge this gap, we established a dedicated melon stomatal dataset comprising 5,708 training images, 1,631 validation images, and 815 test images. On this basis, we developed an improved Mask R-CNN framework using Vision Transformer (ViT) as the backbone. Specifically, standard Multi-Head Attention (MHA) was replaced with Dynamically Composable Multi-Head Attention (DCMHA), which enhances information exchange across attention heads and alleviates the low-rank limitation of conventional attention. In addition, a modified effective Squeeze-and-Excitation (eSE) module was incorporated into the Feature Pyramid Network (FPN) to strengthen channel dependency modeling and multi-scale feature representation. On the melon dataset, the proposed model achieved a mean average precision (mAP) of 72.40 ± 0.09%, with AP50 and AP75 of 91.93 ± 0.14% and 84.59 ± 0.19%, respectively. Repeated-run statistical analyses showed that eSE significantly and consistently improved the main detection metrics across backbones, whereas DCMHA provided a more moderate gain within the ViT-based setting, with clearer support for AP50 than for mAP or AP75 under the baseline FPN setting. Overall, the combined configuration remained among the top-performing models for stomatal instance segmentation. Ellipse fitting further enabled automated quantification of stomatal length, width, count, area, and circumference, showing strong agreement with manual measurements (Pearson r = 0.978). The model also showed preliminary transferability to cucumber, watermelon, pumpkin, and loofah, with an average species-specific R² of 0.86, although each species was evaluated on a limited sample set.
Reproduction assets foundThe paper's authors explicitly state that the source code for model training and inference (including the key modules: DCMHA, eSE-enhanced FPN, Mask R-CNN/ViT pipeline) is publicly available at a GitHub repository, which matches an allowed URL. The melon stomatal image dataset (8,154 images) is described in detail but,Code · publicCode Availability
The source code for model training and inference, including the implementation of the
key modules, is publicly available at:
https://github.com/huangyao110/qk_maskrcnn_trsv2.gitOpen asset ↗huangyao110/qk_maskrcnn_trsv2pdf-page:22 lines:1-322Plant phenotyping relevance matchCrossref · checked 5 Sept 2026
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Yurong Guo · Yue Huang · Chenxu Zhu · Luxu Tian · Jingxuan Zhang · Yimeng Li · Liu Y · Hongbin Wu · Xiuqing Fu
MaizeGrowth chamberRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.
Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.
An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and cross-sectional area profile. New shape descriptors based on cross-sectional area profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Reproduction assets foundThe paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
Luxu Tian · Min Kang · Mohamed Ahmed Moustafa · Hongbin Wu · Xiuqing Fu
TomatoRootMorphology / geometry measurementSegmentationRoot system architectureStress response / tolerance
Automated phenotyping of crops is a vital component of precision agriculture. As a representative economic crop, the radicle length of tomato seeds is a key phenotypic indicator for assessing seed vigor and seedling health. To reduce labor costs and improve measurement efficiency, we developed an integrated seed germination phenotype acquisition system that combines cultivation and imaging, enabling continuous image acquisition throughout the tomato seed germination process. To achieve automatic measurement of radicle length, we propose a deep learning-based segmentation framework, termed RootNet. The framework incorporates a custom-designed module, MambaNextBlock (MNB), within skip connections to enhance long-range feature modelling, and integrates an Atrous Spatial Pyramid Pooling (ASPP) module at the bottleneck to capture multi-scale contextual information. Post-segmentation, Canny edge detection is employed to extract radicle contours, from which actual lengths are computed based on contour arc length. Experimental results show that RootNet achieved 77.75% Intersection over Union (IOU), 86.03% Precision, 88.72% Recall, and 87.46% F1-Score on the root class. Compared with manual measurements conducted using ImageJ, our method showed high agreement across 1170 radicle measurements, with an R² of 0.9785, an MAE of 0.274 mm, an RMSE of 0.344 mm, and a Bias of −0.008 mm. Bland–Altman analysis further confirmed the absence of systematic bias, with 95% limits of agreement ranging from −0.682 mm to +0.666 mm. Meanwhile, measurement efficiency was improved by approximately 680-fold. Furthermore, the method was applied to evaluate the effects of drought, salinity stress, and different concentrations of Streptomyces albidoflavus (HL4) and Streptomyces virginiae (GZ2) on radicle growth. The results indicated that drought stress, salinity stress, and undiluted HL4 inhibited radicle elongation, whereas diluted HL4, as well as both undiluted and diluted GZ2, significantly promoted radicle growth. This study provides an efficient and cost-effective solution for non-destructive crop phenotyping and intelligent agricultural management in precision farming.
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruction methods require extensive species-specific training data and lack editability for hypothesis-driven research. Procedural modeling offers parametric control and large model variability but demands specialized expertise in geometric modeling and an in-depth understanding of complex procedural rules, making it inaccessible to domain scientists. We present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models through iterative natural language Plant Refinements (PR) during template creation, minimizing the need for programming expertise. Our co-design workflow leverages LLM-assisted code generation to progressively refine Python scripts that generate parameterized complex plant geometries as Non-Uniform Rational B-Spline (NURBS) surface representations, with botanical constraints. Plant organs are represented as spline surfaces that can be easily tessellated into polygonal meshes with arbitrary precision, ensuring compatibility with functional structural plant analysis workflows such as light simulation, computational fluid dynamics, and finite element analysis. We demonstrate the framework by generating procedural models of multiple maize genotypes, soybean (which shares the procedural generator with mung bean), and mung bean, with one plant tracked across different developmental stages. We fit procedural models to empirical LiDAR and NeRF-derived point cloud data through manual refinement of the Plant Descriptor (PD), a human-readable YAML file originally templated by the LLM, obtaining consistently low mean symmetric Chamfer distances that indicate close agreement between generated models and measured plant geometry. The pipeline generates dual outputs: triangular meshes (represented as STL or OBJ files) for visualization and triangular meshes with additional parametric metadata for quantitative analysis (stored as SMESH files). We further illustrate analysis-ready use by coupling the procedurally generated models to the HELIOS framework to simulate diurnal photosynthetically active radiation interception in virtual maize and mung bean fields across growth stages. Our framework uniquely combines pre-trained LLM-assisted template creation, mathematically continuous representations that support both phenotyping and rendering, and direct parametric control through the PD. The framework makes sophisticated geometric modeling accessible to plant science researchers while maintaining mathematical rigor through biologically interpretable parameterizations; additionally, the iterative PR dialogue produces an explicit record of model properties that is typically absent in conventional procedural modeling pipelines.
Determining the drivers of ecological stability amid accelerating global environmental change is a critical goal of contemporary ecology. Various candidate drivers have been suggested, with recent attention turning to response diversity—the variation among organism-environment responses. However, despite conceptual interest in response diversity as a driver of stability, there remain few field tests of this relationship. Using multi-species competitive communities of floating aquatic macrophytes as an experimental model for measuring temporal stability and response diversity to nutrient loading, we show that response diversity does not promote temporal stability of total macrophyte cover, but that communities with an uneven distribution of species responses were more resistant to an exogenous shock. To quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology. We measured response diversity as the balance of positive and negative biomass growth responses to dissolved nitrate concentration, weighted by species’ relative contributions to biomass, and tested its effect on temporal stability and resistance to an unexpected pulse disturbance (a large typhoon that disrupted our outdoor mesocosms). Response imbalance predicted typhoon resistance, but species asynchrony and mean population stability best predicted community stability, with no direct or indirect effect of species responses. Overall, our results provide new experimental evidence for how the structure of species responses promotes stability, and we aim our LeafMosaic workflow to empower future field experiments using floating macrophytes to study response diversity and ecological stability.
Main conclusion A novel and efficient method was developed to accurately measure thickness in 3D of many cells from a confocal stack, as well as to track changes in cell thickness overtime. Plant cells and organs are three-dimensional objects with a certain thickness. Among basic geometric parameters (length, width, depth/thickness), cell thickness is less accurately and comprehensively measured, probably because it cannot be directly seen. The current methods of cell thickness quantification have some limitations, such as measuring only from a cross-section, not accounting for the directionality of biological thickness, or not offering a way to track changes in thickness of individual cells over time. This research is an attempt to bridge the gap, by making the quantification of thickness and tracking its changes in many cells easier and more accurate. We devised a novel method to efficiently measure average cell thickness in 3D from cells imaged with confocal microscopy, the most popular technique to image live samples over many days. The method, in combination with the popular software MorphoGraphX, also allows accurate and efficient tracking of changes in thickness between different time points. We tested the method on various organs of the model plant Arabidopsis thaliana such as the shoot apical meristem, the hypocotyl, the cotyledon, and the sepal. We demonstrated that this new method can reliably measure the thickness of hundreds of cells at once in a short amount of time to reveal new biological insights. We believe this would be a useful tool for plant researchers to accurately characterize this hidden morphological dimension.
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Tan-Hanh Pham · Yubin Yang · Jing Zhang · Sanai Li · Stanley Omar PB. Samonte · Fugen Dou · Darlene L. Sanchez · Lloyd T. Wilson · Tanumoy Bera · Jing Wang · Kim‐Doang Nguyen
Abstract Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.
In this study, the diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques. The research was conducted in southwestern Türkiye within the brutian pine stands managed by the Isparta Regional Directorate of Forestry. A total of 175 trees were measured for height and diameter at breast height (d1.3), and these measurements used to estimate volume. The accuracy of these estimations predictions was examined, with volume estimation values serving as dependent variables in various ML algorithms. The performance of nine ML algorithms - AdaBoost Regression, Artificial Neural Network, Deep Neural Network, Decision Tree Regression, Gradient Boosting Regression, Linear Regression, Random Forest Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression - were compared. The results indicated that using only the diameter values (max. correlation 0.984) produced better results than using only the height values (max. correlation 0.932), while combining diameter and height variables (max. correlation 0.987) produced the most accurate results. Among the all algorithms, Random Forest Regression achieved the highest average correlation (0.968), whereas Decision Tree Regression had the lowest (0.906). All algorithms produced correlations exceeding 0.90. These findings demonstrate that ML models can effectively estimate stem volume from UAV-derived diameter and height data under field conditions similar to those in southwestern Türkiye. The integration of remote sensing and ML may therefore offer a viable approach for stem volume estimation in structurally comparable forest environments.
Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture under Mediterranean conditions. Experiments were conducted in Sicily, Italy, on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV photogrammetric data were used to generate canopy models and estimate canopy height, canopy volume, and vegetation density distribution. A voxel-based approach was applied to LiDAR-derived point clouds to quantify internal canopy structure and vegetation density within the canopy volume. Accuracy was assessed by comparing remote sensing-derived canopy metrics with ground-truth field measurements. LiDAR outperformed UAV photogrammetry in canopy height estimation, achieving lower RMSE values than UAV-derived models (0.19–0.21 m vs. 0.52–0.60 m), corresponding to an approximate error reduction of 60–65%. LiDAR also provided more accurate canopy volume estimation, with lower relative errors than UAV photogrammetry (3.5–4.2% vs. 13.7–16.1%). The voxel-based LiDAR approach enabled the quantification of vegetation density distribution within the canopy volume, showing higher sensitivity to internal canopy layers compared with UAV photogrammetry, particularly in the structurally complex Ficus macrophylla canopy. UAV photogrammetry provided reliable estimates of the external canopy surface but underestimated structural parameters in dense vegetation due to canopy occlusion and limited penetration into inner canopy layers. Differences between the two methods were more pronounced in Ficus macrophylla than in Moringa oleifera, confirming the strong influence of canopy complexity on sensing performance. These findings demonstrate that LiDAR-derived structural and voxel-based metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, yield prediction, and canopy management in Mediterranean cropping systems.
This paper develops an external-field optimization framework for logarithmic coefficients of multiplier-defined univalent functions and establishes optimality principles with applications to geometric image analysis. Using the Herglotz representation, logarithmic coefficients are expressed as nonlinear moment functionals of probability measures on the unit circle, where multiplier coefficients act as an external field imposing admissibility constraints. A general Euler–Lagrange condition is derived, yielding a nonlinear equilibrium equation that characterizes extremal solutions. When the external-field potential admits a unique maximizer, the extremal measure collapses to a one-point distribution, leading to explicit optimal special-function solutions. The theoretical framework is applied to a dataset of citrus lesion images. After segmentation and conformal normalization of lesion regions, approximate logarithmic coefficients are computed from boundary harmonic expansions. A distortion index and harmonic separation criterion are introduced, and a coefficient separation theorem is verified numerically, demonstrating that geometric differences in lesion morphology correspond to measurable differences in logarithmic coefficient distributions. The results provide a mathematically rigorous connection between nonlinear external-field optimization, conformal special-function representations, and shape-based image descriptors. This approach offers a theoretically grounded and conformally invariant methodology for analyzing geometric irregularity in biomedical and agricultural imaging.
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Plant phenotyping relevance matchbioRxiv · checked 11 Sept 2026
Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGRh and RGRv) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H2) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H2 differed substantially among traits. Canopy volume (H2 = 0.64) and canopy height (H2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.
Estimating the age and growth of long-lived desert succulents is challenging due to the absence of annual growth rings. This study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades in an iconic “Boojum tree” ( Fouquieria columnaris ) and an adjacent columnar cactus ( Pachycereus pringlei ) from a relictual population in Sonora, Mexico. Results indicate that F. columnaris exhibited a slow mean vertical growth rate of 1.71 cm yr −1 during the 1965-2016 interval which increased to 3 cm yr −1 for the recent 2016-2025 period. This acceleration aligned with values recorded at more favorable sites (i.e. 3.3 to 3.6 cm yr −1 ). Conversely, P. pringlei exhibited a minimal growth rate (1.01 cm yr −1 ), which is much lower than rates reported from repeat photography (5-10 cm yr −1 ) or radiocarbon dating of spines (3-23 cm yr −1 ). Together, these species demonstrate high ecological resilience near their physiological limits in the Sonoran Desert through divergent morphofunctional pathways. F. columnaris favors architectural stability and sustained growth, whereas P. pringlei relies on mechanical robustness and structural redundancy. These findings highlight the efficacy of SIP and historical records for long-term demographic monitoring in extreme arid environments.
The nucleus is the characteristic organelle of eukaryotic organisms. Unlike the classic textbook view of static nuclei, nuclear shape is dynamic in live cells. Altered or deformed nuclear shape is a hallmark of cancer in animal cells and environmental stress in plants. Nuclear envelope proteins interact with chromatin to regulate gene expression. Unfortunately, little is known about the impact of abiotic stress on nuclear shape, movement, and chromatin dynamics. To confront this issue, we developed a pipeline using confocal microscopy and particle tracking software to quantify nuclear and chromatin dynamics in Arabidopsis roots under control and abiotic stress condition. This confocal imaging method utilizes a dual fluorescently tagged marker line - nuclear envelope protein and chromatin - to perform live cell imaging of the root in model plant Arabidopsis thaliana under control and salt-stressed conditions. These captured movies are analyzed to quantify nuclear and chromatin dynamics using open-source image processing software Fiji/ImageJ with the help of the TrackMate plugin. To validate this method, we imaged and quantified chromatin movement in control and salt-stressed roots, revealing a decrease in chromatin speed under salt-stressed conditions. This method allows for quantitative live cell imaging of root nuclear shape and chromatin dynamics during plant development and environmental stress, thus enabling analysis of changes in nuclear and chromatin dynamics caused by abiotic stressors.
Cold hardiness is a critical trait for grapevine survival and productivity in cold climates. This study examined the relationships among cane morphological characteristics, shoot color parameters, and cold hardiness in two grapevine cultivars ('Prairie Star' and 'Frontenac') across four dormant-season sampling times (ST 1-ST 4) and three internode diameter classes (small, normal, and large). Morphological traits, including internode length, shoot diameter, and cross-sectional area, did not show a consistent temporal trend across sampling periods, suggesting that the observed variation was primarily associated with sampling time and cane class rather than progressive structural change during dormancy. In contrast, colorimetric traits showed a clear seasonal pattern, with shoots becoming darker and redder from ST 1 to ST 4, consistent with advancing lignification and cane maturation. Cold hardiness, assessed using low-temperature exotherms of bud, phloem, and xylem tissues, increased substantially from early to mid-dormancy, with xylem tissues reaching the greatest freezing tolerance by ST 3-ST 4. 'Prairie Star' showed slightly greater xylem cold hardiness than 'Frontenac', while bud survival remained consistently high across all treatments. Strong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status. Sampling time was the primary source of multivariate variation, with cultivar and internode class contributing secondary effects. These findings demonstrate that observable cane traits, especially shoot color, reflect the progression of seasonal cold acclimation and may support the evaluation and selection of cold-hardy grapevine germplasm.
Mallikarjuna Rao Kovi · Antonio Candea Leite · Morten Lillemo
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Plant phenotyping relevance matchOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Automated three-dimensional reconstruction of plant architecture under- pins high-throughput crop phenotyping, yet its deployment in practical field settings is constrained by two fundamental limitations of Neural Radiance Fields (NeRF): degraded geometry under sparse, unstructured image capture, and a complete absence of calibrated uncertainty estimates that would allow practitioners to distinguish reliable geometry from reconstruction artefacts. We present UA-PlantNeRF, a unified framework that resolves both limita- tions through three tightly coupled contributions. First, building on VISAR, our prior intelligent video frame-selection strategy (weights α1 = 0.4, α2 = 0.3, α3 = 0.3), the pipeline identifies maximally informative, non-redundant view- points from raw footage with as few as 15 frames. Second, a dual-head NeRF architecture augmented with Monte Carlo Dropout produces jointly decom- posed aleatoric and epistemic uncertainty alongside each reconstructed voxel, trained under a heteroscedastic negative log-likelihood objective. Third, split conformal prediction—with calibration performed on held-out plants to preserve exchangeability— yields provable, distribution-free per-ray cov- erage guarantees at any user-specified confidence level. Evaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset) at sparsity levels N ∈ {15, 30, 50}, UA-PlantNeRF achieves PSNR 28.7±0.6 dB and SSIM 0.89±0.01 at N = 30, outperforming all sparse-view baselines (p
Quantitative disease resistance in plants emerges from complex interactions between host tissues and pathogen growth dynamics, producing a spectrum of phenotypic responses. In plant-fungal interactions, disease is most visibly expressed through lesions that vary in number, size, shape, and color, collectively defining a lesion profile. For Cochliobolus heterostrophus, a fungus causing Southern Corn Leaf Blight of maize (Zea mays ssp. mays), we show that infection on different maize genotypes produces strikingly different lesion profiles. However, it remains unclear whether such macroscopic variation in lesion profiles corresponds to consistent differences in the three-dimensional organization of pathogen colonization within host tissue. We therefore examined variation in the three-dimensional structure of C. heterostrophus-infection networks across host genotypes representing four lesion-profile classes. Using light-sheet microscopy and filament-tracing methods adapted from neuroscience, we developed quantitative metrics to characterize infection network organization, including depth, density, shape, and spatial association with host vascular tissue. In this dataset, network depth was similar across genotypes, whereas network morphology (shape and density), spatial association with vascular bundles, hyphal segment length, and branching frequency varied. Notably, genotypes with similar quantitative resistance levels sometimes exhibited distinct patterns of fungal colonization, suggesting that comparable resistance can arise from different underlying infection dynamics. These findings indicate that lesion profiles may not uniquely predict infection network structure and highlight the utility of three-dimensional network metrics for describing variation that likely reflects multiple underlying host and pathogen processes. This multi-scale framework provides tools for linking macroscopic disease phenotypes with microscopic infection processes in quantitative disease resistance.
Reproduction assets foundThe paper's Data availability statement explicitly deposits metadata, data, and computer code as Supplementary Files accompanying the open-access publication (Supplementary Materials 1-5, including XLSX datasets and an untyped Supplementary Material 5 likely holding code). These are paper-specific phenotyping assets (eCode · publicMetadata, data, and computer code from this study are available in Supplementary Files included with the publication.Open asset ↗lines:147-204Plant phenotyping relevance matchCrossref · checked 13 Sept 2026
The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits.
Accurate mapping of forest canopy height is fundamental to modern forestry, providing essential structural data for biomass estimation and monitoring forest health. This study evaluates the broad usability of global (25 m) and high-resolution (1 m) Canopy Height Models (CHMs) by comparing them against temporally aligned Airborne Laser Scanning (ALS) reference layers from 2018 and 2024. At the 25 m scale, we evaluated four global products: Global Forest Canopy Height (GFCH), Global Map of Tree Canopy Height (GMTCH), High-Resolution Canopy Height model of Earth (HRCH), and Europe Temporal Canopy Height (EUCH). These satellite-derived models exhibit significant height-dependent limitations, systematically underestimating mature forest canopies (>30 m) by more than 15 m due to signal saturation, though EUCH and GMTCH performed moderately better. Transitioning to 1 m high-resolution data revealed a dramatic recovery in structural fidelity. A photogrammetrically derived model (PALS) achieved an RMSE of 4.89 m and a Mean Error (ME) of 1.86 m, demonstrating remarkable vertical stability across complex topography, even on slopes >25°. While coniferous stands produced higher absolute errors (RMSE = 6.75 m) than deciduous stands (RMSE = 6.19 m) due to spire-like architectures, PALS effectively captured fine-scale canopy textures. Experimental deep learning architectures, specifically the ArcGIS Living Atlas model, showed promise with an RMSE of 8.90 m, though out-of-the-box implementations struggle without local calibration. For forest disturbance monitoring, a distinct performance trade-off emerged. High-resolution photogrammetry (PALS) provided the highest overall precision for identifying clear-cuts (F1 = 0.353) but was conservative, capturing only 51% of the reference area. In contrast, the global HRCH model captured the total spatial footprint (103.9% of area) despite its geometric inaccuracies. The Living Atlas deep learning model offered the most balanced sensitivity, detecting 118.6% of the area with a competitive F1 score of 0.326. Ultimately, digital aerial photogrammetry provides a cost-effective solution for frequent operational updates, such as the two-year national mapping cycle in the Czech Republic.
Hybrid wheat breeding offers a promising route to enhance grain yield and yield stability through heterosis, yet hybrid grain production remains constrained by limited cross-pollination efficiency due to high rates of autogamy. To achieve cross-pollination in an autogamous species like wheat, pollen must shed outside the floret. This is typically assessed by scoring visual anther extrusion (VAEX), a key floral trait that sets the foundation for cross-pollination. However, VAEX explains only part of the variation in hybrid grain set. To address this, we analyzed floral structures and reproductive processes underlying cross-pollination efficiency in wheat. From 24 elite winter wheat genotypes, we developed traits describing anther extrusion kinetics, pollen release, and floral bract architecture. These traits showed substantial genotypic variation and high heritability. While VAEX alone explained approximately 49% of the variation in hybrid grain set, combined trait analyses explained up to 77%, demonstrating that hybrid grain production is governed by coordinated floral and reproductive trait interactions. Together, our analyses define a hierarchical trait architecture linking floral bract mechanics, anther extrusion dynamics, and pollen shedding to cross-fertilization success. This establishes a systems-level phenotyping framework for improving male parent selection in hybrid wheat breeding. Highlight High cross-pollination efficiency in wheat is a multi-factorial process that requires lighter floral bract architecture combined with adequate anther extrusion and pollen release for improving hybrid grain production.
The agricultural sector is the most important asset in the country, which boosts development by reducing unemployment, food shortages, poverty, and unstable economic conditions. The recognition and classification of agricultural imagery are the fundamental requisites of contemporary farming methodologies. Thus, it helps to accurately identify the enumeration of plant growth, plant diseases and so on, leading to an increase in crop yield production. There are numerous methods for classifying crop images; however, the conventional methods struggle with issues like noise, distortion, and image quality. It is not capable of fully exploiting the rich potential of image characteristics due to the weather conditions and shooting angles. It does not have the ability to significantly extract the relevant information in the training process and enhance the negative classified outcomes. Multiple learning-based methods have been created to gain more accurate and trustworthy information from image classification. In order to overcome these challenges, a novel crop image classification model is implemented in this research work for classifying the crop images to improve yield production. The required high-quality crop images are collected from the standard datasets. These images are further fed into the feature extraction phase, the Visual Geometry Group 16 (VGG16), graph convolutional neural network (GCNN), and residual network (ResNet) models are utilized to extract the relevant primary information in the collected crop images for generating ensemble convolution features. Subsequently, the resultant features are fed into the classification process, where the multi-scale and dilated adaptive recurrent neural network (MDARNN) mechanism is utilized to effectively classify the crops. In this process, a random function improved dark forest algorithm (RFIDFA) is employed for tuning the RNN parameters, thus improving the classification process. Finally, the validation of the designed approach is performed using several performance measures and compared with the previous works to ensure the designed model’s superior performance. The designed method achieves the best outcome of 95.19% precision, 95.14% NPV, and 95.15% accuracy measures. The developed crop image classification method can continuously monitor the crop growth information to improve yield production. Also, it helps to optimally detect the disease and pest-affected crops in an earlier stage to reduce crop losses, allowing for proper treatment and preventing widespread infection. It is possible to determine the water needs of various crops to ensure efficient crop production and precise farming practices.
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Juan Pedro Carbonell Rivera · Jesús Torralba · Pablo Crespo-Peremarch · James McGlade · Marina Simó-Martí · Luis Ruiz Fernandez · Iyán Teijido-Murias
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Accurate characterization of tree stem geometry is essential for forest inventories, yet conventional field measurements of diameter at breast height (DBH) are limited to a single cross-section and do not capture vertical variability along the trunk. This study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements. Data were acquired simultaneously within a 2.5 m radial plot. DBH was estimated through RANSAC-based circular fitting, and stem sections were extracted every 20 cm to assess diameter stability along the trunk. All techniques produced similar mean DBH values closely matching field measurements (23 cm), with MLS achieving the lowest RMSE (1.29 cm), followed by SfM (1.52 cm), GS (1.60 cm), and iPad-LiDAR (2.26 cm). However, marked differences were observed in vertical completeness. MLS captured the full vertical profile of the stems, reaching 14.11 m, whereas SfM and GS from iPhone, and iPad-LiDAR were limited to approximately 6 m or less. The results indicate that although low-cost image-based approaches can provide accurate DBH estimates under controlled conditions, MLS remains the most robust solution for comprehensive vertical stem characterization.
Accurate and standardized phenotyping of complex, environmentally sensitive quantitative traits remains a major bottleneck for reliable locus discovery and breeding applications. Here, we established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber. The workflow was applied to a permanent recombinant inbred line (RIL) population (n = 211) evaluated across two seasons (2023-2024), from which nine traits were extracted from 3D models. All 211 RILs were whole-genome resequenced to generate genome-wide SNPs, enabling construction of a high-density linkage map and subsequent QTL mapping, complemented by GWAS for physical anchoring of association signals. Using this integrated design, we identified 29 QTLs across the nine traits and resolved cross-season major-effect loci with consistent genetic signals. Notably, two cross-season loci were detected as novel: FL4.1/FSL4.1 affecting fruit length and fruit stalk length, and NLB1.1/LLB1.1 affecting branching. GWAS further anchored lead variants to physical coordinates and supported cross-season associations. Together, these results demonstrate that standardized 3D phenotyping provides a reproducible and interoperable trait definition framework that supports cross-season locus discovery and downstream marker development for quantitative genetic dissection in cucumber.
Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods.
High-quality, real-time three-dimensional (3D) reconstruction and phenotyping of field crops are essential for advancing digital farm management. However, challenges such as diverse crop postures and fluctuating illumination in open-field environments significantly hinder reconstruction accuracy. To address these issues, this study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS), designed to enable low-cost, high-fidelity reconstruction and phenotypic analysis of agricultural fields, with chili pepper plants as an example. Specifically, a dataset encompassing three distinct growth stages of chili pepper plants was collected and preprocessed using a phenotyping robot (PR) equipped with oblique-view imaging. The AgriGaussian method integrates three core components: (1) a Depth-Supervised Strategy (DSS) for adaptively enhancing phenotypic features based on gradient variations; (2) a Crop Appearance Extraction (CAE) module that suppresses the effects of illumination and environmental interference while preserving fine phenotypic details; and (3) a Multi-scale Chunking Training (MCT) strategy that enables scalable reconstruction of large agricultural scenes. Experimental results demonstrate that AgriGaussian achieves high-fidelity reconstruction, with peak signal-to-noise ratio (PSNR) of 26.85 dB, structural similarity index (SSIM) of 0.86, and learned perceptual image patch similarity (LPIPS) of 0.24—surpassing existing baseline algorithms. Compared to manual measurements, the reconstructed canopy height and volume exhibit strong agreement, with coefficients of determination (R2) reaching 0.90 and 0.86, respectively, and average absolute percentage errors as low as 5.1%. Furthermore, the method accurately reconstructs fine-grained features such as foliar lesions and surface textures. In summary, AgriGaussian enables large-scale, high-fidelity reconstruction of field crop phenotypes and provides a promising foundation for advancing 3D phenotyping and digital twin technologies in precision agriculture.
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Plant phenotyping relevance matchCrossref · Europe PMC · checked 5 Sept 2026
Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers—across key crop genomic tasks, including gene expression prediction, alternative splicing analysis, cis ‐regulatory element identification, epigenomic profiling, and genome‐based trait prediction. In phenomics, these models facilitate high‐throughput extraction of crop phenotypic traits from multispectral, unmanned aerial vehicle, and ground‐based imagery, supporting yield forecasting, disease diagnosis, and stress response monitoring. We critically evaluate the performance and limitations of each model type across tasks, considering trade‐offs between complexity, accuracy, and interpretability, to offer practical guidance for crop researchers. Additionally, the review addresses major challenges in deploying deep learning—such as data scarcity, model transparency, and computational demands—and proposes future pathways to enhance model generalizability, multimodal data integration, and applications in intelligent breeding and sustainable agriculture.
Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract
Ear density ( ) and ear surface area in cereals are important traits for adaptation to low inputs and climate change. Here we propose a high-throughput field phenotyping method to estimate these traits using nadir and 45° RGB images acquired by the Phenomobile ground robot. First, the YOLOv5 ear detection algorithm is applied to nadir RGB images to estimate . Second, an ear segmentation algorithm is applied to nadir and 45° RGB images to compute the ear gap fraction at different viewing angles. The Beer-Lambert law is then inverted to compute the ear area index (EAI) from the observed ear gap fraction. is finally derived as the ratio between EAI and . We applied the methodology to a panel of 10 commercial bread wheat varieties how both traits vary across 12 environments. The relative error obtained for awnless varieties is 12% (56 ears m -2 ) for and 18% (1.3 cm 2 ) for . For awned varieties, ground-truth observations of were shown to be biased due to an overestimation of awns contribution, leading to an error of 41% (3.6 cm 2 ). was strongly correlated with grain dry mass per ear at harvest ( r 2 = 0.80 across genotypes and environments, r 2 per genotype ranged between 0.80 and 0.95) and was strongly correlated with grain yield ( r 2 = 0.83). These results indicate that both EAI and can be interesting non-destructive proxies for yield and grain dry mass per ear.
Reproduction assets foundThe authors publicly release their ear surface area estimation algorithm with an example dataset on an INRAE forge repository, and the Phenomobile-derived ear density/ear surface area estimations used in the multi-environment analysis are included as supplemental material with the open-access article. The YOLOv5 GWC_SoDataset · publicThe algorithm developed to estimate the EAI and the average ear surface using binary images from ear segmentation are publicly available in the repository https://forge.inrae.fr/raul.lopez-lozano/wheat-ear-surface , jointly with an example dataset from the Mauguio 2023 trial (4 treatments, 1 replicate). The Phenomobile estimations of ear surface area and ear density used in the multi-environmental mixed model presented in Section 2.5 are included as supplemental material.Open asset ↗lines:614-652Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.
Reproduction assets foundThe article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code orDataset · publicDataset preparation
and the training process are detailed in Zhou et al. (2025a),
and a subset of the dataset has been publicly released on
Dryad (Zhou et al. 2025b).Open asset ↗Dryadpdf-raw-page:5 lines:1-55Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Plant phenotyping relevance matchCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.
Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel of 372 genotypes (2022-2023) using image-analysis-based phenotyping on 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r > 0.96 for pod length (PL) and seed-per-pod (SPP)). Four complementary genome-wide association studies models identified 65 significant SNPs (-log10(P) ≥ 5.56) associated with pod curvature, length, width, and SPP traits. A significant SNP (5_35265704) on chromosome 4 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Virad04G0076900, located 15.6 kb from this SNP, is part of the GH3 gene family and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 6, has been found to be significantly associated with both PL and SPP. A candidate gene, Virad06G0002400 (36.5 kb from this SNP), encodes a potassium transporter and shares homology with the Arabidopsis gene HAK5 (AT4G13420), known to influence pod growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, demonstrating comparable accuracy to manual methods for linear traits and up to 22% improvement for complex shape traits. These results highlight the potential of deep learning-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.
Reproduction assets foundThe paper's image-based pod phenotyping and genomic analysis scripts are explicitly stated to be publicly available on the authors' GitHub repository. Raw phenotypic data (image-based and manual measurements, BLUEs, BLUPs, GP results) are only in supplementary files without a direct public URL, so they are not listed; Code · publicAll analysis scripts used in this study are publicly available at GitHub: https://github.com/vboddepalli89/Image-based-pod-phenotyping .Open asset ↗https://github.com/vboddepalli89/Image-based-pod-phenotypinglines:395-459Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Tree height is a fundamental attribute in ecological research and commercial forestry, serving as a key indicator of site productivity. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras and Light Detection and Ranging (LiDAR) sensors offer a cost- and time-efficient alternative to traditional field-based, satellite, and manned aircraft methods for tree height measurement. The objectives of this study are: (1) to evaluate the effects of UAV flight speed and image overlap on data quality, mission efficiency, and processing requirements; (2) to compare the performance of Structure-from-Motion (SfM) photogrammetry and LiDAR in generating canopy height models (CHMs); and (3) to quantify the accuracy of UAV-derived tree height estimates relative to field measurements and identify optimal flight configurations. Three flight speeds (10, 15, and 20 mph) and four forward/side overlap levels (50%, 60%, 70%, and 80%) were tested, with UAV-derived height estimates validated against 920 field-measured trees. Both datasets were processed in ArcGIS Pro to produce canopy height models (CHMs), RGB imagery via Structure-from-Motion (SfM) photogrammetry, and LiDAR data from laser-scanned point clouds. Orthomosaic quality was assessed using tie point density, reprojection error, ground resolution, image georeferencing deviation, Global Positioning System (GPS) root mean squared error (RMSE), and block adjustment success, while LiDAR point cloud quality was evaluated using point density (pts/m 2 ) and height percentiles (P25, P50, P75, P95). Height estimation accuracy for both sensors was quantified using the coefficient of determination (R 2 ), RMSE, and Bias. Results indicate that image overlap exerted a stronger and more consistent influence than flight speed across all dimensions of mission efficiency, data volume, and processing time. Flight duration more than doubled and image counts increased sixfold when overlap increased from 50:50 to 80:80. Higher overlaps improved orthomosaic continuity, tie point density, reprojection accuracy, and CHM quality, though at the cost of longer processing times, greater storage demands, and increased computational requirements. LiDAR point density similarly increased with overlap, yielding smoother CHMs at ≥70% overlap, while height percentiles remained stable across configurations. In terms of accuracy, UAV imagery at 10 mph with 80:80 overlap achieved the best photogrammetric performance (R 2 ≈ 0.60, RMSE = 4.5 m, Bias = 4.3 m), though all imagery-derived estimates exhibited systematic height underestimation. LiDAR-derived heights were substantially more robust across all flight configurations, with the best performance at 10 mph and 80:80 overlap (R 2 ≈ 0.89, RMSE < 1.5 m, Bias < 0.5 m). These findings demonstrate that higher overlap, particularly at moderate flight speeds, substantially enhances data quality and tree height estimation accuracy, offering practical guidance for optimizing UAV-based forest inventory workflows.
To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R 2 of 0.835 and an NRMSE of 9.5%, whereas the DSM-based model showed lower performance (R 2 = 0.641; NRMSE = 14.2%). Under the same random-forest modeling framework, fusing VIs with DSM features improved the overall model performance to R 2 = 0.892 and NRMSE = 7.6%, indicating that DSM-derived structural information mainly enhanced the feature representation of maize LAI. Using the same VI-DSM feature set, the stacking model with support vector machine (SVM) as the meta-learner further improved the overall performance to R 2 = 0.930 and NRMSE = 6.3%. The additional gain from stacking was moderate but consistent, whereas feature fusion contributed the dominant improvement. The combined VI-DSM-Stacking workflow improved prediction stability across planting densities, especially under low- and high-density canopy conditions where soil background interference and spectral saturation were more evident. These results demonstrate that integrating spectral and DSM-derived structural information with stacking ensemble learning can improve the accuracy and robustness of UAV-based maize LAI estimation.
Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Plant phenotyping relevance matchCrossref · checked 14 Sept 2026
Published31 May 2026International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
Crop cultivation sustains the livelihoods of a substantial portion of households across developing economies, yet the crops on which those households depend are perpetually at risk from pathogenic infections that erode both yield volume and produce quality. Infected fields, when not addressed at the right time, translate into mounting financial strain that smallholder growers — who operate with limited financial reserves — are ill-equipped to withstand. The dominant method of spotting such infections today still relies on a farmer walking the field and judging leaf condition by eye, or waiting for an agronomist's visit — a workflow that is neither fast nor consistent enough for large-scale cultivation. Progress in deep learning has fundamentally changed what automated visual inspection can accomplish, and plant pathology diagnosis is one of the fields that has benefitted most visibly. Image-based pipelines can now scan a leaf photograph and return a disease classification in fractions of a second. Among the architectures driving this capability, Convolutional Neural Networks occupy a central role: their layered filter design allows them to extract and encode visually informative features — texture discontinuities, color anomalies, lesion geometry — without any manual specification of what to look for. We present a CNN-driven plant disease recognition system that operates on photographs of plant leaves and returns a disease label together with a confidence estimate. Our training corpus is the Plant Village benchmark collection, a large repository of annotated leaf images spanning healthy and diseased specimens across multiple crop varieties. The input pipeline applies spatial normalization, pixel rescaling, and augmentation strategies to condition the data before it reaches the network. A React.js browser interface connects end users to the model via a lightweight prediction API, enabling diagnosis without any specialist involvement. Validation results affirm that this deep learning approach surpasses rule-based image processing baselines on both accuracy and response time, and the system holds clear potential for adoption in early disease management program
Omar Farouk Fauzi · Muhammad Zulkarnain Abd Rahman · Alvin Lau Meng Shin · Wan Hazli Wan Kadir · Mohd Faisal Abdul Khanan · Mohd Radhie Mohd Salleh · Hamdan Omar · Muhammad Safwan Ruslan · Puven Raj P. Chazian · Mohd Asraff Asmadi · Muhammad Khalid
Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.
Hyperspectral imaging is an imaging technique that allows for acquisition of high-resolution spectral information beyond that of the visible spectrum. When applied to plants, it effectively enables non-invasive characterization of physiological status and has been widely used in agricultural settings. Marchantia is a model bryophyte species whose flat morphology and visually distinct stress-response phenotypes makes it an ideal candidate for imaging studies. Here, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing. This protocol features a streamlined data processing pipeline hosted on a web-based development platform that automates 1) the segmentation of plant area into spatially distinct regions for localized analysis of intra-specimen physiological gradients, and 2) classification of plant pixels based on their spectral signatures. All results are exported as structured CSV files for ease of further analysis as desired by the user.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicExample images used in this protocol have previously been published by Krishnamoorthi et al. (2024) 4 and can be downloaded from https://github.com/dr-daisuke-urano/PlantHyperspectralSVDOpen asset ↗PlantHyperspectralSVDlines:47-85Plant phenotyping relevance matchOpenAlex · checked 13 Sept 2026
Published29 May 2026ISPRS annals of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Unmanned Aerial Vehicles (UAVs) have become valuable tools for high-resolution ecological monitoring, particularly in complex environments such as mangrove forests. This study investigates the impact of flight altitude on the accuracy of tree height estimation in the Melgonze mangrove forest, in southern Iran. Two UAV flights were conducted at altitudes of 100 meters and 150 meters using a DJI Phantom 4 Pro, and photogrammetric processing was performed using Agisoft Metashape. A total of 16 mangrove trees were measured in the field to provide ground-truth reference data. Canopy height models (CHMs) were generated from both UAV datasets and compared to the field measurements. Preliminary results indicate that the 100-meter flight achieved higher accuracy, with a lower root mean square error (RMSE =21.2 cm), Mean Absolute Error (18.94 cm), and a higher coefficient of determination (R² = 0.97) compared to the 150-meter flight (43.3 cm, 35 cm, and 0.92, respectively). These findings underscore the significance of flight altitude in UAV-based assessments of forest structure and offer practical guidelines for optimizing data acquisition in future mangrove mapping applications.
While unmanned aircraft system (UAS)-based photogrammetry and light detection and ranging (LiDAR) are increasingly used for canopy height estimation in forestry and other orchard systems, their application to pecan orchards remains limited. Accurate measurements of tree height and canopy structure are essential in pecan production for assessing tree growth and health, and for supporting precision orchard management. This study provides one of the first systematic evaluations of UAS-based structure-from-motion (SfM) photogrammetry and UAS-mounted LiDAR for estimating pecan tree height. A rotary-wing UAS equipped with RGB and near-infrared (NIR) cameras collected imagery at 60 and 120 m aboveground over two pecan orchards containing 480 and 308 trees, and LiDAR data were acquired at 70 m. UAS imagery was processed to generate three-dimensional (3D) point clouds, digital surface models (DSMs), digital terrain models (DTMs), and orthomosaics. DTMs were derived using point cloud classification and DSM filtering, and tree heights were calculated relative to these terrain models using canopy height models (CHMs) and point cloud–based approaches. LiDAR data were processed to produce calibrated point clouds, DSMs, and DTMs, from which tree heights were extracted using comparable methods. Image-based tree heights showed strong agreement with manual measurements, with point cloud–derived high percentiles or maxima [ R 2 = 0.982–0.996; root mean square error (RMSE) = 14 to 25 cm] consistently outperforming CHM-based estimates across ground elevation methods, camera types, and flight altitudes. LiDAR-derived tree heights exhibited similarly high accuracy. Image-based and LiDAR-derived heights were strongly correlated across all trees at 120 m ( R 2 = 0.982–0.995; RMSE = 18–25 cm), confirming the reliability of SfM photogrammetry. However, incomplete canopy reconstruction in some 60 m datasets led to underestimation, highlighting the importance of sufficient image overlap for accurate 3D canopy modeling. These results demonstrate that UAS image-based point clouds can provide pecan tree heights comparable to LiDAR, offering a cost-effective approach for tree growth monitoring, orchard management, and precision agriculture applications.
UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the difficulty of growth monitoring and yield estimation. This study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping. Over three ramie growth cycles, a total of 15 UAV flights were conducted over an experimental field consisting of 72 plots. The structure from motion (SfM) algorithm was applied to estimate PH. Remote sensing features derived from UAV imagery were used with background segmentation and machine learning to estimate LAI. The AGB was estimated by combining remote sensing-derived PH, LAI, and climate data. The results showed that the estimated and measured phenotypes were highly correlated, with optimal coefficients of determination of 0.961 for PH and 0.873 for LAI. Background segmentation improved LAI accuracy. Integrating climate data, remote sensing-derived PH and LAI significantly enhanced the accuracy of AGB estimation. In conclusion, this study provides a feasible method for extracting ramie phenotypes from UAV remote sensing imagery, providing methodological support for large-scale management of the crop industry and intelligent, precise monitoring of crop growth.
Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen’s κ = 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP 50 and 81.8% mAP 50:95 on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component’s non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP 50 at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.
Abstract. Accurate monitoring of vegetation health and canopy structure is essential for optimizing agricultural productivity and managing natural resources. Remote sensing technologies, combined with artificial intelligence (AI) and advanced satellite data, have revolutionized the capacity to assess crop conditions at large scales with high temporal and spatial resolution. This study leverages Sentinel-2 multispectral imagery and a novel AI-driven model approach to estimate Leaf Area Index (LAI) across multiple fields for canola. By integrating spectral reflectance data with view and solar geometry parameters, the model effectively captures the complex interactions between canopy structure and environmental factors. The methodology employs a two-layer neural network calibrated with physically based normalization to translate Sentinel-2 spectral and angular inputs into accurate LAI estimates. Validation against observed field measurements demonstrates strong agreement, underscoring the model’s robustness and reliability. Spatial analysis reveals distinct LAI patterns among the crop types, highlighting differences in canopy density and growth dynamics. Temporal profiling further illustrates crop-specific development trends, with canola showing extended canopy expansion. The results confirm that the fusion of remote sensing data with AI modelling provides a powerful tool for precision agriculture, enabling detailed monitoring of crop growth and facilitating informed decision-making. This approach offers significant potential for enhancing yield prediction, resource management, and sustainable farming practices, ultimately supporting global food security efforts.
Main conclusion The progress of soybean phenotypic detection and intelligent sensing technologies has been reviewed under salt-alkali stress , and an integrated approach combining three-dimensional imaging with near-infrared spectroscopy has been proposed to construct full-spectrum three-dimensional images. The approach could provide a reference for the breeding of salt-alkali-tolerant soybean varieties and the optimization of cultivation practices. Soil saline-alkali is one of the major environmental factors limiting global agricultural development, posing a serious challenge to normal crop growth, resource use efficiency, and sustainable agricultural development. Soybeans are a vital oilseed crop and plant-based protein source, and their phenotypic traits are significantly affected by saline-alkali stress, severely limiting soybean grain yield and quality. With the rapid advancement of technologies such as intelligent sensing and big data, this progress has driven new developments in plant phenomics detection, offering fresh insights into germplasm resource evaluation, breeding, gene function, and the cultivation of salt-alkali stressed soybeans. This article introduces the impact of salinity-alkali stress on soybean "phenotype-environment-gene" information, reviews the technical progress and application fields of traditional phenotypic detection methods for obtaining various phenotypic indicators across crops, and focuses on a rapid detection method of soybean phenotype under salinity-alkali stress. This paper analyzes the current state of research on detecting phenotypic indicators of soybeans under saline-alkali stress using intelligent sensing methods, including near-infrared spectroscopy, image recognition, and three-dimensional imaging. It is anticipated that through the integration of three-dimensional imaging and near-infrared spectroscopy, forming "full-spectrum three-dimensional images" with spatial structure and spectral information, this approach will advance the breeding and cultivation of superior salt-alkali tolerant soybean varieties through "intelligent data-driven" methods.
A comprehensive description of plant phenotypes of certain taxa is an important task when describing genera and species, as well as when setting their natural taxonomies. The development of modern technologies of effective phenotyping makes it possible to obtain a large amount of data with a quantitative and/or qualitative description of various traits in plants, mainly based on the analysis of their digital images. The study compared the results of the F2 hybrids assessment - visually and using machine learning methods - of two endemic tetraploid (2n = 4x = 28) wheat species which are Ethiopian wheat (Triticum aethiopicum Jakubz.) and Kartalian or Dika wheat (T. carthlicum Nevski). In the latter case, it is proposed to use the method of a mixture of Gaussian (normal) distributions in plant morphometry in order to identify groups that differ in character values. Most taxonomically important (species-specific) traits are controlled oligogenically and have a clear phenotypic manifestation, so hybridological analysis was an indispensable and basic type of analysis for subsequent detailed phenotyping of wheat spikes using machine-learning methods. According to a number of criteria, the estimates of patterns of inheritance obtained by different methods coincide. Based on the conducted research, we can state that the trait "tetraaristatum" (the presence of awns on both flower and spike glumes) is species-specific (taxonomically important) for T. carthlicum and it can be effectively used for taxonomic purposes both in carrying out hybridological analysis and in experiments using machine learning. Such a species-specific character is the "character (type) of awnedness" for T. aethiopicum. Our study demonstrates that a combination of automatic phenotyping methods and a model of a mixture of Gaussian distributions can, in principle, lead to an automatic analysis of the allocation of classes in F2 hybrids. It allows, in turn, to detect the presence of genes associated with species-specific traits of wheat plants. Further, the improvement of the applied artificial intelligence (AI) algorithms is required.
Reproduction assets foundThe paper's supplementary materials (Supplementary Tables S1–S3 and Figure S1) contain the paper-specific phenotyping data: species-specific trait descriptions, the 19 spike morphometric characters per projection, and the Gaussian mixture model splitting results (means, variances, group sizes, χ² values). The full textSupplement · publicof these traits
are controlled by oligogenes and have a clear phenotypic
manifestation, the hybridological method was an indispensable
and primary type of analysis for subsequent detailed
phenotyping spikes of wheat species using machine learning
methods.
Supplementary Materials are available in the online version of the paper:
https://vavilov.elpub.ru/jour/manager/files/Suppl_Kruch_Engl_30_3.pdf
Plant material. The object of study was interspecific
hybrids obtained by crossing two endemic tetraploid
wheat species ♀T. aethiopicum Jakubz. (k-19301/2) with
♂T. carthlicum
Nevski (k-32496). The experiment was produced
in spring sowing in the greenhouses of the Breeding
and Genetics Complex (BGC)Open asset ↗lines:111-200Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R 2 = 0.9965) for the Precision Mode and 0.0307 (R 2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.
Reproduction assets foundThe paper's Data Availability statement says the collected phenotype/structural/spectral data are publicly available on the authors' GitHub repository (allowed URL), while the analysis code is only available from the corresponding author upon request (request_only, no public URL).Dataset · publicThe data collected and used in this study are publicly available at: https://github.com/baijc4095-code/2024data . The code used for analysis can be obtained from the corresponding author upon reasonable request.Open asset ↗baijc4095-code/2024datalines:240-256Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.
Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R2 = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests.
Black locust (Robinia pseudoacacia L.) is a key tree species globally and in Hungary, valued for its economic benefits, adaptability, and ecosystem services. Despite its invasiveness and susceptibility to frost damage, its high-quality timber and significant nectar production make it economically important. This research, conducted as a collaboration between the Hungarian Forest Research Institute and the University of Debrecen, aimed to evaluate the applicability of remote sensing technologies in supporting black locust (Robinia pseudoacacia L.) research and monitoring efforts. A clonal trial established in 2020 in eastern Hungary aimed to assess the performance of newly bred black locust clones. Tree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches. Tree vitality was evaluated through UAS-based multispectral analysis using vegetation indices, including NDVI, GNDVI, NDRE, and LCI. Our findings revealed no significant differences (p>0.05) between UAS-based and traditional height measurements, confirming UAS as a reliable tool. Clones »NK2« and »PL251« showed superior growth (height of 7.6 m and 7.4 m) and health, while »Üllői« cultivar performed the weakest (5.3 m). Strong correlations were found between some vegetation indices (NDRE and LCI) and tree heights (r=0.593 and r=0.587), emphasizing the potential of remote sensing in efficient forest management. This study highlights the value of integrating UAS technology in forestry, offering cost-effective, accurate and comprehensive data for improving black locust cultivation practices.
This article presents the development and experimental study of a compact, automated apple sorting machine based on computer vision, indirect fruit weight estimation, and color grading. It is designed for use in small and medium-sized farms, where the use of industrial lines is limited by high cost and complex maintenance. The proposed machine enables non-destructive assessing and sorting of apples in a continuous flow mode without the use of mechanical weighing, includes a fruit feeding and positioning module, a computer vision system, an image processing unit, and a sorting actuator synchronized with the conveyor movement. Fruit weight and color assessment is based on visual geometric parameters (diameter, fruit height, projected area, and the proportion of surface color), extracted from digital images, followed by classification by product category using regression models. As part of the experimental study, a correlation analysis was conducted between the actual weight of apples and their geometric parameters for five varieties typical of Kazakhstan. It was shown that the projected fruit area exhibits the most stable correlation with weight, justifying its use as the primary predictor in constructing a regression model for indirect weight estimation. An assessment of the accuracy of apple classification by product categories was carried out and the influence of conveyor speed on the stability and correctness of sorting was observed. The experimental results with a total 1250 apples of five varieties - Aport Alexander, Sinap Almaty, Kazakhski Yubileinyi, Ainur, and Nursat indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s. In this mode, sorting throughput is approximately 400 kg/hour, with an average accuracy of 92% for the automatic classification in accordance with GOST requirements. These results confirm that the proposed approach provides sufficient real-time sorting accuracy with a simple machine design. The machine can be used as a standalone sorting solution, as well as a base platform for further expansion of functionality by integrating surface defect assessment and grade identification modules.
Nodule color and morphology are key readouts of legume symbiotic performance. However, long-term preservation of post-excavation nodules with intact morphology, color, and microbial cleanliness remains a major challenge. This study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization. A comprehensive evaluation was subsequently established, incorporating composite morphological score (0-5), color difference (ΔE) and its piecewise slope over time ([Formula: see text]), and visible contamination grade (0-3). Peanut and soybean nodules from multiple regions and cultivars were tracked for 24 months under five preservation methods: TAPP, FormalinCu, TAPP-Resin, Resin, and AirDry. TAPP showed the best overall preservation, with composite morphological scores of 4.65 ± 0.14 for peanut and 4.63 ± 0.22 for soybean at 24 months, and no visible mold. Color change slowed over time: [Formula: see text] decreased from 1.83 to 1.10 ΔE·month - 1 during 0-1 month to 0.16 ΔE·month - 1 during 12-24 months, yielding final ΔE values of 10.53 ± 1.88 and 10.32 ± 1.93, respectively. Notably, TAPP pretreatment markedly improved resin-embedded samples, demonstrating scalability and flexible deployment. In addition, this study further proposes a stage-wise workflow that integrates on-site pre-fixation, long-distance transport, and long-term storage to enable cross-regional circulation and collaborative phenomics of oxidation-prone, dehydration-sensitive nodules. Together, this work establishes a standardized, traceable workflow to preserve and benchmark legume root nodule phenotypes, supporting cross-laboratory comparability and longitudinal cross-source analyses.
Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R2 rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m2·m−2. Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval.
Reproduction assets foundThe paper's field-measured LiDAR point cloud and LAI data (Yingke Oasis and Huazhaizi sites) come from a publicly accessible TPDC dataset with an explicit URL in the Data Availability Statement. No author analysis code, trained models, or synthetic dataset deposit is stated.Dataset · public2024WX06.
Data Availability Statement: The dataset used in this study was obtained from the National Tibetan
Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ (accessed on 6 September 2025)), a publicly
accessible scientific data platform providing multi-source geoscientific datasets. The specific dataset
can be accessed via: https://www.tpdc.ac.cn/zh-hans/data/4d60d570-0aa9-417b-8a9d-c32b73b564
(accessed on 6 September 2025). The TPDC database integrates long-term observational and remote
sensing data with standardized quality control, ensuring the reliability and consistency of the datasets
for scientific research.
Acknowledgments: The authors would like to acknowledge the National TibetaOpen asset ↗4d60d570-0aa9-417b-8a9d-c32b73b564pdf-raw-page:19 lines:1-51Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Plant phenotyping relevance matchEurope PMC · OpenAlex · checked 13 Sept 2026
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Xi Yang · Kaiyu Guan · Min Chen · Jennifer E. Johnson · Rong Li · Hyungsuk Kimm · Genghong Wu · Jongmin Kim · Hamid Dashti · Sheng Wang · Manuel Lerdau · Christian Frankenberg · Joseph A. Berry
Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis.
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Accurate characterization of closely related Ilex taxa is essential for the conservation, documentation, and utilization of plant genetic resources. In this study, five Ilex taxa from eastern China ( Ilex rotunda Thunb., Ilex chinensis , Ilex cornuta Lindl. & Paxt., Ilex cornuta 'Fortunei', and Ilex latifolia Thunb.) were evaluated using an integrated framework combining fruit morphometric traits, CIELAB color parameters, and electronic-nose (E-nose) volatile fingerprints. Fruit transverse diameter, longitudinal diameter, single-fruit weight, fruit shape index, and peel color traits (L*, a*, b*, and chroma, C*) differed significantly among taxa (one-way ANOVA, all p I . cornuta produced the largest and heaviest fruits, I . chinensis showed the most elongated fruit shape, and I . rotunda exhibited the highest redness and chroma values. Chemometric analyses of E-nose responses further improved taxon discrimination and revealed clear divergence in volatile-response patterns. Trait-space relationships were broadly consistent with the preset phylogenetic framework, with I . rotunda showing the greatest divergence and I . cornuta and I . cornuta 'Fortunei' showing the closest similarity. These findings indicate that integrated fruit phenotyping and rapid volatile profiling provide a practical approach for Ilex germplasm identification, comparative evaluation, and resource documentation, with potential value for conservation planning and horticultural utilization.
High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination (R2) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping.
Northern Corn Leaf Blight (NCLB; also, Turcicum Leaf Blight, TLB), caused by Exserohilum turcicum (teleomorph: Setosphaeria turcica), is one of the most destructive foliar diseases of maize worldwide, often causing severe yield losses under favorable conditions. We developed a maize-specific, web-based Decision Support System (DSS) for real-time NCLB detection and management ( https://maize-nclb.streamlit.app/ ), integrating advanced deep-learning for automated diagnosis and fungicide advisory. Among thirteen Machine-learning and deep-learning models evaluated for classification, the Visual Geometry Group 16-layer convolutional neural network (VGG16) outperformed all others, achieving 94.0% accuracy, with balanced precision, recall, and F1-score of 0.94, and an AUC-ROC of 0.93. Confusion matrix analysis revealed minimal misclassification, with only 12 errors out of 357 samples, confirming the model's high reliability in distinguishing healthy and infected plants, while Grad-CAM visualizations consistently highlighted biologically meaningful lesion regions, supporting the model's interpretability and alignment with plant pathological symptoms. Field validation of DSS-guided fungicide recommendations (Azoxystrobin 18.2% + Difenoconazole 11.4% w/w SC) demonstrated significant benefits, reducing disease incidence to 6.8% compared with 67.4% in controls, achieving 90% disease reduction, and enhancing grain yield by 35.4% (8.55 t/ha), with a favorable cost-benefit ratio of 1:2.49. Seasonal disease progression analysis further confirmed DSS effectiveness, with cumulative disease burden reduced by approximately 85% compared with untreated control. These results highlight the potential of integrating deep-learning with field-validated management strategies into a practical DSS, demonstrating its potential for precision disease management in maize.
Reproduction assets foundThe paper explicitly states that the complete implementation (model training, preprocessing, evaluation, Grad-CAM visualization) and the final trained VGG16 model are publicly available on GitHub, and the deployed Streamlit DSS is publicly accessible. The Scribd link is a cited prior-work bulletin, not a paper-specificCode · publicthe complete implementation, including model training, preprocessing, evaluation, and Grad-CAM visualization, along with deployment instructions, is publicly available at: https://github.com/anuragd02/NCLB-VGG16-Detection.Open asset ↗anuragd02/NCLB-VGG16-Detectionhtml-lines:133-143Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 5 Sept 2026
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L ( SKIP19L ), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.
To achieve high-precision and high-efficiency estimation of the Leaf Area Index (LAI) of jujube trees using drone remote sensing, and to overcome the limitations of traditional vegetation index methods, such as saturation in the later stages of crop growth, sensitivity to background noise, and difficulty in capturing temporal dynamics, this study proposes a parallel hybrid deep learning framework using CNN-GRU. The model adaptively extracts spatial-spectral local features from drone RGB images through a convolutional neural network (CNN) branch, while a gated recurrent unit (GRU) branch learns the sequential evolution of LAI during key phenological periods. Finally, a meta-learner integrates spatial-temporal information for decision-making. To verify the model's effectiveness and prediction performance, the study systematically collected multi-temporal ground-measured LAI data and synchronized drone remote sensing images during critical growth stages of jujube trees in two independent years: 2024 (Bachu, Xinjiang) and 2025 (Alaer, Xinjiang). A series of spectral and texture indices were extracted as model inputs. The experimental results show that the proposed CNN-GRU model exhibits excellent learning and fitting capabilities on the training set, with an R 2 value of 0.839. On the test set, after optimization with data augmentation strategies, the model's prediction accuracy is significantly improved, with prediction accuracy reaching its best level, with an R 2 of 0.83 and an RMSE of 0.150. All error metrics outperform mainstream comparative models such as Transformer, KNN, MLP, and CNN. This study demonstrates that the hybrid deep learning architecture, combining spatial feature extraction and time-series modeling, is an effective approach for accurate and robust remote sensing inversion of crop LAI in complex agricultural scenarios, providing a reliable technical tool for the digital management of smart orchards and precise agricultural decision-making.
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Sylvain Poque · Ulrika Carlson-Nilsson · Muhammad Omer · Anna Palmé · Ingunn M. Vågen · G. Poulsen · Matti W. Leino · Kristiina Himanen · Hamid Khazaeı
Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.
Conventional two-dimensional image-based methods are limited in measuring the three-dimensional morphology of tobacco stems, especially thickness and curved geometry. This study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system. The method combines improved centerline skeleton extraction, upper–lower surface registration, and skeleton-guided cross-sectional analysis to estimate length, width, thickness, and fineness. Adaptive neighborhood re-weighting and curvature-constrained regularization are introduced to improve skeleton extraction, and reference-assisted registration is used to support thickness measurement. For a standard gauge block, the proposed method achieved mean absolute errors below 0.009 mm and root mean square errors below 0.011 mm for length, width, and thickness measurements. Validation on 30 tobacco stem samples showed good agreement with the YC image-based method for length and width, with correlation coefficients of 0.998 and 0.997, respectively. The results demonstrate the feasibility of thickness-aware three-dimensional morphological measurement of tobacco stems under the tested conditions.
Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes is therefore essential for breeding, yet conventional phenotyping is often slow, space-demanding and expensive. We present LOCOPOTS — a LOw-COst high-throughput screening platform for in vitro POTatoes under abiotic Stress — which combines individual in vitro plant culture, low-cost RGB imaging and machine-learning-based automatic segmentation using a trained model of a convolutional neural network, based on U-Net architecture. LOCOPOTS enabled the automated extraction of growth, colour, and vegetation-index traits and demonstrated robust performance across independent phenotyping rounds. We screened 30 potato varieties under control, low-nutrient and saltinity conditions, identifying contrasting growth and physiological responses. Integrated traits such as final area and height, Area_AUC and height_AUC, together with GLI, Ch ol , cive and chlorophyll fluorescence parameters, discriminated genotype performance under stress. Metabolic profiling further revealed genotype-specific reprogramming in carbon and nitrogen metabolism under low nutrition and salt stress, including changes in fructose, myo-inositol, β-aminobutyric acid, γ-aminobutyric acid, proline, and certain polyamines, identifying them as specific chemical biomarkers of plant stress responses. LOCOPOTS provides a scalable, affordable and space-efficient platform for early screening of potato genetic diversity and identification of candidate traits associated with stress resilience.
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement.
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Plant phenotyping relevance matchOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Summary Plant–microbe interactions are inherently spatial, yet the physical structure of the soil and rhizosphere is rarely treated as a mechanistic variable in experimental design. X‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur. Rather than a secondary imaging technique, X‐ray CT can offer a wealth of data as a primary experimental platform for future plant–microbe research. Here, we highlight key structural traits that X‐ray CT can quantify and discuss how they may shape microbial behaviour, plant immune responses, and disease outcomes. We expand on how X‐ray CT could be employed in future to provide a framework to disentangle direct microbial effects from indirect, structure‐mediated feedbacks. For breeding and management, it could enable selection for root traits and soil practices that engineer favourable microhabitats rather than targeting organisms in isolation. Despite this potential, broader adoption will require overcoming current limitations related to access to instrumentation, analytical expertise, and the integration of structural data with biological measurements. Overall, we suggest that resolving these issues will enable the integration of X‐ray CT‐derived structure with molecular, microbiome, and modelling approaches to enable the development of digital rhizospheres, offering a pathway from descriptive observations to predictive, structure‐aware in silico frameworks in plant–microbe research.
Rice is a staple food and a major source of calories for much of the global population. With the global population continuing to rise, breeding high-yielding rice cultivars is critical for future food security. Grain size is a key trait directly related to rice yield. In this study, QTL mapping was conducted using both phenotypic data collected with Vernier calipers and image-based phenotyping. All QTLs identified through caliper measurements were also detected using image data, which allowed for more precise localization with higher LOD scores. Grain size-related QTLs were identified on chromosomes 3, 5, 6, and 7, including major genes such as GS3, qSW5, and GW7. A novel QTL region between markers RM586 and RM1163 on chromosome 6 was identified, which has not been previously reported. Introgression of this region positively affected grain length, and an additive effect was observed when combined with qGL3. Within the RM586-RM1163 region, 16 open reading frames (ORFs) were annotated, and Gene Ontology (GO) analysis suggested their roles in regulating cellular structures and organelle functions during grain development. Among these, OsGSq6 showed a significant increase in expression from the panicle formation stage to the heading stage. Fifteen SNPs were identified within the gene, resulting in 11 distinct haplotypes, several of which were predominantly found in indica rice. OsGSq6 encodes a phosphotyrosyl phosphatase activator, suggesting its role in grain development. Image-based phenotyping also enabled the detection of varietal admixtures, contributing to improved genetic purity. This approach offers a promising strategy for enhancing rice breeding precision.
Sudanian savannas remain underexplored in terms of utilizing close-range photogrammetry (CRP) for assessing tree characteristics, leaving a gap in ecological research. This study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species. Two labeling methods, a machine learning-based approach (StemML) and a flatness/vertical structure-based method (StemFlat), were used to extract stem points. We applied three diameter estimation techniques: convex-hull line fitting (CHM), least squares circle fitting (LSM), and Ransac circle fitting (RANSAC), comparing their results against field measurements using root mean square error (RMSE), bias and the coefficient of determination R2. The combination of StemML and CHM yielded the best performance, with an RMSE of 2.1 cm (5.8%), R2 of 0.983 and a bias of −0.30 cm, accurately identifying 93% of stem segments. Diameter estimation accuracy varied with height, with optimal alignment between CRP-derived profiles and manual measurements occurring between 0.5 and 2.5 m. These findings demonstrate CRP’s potential for modeling savanna tree stems and highlight the importance of method selection in ensuring reliable measurements.
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R 2 = 0.77), biomass (R 2 = 0.73), and yield (R 2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R 2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R 2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Thinning is a cornerstone of sustainable forest management (SFM), yet decisions are often constrained by subjective experience and a lack of quantitative spatial data. To address this, we developed a precision thinning framework for Cunninghamia lanceolata plantations by fusing terrestrial and aerial LiDAR data. Instead of relying on manual selection, this study established a quantitative mechanism for identifying harvesting targets. We employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85). Furthermore, an objective multi-criteria weighting method (AHP – CRITIC) was constructed to prioritize thinning targets based on stand stability and growth status. Results indicated that the proposed framework achieved an individual tree segmentation F1-score of 89.24%. Simulated thinning based on the calculated weights significantly optimized the stand spatial structure: canopy openness increased by 11.8%, and spatial indices converged toward optimal ranges. Compared with conventional practices, the proposed approach effectively reduced growth dispersion and enhanced population coordination. These findings demonstrate that integrating multi-platform LiDAR with objective weighting algorithms offers a scientifically rigorous pathway for precision forestry, promoting both productivity and the long-term sustainability of plantation ecosystems.
Traditional field surveys of urban park biodiversity lack efficiency and scale, whereas LiDAR offers precise 3D vegetation quantification. This study investigates how 3D vegetation structural complexity impacts urban park plant diversity. We integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures. Using six key 3D structural metrics, we modeled their relationship with plant diversity via Random Forest. Results indicate canopy height standard deviation (Hstd) primarily influences cultivated plant diversity, while the vegetation density index (VDI) drives spontaneous diversity. The model predicted species richness better (30.63% variance explained) than the Shannon index (7.63%). These drivers exhibited significant non-linear effects and potential ecological thresholds. A strong synergy emerged: when the vertical structure is complex and the 3D spatial density is high, the predicted plant diversity initially exhibits a trend of saturation and stabilization. Ultimately, multi-dimensional 3D vegetation structure proves to be a robust indicator of plant diversity. Our proposed multi-platform LiDAR fusion framework enables rapid, precise ecological assessments, providing methodological references and support for the transition from 2D to 3D green quality evaluation in the fine-scale management of similar cities.
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Image analysis of pits and grains provide alternative routes for overcoming the invasive approach of genomic tools in the investigation of archaeological or modern plant material, which is only seldom a viable option due to the complex and laborious methodologies required. Nevertheless, any investigation of pit morphology and cultivar interpretation requires a high quality, comprehensive dataset for comparison. Such a benchmark dataset for the morphology of olive (Olea europaea) pits is presented in this paper, designed to facilitate similar research and establish a base for future investigations. The dataset was established by image analysis of pits of 18 olive cultivars that were photographed in both lateral and dorsal positions. A dedicated MATLAB® code was developed to extract the silhouettes of each pit and to calculate 16 morphometric traits of each view of the pit. Altogether, a total of 1008 photos of 504 pits of the 18 cultivars, together with their detailed morphometric description and statistical analysis are available here. These were used to test the accuracy of the dataset and the new approach in representing the different cultivars.
Reproduction assets foundThe paper's olive pit images (1008 photos of 504 pits) and morphometric trait data (16 parameters per view) are openly deposited on Zenodo, along with the authors' MATLAB 'PitAnalyzer' software used for silhouette extraction and trait calculation. Both are paper-specific, public, and directly actionable via the Zenodo.Dataset · publicAll the images are available on a dedicated Zenodo repository17. The file name of each image comprises an abbreviation of the cultivar name (Table 1), tree number (a, b or c), pit number (1–30) and the pit position (VD VL for dorsal and lateral, respectively).Open asset ↗Zenodohtml-lines:220-292Code · publicThe code that was used in this work is compiled as a stand-alone software based on MATLAB “PitAnalyzer”. The software is available to download at the following repository, where any use of it should be attributed appropriately to this publication (https://zenodo.org/records/18789307).Open asset ↗Zenodohtml-lines:381-404Plant phenotyping relevance matchEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.
Wheat (Triticum aestivum L.) is a globally essential cereal crop whose productivity and processing efficiency are critically influenced by the morphological traits of the grain. While biotic and abiotic stresses reduce field yields, post-harvest milling losses further diminish flour output, underscoring the importance of optimizing grain morphology for processing efficiency. This study investigates the relationships between the wheat grain shape and size parameters and their impact on milling performance outcomes to identify optimal morphological characteristics that minimize yield losses. Using a Korean wheat core collection of 566 accessions, we applied image-based phenotyping to quantify key grain traits, in this case the width, length, area, perimeter, aspect ratio, circularity, roundness, and skewness. Multivariate analyses through k-means clustering and principal component analysis showed two distinct morphological groups and highlighted the kernel width and uniformity as potential indicators. Strong positive correlations between size traits and negative correlations between shape descriptors emphasize the trade-offs influencing milling quality. Optimal wheat grains for enhanced the milling yield exhibited large, plump, regular kernels with high circularity and low skewness. These findings provide quantitative criteria to guide wheat breeding programs with the goal of genetically optimizing the grain morphology to improve the milling yield and processing quality, thereby contributing to global food security.
With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO2 exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems.
In root nodule symbiosis, symbiosome compartments accommodate nitrogen-fixing rhizobia inside the plant cell. Differentiated into bacteroids, the rhizobia are surrounded by a peribacteroid space and a plant-derived peribacteroid membrane, which separates them from the plant cytoplasm but allows signal and nutrient exchange between host and microbe. The morphological features of symbiosomes are primarily determined by ultrastructural single focal plane imaging, with limited information about spatial details. This study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants. The 3D model of a mature colonised root nodule cell region demonstrates a dense, puzzle-like arrangement of symbiosomes relative to one another and adjacent plant organelles. The symbiosome shape and size depends on the orientation and number of bacteroids within the compartment and features connective tubular structures. Furthermore, vesicular structures, some likely of bacterial origin, were present at the interface. The study presents a multi-angled analysis of symbiosome-related structures, highlighting their volumes, spatial distribution, and pronounced compactness. Interface associated vesicles, protrusions and connective structures hint towards a dynamic and flexible system that contributes to the plant-microbe crosstalk.
The leaf area index (LAI) is a key parameter for characterizing crop growth and water use efficiency. Therefore, efficient and accurate monitoring of LAI is essential for precision rice management. To overcome the limitations of traditional LAI measurement methods, which are time consuming, labor intensive, and difficult to scale, this study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models. The framework incorporates color indices (CIs) derived from RGB imagery, vegetation indices (VIs) derived from multispectral data, texture features (TIs), and texture feature indices (TFIs), and employs six machine learning algorithms to develop optimized LAI estimation models for the rice booting stage. The results indicate that at a flight altitude of 30 m, the CNN model integrating CIs and TIs achieved an accuracy of R 2 = 0.815. At 60 m, the RF model combining VIs and TFIs showed superior performance, with an R 2 of 0.866. Further integration of CIs, VIs, and TFIs at 30 m produced the best results, increasing R 2 to 0.901, reducing RMSE to 0.273, and raising RPD to above 3.0. These findings demonstrate that TFIs significantly enhance the spectral-spatial representation capability of multispectral data, thereby improving model accuracy. The combined use of CIs and VIs across different sensors compensates for the inherent limitations between spectral and spatial information, while the integration of multi-resolution TIs and TFIs effectively overcomes the constraints of single-source data. Overall, the proposed approach provides a robust and efficient solution for high-precision LAI estimation during critical growth stages of rice, offering strong support for precision agricultural management.
Harris ZN, Tran V, Piotter E, Hanlon MT, Rubin MJ, Miller AJ.
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Plant phenotyping relevance matchCrossref · checked 5 Sept 2026
Field / plotWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationBiomass / plant weightGrowth / development / phenologyPlant / canopy heightWater status / transpiration
This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.
Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.
High-throughput phenotyping is a core prerequisite for breeding climate-resilient crops. To complete related breeding work, breeders must evaluate the performance of large-scale crop populations under seven types of field abiotic stresses including drought and high temperature, and the combined technology of unmanned aerial vehicle (UAV) imaging and artificial intelligence can provide core support to meet this demand. This review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits; second, sorting out the biological significance of 12 categories of image-derived traits; third, breaking down the seven full workflow nodes ranging from flight planning to breeding decision support. Existing prior research on six crop types including wheat and rice has confirmed that this technology can improve the speed, scale and repeatability of field screening, and delivers outstanding effects when combined with multi-environment testing, genomic tools, and breeders’ expertise. This paper also sorts out six core limitations currently restricting the real-world deployment of this technology, and puts forward six future development directions to support its large-scale application.
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Plant phenotyping relevance matchCrossref · OpenAlex · checked 15 Sept 2026
Xin Zhang · Jidong Zhang · Yunling Peng · Xun Yu · Lirong Lu · Yadong Liu · Yang Song · Dameng Yin · Shaogeng Zhao · Hongwu Wang · Xiuliang Jin · Jun Zheng
Maize ( Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R 2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs . 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F 1 DH and F 2 DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.
Abstract Branch shape is a relevant tree morphological trait, representative of tree ontogenetic stage, successional status and resource-use strategy. However, branch-level studies have been limited due to tedious, time-consuming or costly measurement procedures. Here we applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation, applied to young open-grown Carya laciniosa , an ecologically valuable but rare large-seeded deciduous tree species. After accounting for branch size and orientation, we fitted different polynomial models to photogrammetric points of 41 major branch axes belonging to four arboretum-grown leaf-off C. laciniosa trees, ranging from 6.1 to 8.7 m in height. Parametric branch shape was identified with great precision by the fourth-order polynomials (R 2 = 0.96 ± 0.07 SD), but also acceptably by the third-order polynomials (R 2 = 0.93 ± 0.13 SD). The shape parameters were weakly related to branch position within the crown (R 2 < 0.40), in contrast to branch size (R 2 = 0.73). The identified S-shaped branch type may be termed plagio-orthotropic, with the proximal part arching plagiotropically and the distal part ascending orthotropically. This type of shape was stable across canopy height strata, but the shape variation and the magnitude of branch curvature clearly decreased towards the upper canopy layers, revealing combined effects of branch age, gravitropism and bending strains induced by the seasonal loads. Our results corroborate the architectural similarity among the mid-successional Carya spp. This study highlights the relevance of branch shape, which can be feasibly recorded in terms of transferable parameters, possibly as a generic functional trait with a potential for quantification of ecosystem services, such as rainfall interception and retention, shading potential, thermal regulation and biodiversity support.
To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.
Accurate soybean field phenotyping is increasingly important for breeding. However, traditional measurement methods are labor-intensive and subjective, while UAV-based approaches are challenged by complex backgrounds and densely distributed small targets. This study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry. A lightweight point-based model, Soy-MOPNet, is then proposed for fast and parallel detection of soybean seeds and stem nodes. The model incorporates the proposed SDConv, optimized hierarchical dilated convolution (HDC) principles, and PBOS to enhance adaptive feature fusion, receptive field design, and multi-branch training stability, respectively. Based on the detected keypoints, six phenotypic traits are extracted in parallel, providing comprehensive support for field phenotyping, breeding selection, and precision agricultural management.
This paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring. During the shooting process, a unified UAV photogrammetry strategy is set to ensure that the obtained images have high spatial resolution, and after pre-processing the original images, a convolutional neural network (CNN) model is utilized to extract features from the images and improve the accuracy of the CNN with the help of the idea of transfer learning. In addition, multi-scale feature fusion and attention mechanism are introduced to allow the model to focus on important location information, and weighted multi-task loss function is used to jointly optimize the multi-objective values such as plant height, leaf area index, and biomass. Experiments show that the method has good real-time performance and scalability while maintaining high prediction accuracy, providing an effective solution for crop monitoring in precision agriculture.
Abstract Verticillium wilt is one of the main factors hindering cotton yield increase, the more severe the disease, the more severe the yield loss. In order to achieve early detection and prevention of Verticillium wilt, this study investigated cotton plants naturally affected by Verticillium wilt in the field. We analyzed the cotton canopy multispectral data under the stress of Verticillium wilt and the leaf area index (LAI) data collected from the ground. Due to the low prediction accu-racy of traditional empirical models, this paper selected three different back propagation BP neural network models with 5, 10, and 15 hidden layer nodes (hln) and optimized them using four intelligent swarm algorithms: genetic algorithm (GA), particle swarm optimization (PSO), spotted hyena algorithm (SHO), and improved spotted hyena algorithm (ISHO) to estimate the cotton LAI under the stress of Verticillium wilt. Finally, BP algorithm with different node number of hidden layer was optimized by comparing four intelligent swarm optimization al-gorithms to estimate LAI of cotton under verticillium wilt stress. The findings indicated that GA-BP (hln15) was the best in the GA-BP algorithm; In the PSO-BP algorithm, PSO-BP (hln10) performed best; In the SHO-BP model, SHO-BP (hln5) had the highest model performance; In the ISHO-BP model, ISHO-BP (hln5) is the best, with a determination coefficient(R2), root mean square error(RMSE), and prediction accuracy(PA) of 0.952, 0.235, and 89.30%, respec-tively; The estimation results of ISHO-BP (hln5) model can better represent the distribution of Verticillium wilt plants than GA-BP (hln15), PSO-BP (hln10), and SHO-BP (hln5), and its estimation error range is (-0.8,0.5). Therefore, the application of ISHO-BP (hln5) model pro-vides a new method for estimating cotton LAI under pest and disease stress, and also provides technical support to meet the diversified needs of cotton farmers to increase their income and national economic development.
Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.
To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.
The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are adapted to recognize and classify plants (plant class), leaves (leaf class), and a reference marker (ref_obj class) of a known size. Segmentation of strawberry leaves using the YOLO11x-seg model makes it possible to analyze the morphometric parameters of individual leaf plates (area, perimeter, roundness, aspect ratio). A set of RGB images (2000 pieces) obtained using a GoPro HERO11 camera under controlled laboratory conditions was formed and annotated, followed by augmentation to increase the model's resistance to variations in shooting conditions. The developed algorithm converts the coordinates of the bounding boxes and segmentation masks of recognized objects into metric units using calibration coefficients calculated from a marker of known size (100×100 mm). The software implemented using PyQt5, TensorFlow, Keras, and OpenCV libraries provides not only visualization of results but also data storage in a local SQLite database with the ability to export to JSON and Excel formats. Validation of the model showed high accuracy in detecting plant bounding boxes (mAP50 = 0.906) and leaf segmentation (mAP50 -mask = 0.625). The average processing speed was 20.3 ms/frame for detection and 34.5 ms/frame for segmentation. The measurement error was less than 3.5 % for the overall parameters of the plant and 5.2 % for the morphometric parameters of the leaves, confirming the effectiveness of the method for assessing the height, width and area of plants, as well as the analysis of the leaf apparatus. The research results show the promise of an approach for automating plant phenotyping in real time.
Recent advancements in 3D reconstruction technologies have significantly transformed plant phenotyping, enabling precise, scalable, and automated trait extraction. Traditional manual phenotyping methods are increasingly being replaced by image-based approaches, such as photogrammetry, LiDAR, RGB-D sensing, and deep learning (DL)-based techniques. These tools allow for non-destructive, high-throughput measurements of plant morphology, structure, and physiological traits. This review synthesizes the state of the art in 3D reconstruction methods, including conventional geometric algorithms and emerging DL methods, and evaluates their application across diverse plant species. In addition, we discuss the sensing modalities, evaluation metrics, and crop-specific deployments. Although promising, current technologies still face challenges in terms of computational efficiency, scalability to outdoor environments, and generalizability across crop types. This review concludes by identifying research gaps and future directions for making real-time, field-deployable 3D phenotyping systems.
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
Abstract. Vegetation in dryland ecosystems often exhibits spatial patterning that leads to the formation of fertile islands. Discrete patches accumulate nutrients, organic litter, seeds and water, slow down geomorphodynamic dispersion processes and contrast with rather barren interpatch areas. While this fragmentation shapes surface dynamics in dryland environments across the globe, their spatial arrangement remains difficult to quantify at fine scales. In this study, we utilised data collected by an uncrewed aircraft system (UAS) together with field data to study surface patterns in degraded Argania spinosa forests in South Morocco that show fertile island dynamics. Point clouds generated from UAS imagery using a Structure-from-Motion photogrammetric workflow were classified into vegetation and ground points, allowing the derivation of digital terrain and digital surface models as well as conventional orthophotos and artificial ground-only orthophotos. This high-resolution geospatial data was used to map tree-influenced soil surface areas and crown areas. Their size and spatial relationships were compared and complemented by a detailed assessment of tree morphologies and terrain characteristics based on both field observations and UAS-based geodata. Spatial and statistical analyses were conducted to study the effects of tree morphology, hillslope wash, shading and wind on emerging surface patterns beneath Argania spinosa trees. Across the 496 evaluated tree-influenced areas, the surface influence extended on average to 1.69 times the size of the crown covered area. The extent of this influence beyond the canopy cover is strongly controlled by tree size and morphology, indicating that browsing-induced degradation influences not only tree conditions but also the spatial extent of positive surface effects in the interpatch area. The tree-influenced areas exhibit a consistent north-east displacement, a pattern that surprisingly appears largely decoupled from hillslope wash and is most reasonably explained by a combined influence of shading and wind effects. The results of this study demonstrate the potential of UAS imagery to complement fertile island research by providing spatial insight beyond conventional field-based assessments. At the same time, the impact of browsing on surface dynamics in a UNESCO biosphere reserve is highlighted as a factor contributing to degradation in this silvopastoral land-use system.
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Plant phenotyping relevance matchOpenAlex · checked 15 Sept 2026
Published25 Apr 2026ISPRS Journal of Photogrammetry and Remote SensingCited by 0 · OpenAlex ↗
This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of ∼ 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R 2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R 2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.
This study presents a robust framework for high-throughput olive germplasm classification, addressing the phenotyping bottleneck that currently limits breeding programs. Unlike prior research restricted to narrow genotypic ranges or single-image modalities, we analyzed 65 genetically diverse olive cultivars from the Tarom Olive Research Station (Zanjan, Iran). We employed a dual-image phenotyping approach, integrating high-resolution imagery of both fruits and kernels with quantitative weight metrics. This methodology enabled the extraction of critical morphological traits—including eccentricity, solidity, and shape factors—to train and validate seven Machine Learning (ML) algorithms. Our comparative analysis of Discriminant Analysis (DA), Support Vector Machine (SVM), Neural Networks (NN), and ensemble methods reveals that the DA model achieves superior performance, attaining a recall and precision of 0.98 when integrating fruit, kernel, and weight data. This significantly outperforms standard models like KNN and Naive Bayes in this domain. These findings demonstrate that combining multi-view imaging with morphological feature extraction provides a highly accurate, cost-effective tool for managing olive genetic resources and accelerating crop improvement.
Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R2 = 0.82 and 0.91) and tree height (R2 = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R2 = 0.73 and 0.68) and crown volume (R2 = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R2 = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions.
Laura Verena Junker‐Frohn · H. Lenz · Shiyan Jia · Alexander Putz · Jens Wilhelm · Constantin Eiteneuer · Sascha Adels · Olaf Mück · Anna Galinski · Jonas Lentz · Fabio Fiorani · Mark Müller-Linow · Kerstin Nagel
Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.