Abstract Rain-induced cracking is among the most damaging preharvest disorders of sweet cherry because even narrow cuticular fractures reduce fresh-market value and create entry sites for water loss and fungal infection. Conventional inspection is subjective and is least reliable for low-contrast microcracks near the pedicel cavity. This study developed a Fourier-guided multi-scale Vision Transformer (FGM-ViT) for joint fruit-level severity grading and pixel-level crack segmentation. The dataset comprised 960 individual fruit from four commercial cultivars and eight orchard blocks, imaged from four rotational views after natural rainfall exposure or controlled water-immersion challenge. Crack severity was assigned as intact, microcrack, moderate, or severe using stereomicroscopic measurements of cumulative crack length, maximum width, affected surface area, and crack location. All views of an individual fruit and all observations from the same orchard block were constrained to the same fold in a nested five-fold grouped evaluation. FGM-ViT combined a multi-scale spatial encoder with luminance-normalised Fourier residual tokens and frequency-guided cross-scale attention. The complete model achieved 93.8 ± 0.9% fruit-level accuracy, 93.7 ± 1.0% macro-F1, and 0.842 ± 0.018 Dice coefficient for crack segmentation. It exceeded the spatial-only transformer by 2.2 percentage points in accuracy and 2.2 points in macro-F1, with the largest gain observed for microcracks. Classification errors were restricted almost entirely to adjacent severity categories. Under pedicel overlap, glare, brightness shifts, and motion blur, FGM-ViT retained a 2.7–3.5-point advantage over the spatial-only model. Frequency-band occlusion indicated that mid-to-high spatial frequencies carried complementary evidence for narrow fractures, whereas spatial features remained essential for crack location and marketability interpretation. The dual-output framework provides an auditable, low-cost RGB approach for sweet-cherry sorting, cultivar screening, and rain-cracking phenotyping.
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.
Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it. This can be overcome by the use of machine learning and deep learning algorithms. Hence, we are proposing a method that which is detecting the disease of a tomato plant from their leaf images. Here the process is performed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN. Once after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified. And the precautions are also provided for the classified plant.
Main conclusion This review highlights that integrating physiological, molecular, imaging, and AI-based approaches enables early and reliable detection of graft incompatibility, improving rootstock-scion selection, orchard sustainability, fruit productivity, and long-term tree performance. One of the most serious problems in fruit growing is the breaking, weakening, or dying of the tree at the graft union, either within a short period of time or after 10-15 years. This condition is often triggered by environmental factors; however, it is certainly not solely caused by environmental conditions. This problem is defined as graft incompatibility. Graft incompatibility refers to the failure of successful anatomical and physiological integration between a rootstock and a scion, primarily due to biochemical, molecular, and genetic mismatches that impair vascular reconnection and long-term stability of the graft union. Graft incompatibility remains a significant constraint in fruit tree production, resulting in reduced longevity, yield, and quality of orchards. This review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility, with special emphasis on Prunus species such as sweet cherry. Physiological and biochemical markers, including phenolic accumulation, antioxidant enzyme activities, and isozyme patterns, serve as early indicators of incompatibility. At the molecular level, transcriptomic, metabolomic, and epigenetic analyses have revealed differentially expressed genes (DEGs) and post-translational modifications associated with stress signaling, vascular reconnection, and callus formation. Imaging-based non-destructive technologies such as micro-CT, MRI, terahertz, and hyperspectral imaging now allow real-time visualization of graft-union structures without damaging plant tissues. The integration of artificial intelligence and machine learning with multi-omics datasets and imaging tools offers unprecedented potential for predictive diagnosis and compatibility assessment. Collectively, these multidisciplinary advances are reshaping the detection and management of graft incompatibility, enabling faster, more reliable, and sustainable rootstock-scion selection in fruit tree breeding.
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.
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 matchOpenAlex · checked 15 Sept 2026
• Depth informed trunk detection performed reliably under field conditions. • Trunk diameter estimates aligned closely with ground truth. • Transmission-based digital odometer provided reliable along-row positioning. • Full-block mapping demonstrated large-scale applicability. Trunk diameter is an important structural trait used to assess tree size, vigor, and long-term growth in orchard systems, but it remains difficult to measure efficiently at orchard scale. This study developed and validated a low-cost mobile imaging system for automated trunk diameter estimation and spatial mapping in tart cherry (Prunus cerasus) orchards. The system integrated RGB-D cameras, deep learning-based trunk detection and segmentation, three-dimensional depth reconstruction, and driveline-based digital odometry for spatial positioning under canopy conditions where GNSS performance was unreliable. Trunks were detected using a YOLO-based model, segmented using a Segment Anything Model (SAM)-based approach, and reconstructed in 3D from depth data to estimate real-world trunk diameter at 40 cm above the trunk base. The system was evaluated in a 15-year-old, 1 ha experimental orchard in Utah, USA, and then applied in a 14-year-old, 9.5 ha commercial orchard. Under unobstructed viewing conditions, trunk diameter estimates showed strong agreement with manual measurements, with a mean absolute error (MAE) of 0.95 cm, a root mean square error (RMSE) of 1.16 cm, and R 2 of 0.79. Across all 462 trees, automated per-tree averaging produced an MAE of 1.40 cm. In the commercial orchard, mapped trunk diameter patterns aligned with UAV-derived canopy height, reflecting underlying zones of tree vigor. These results show that mobile RGB-D imaging combined with driveline-based odometry can provide practical, cost-effective orchard-scale trunk diameter mapping under commercial field conditions.
Marcel El Hajj · Kasper Johansen · Fabio Camargo · Oliver Lopez Valencia · Yu-Hsuan Tu · Victor Angulo Morales · Omar A. López Camargo · Samer K. Al Mashaharawi · Dominique Courault · Matthew F. McCabe
Monitoring crop conditions is crucial for effective crop management and provides valuable insights into soil-plant-atmosphere interactions. While some studies have used unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards, LiDAR-based time-series analysis to assess PAI variations with phenology throughout the growing season represents a significant gap in knowledge. Tracking PAI dynamics across phenological stages reflects canopy development and leaf expansion, which are directly linked to yield formation. Furthermore, the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point clouds is yet to be determined. This study aimed to demonstrate the potential of UAV-derived LiDAR time-series to monitor the PAI and tree vertical profiles at high spatial resolution throughout the growing season of a cherry orchard located in southeastern France. A time series of 14 point cloud acquisitions with a density of 3300 points/m² was collected between February and December 2022, with at least one acquisition per month, covering all phenological stages of the cherry orchard. Field measurements were collected on May 30, and October 6, to measure the PAI at twilight using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA), with 248 trees sampled. A voxel-based method was applied on the LiDAR point cloud data to create a three-dimensional grid within which PAI was estimated for each voxel. The results showed that a voxel size of at least 70 cm is required to retrieve reliable PAI estimates, while a voxel size of 100 cm produced the most accurate PAI estimates (RMSE = 0.5 m2.m-2, bias = 0.07, R2 = 0.59), when assessed against in-situ PAI measurements. The temporal variation of canopy PAI illustrated the progression of the phenological stages, including flowering, leaf development, ripening and senescence, and 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. The LiDAR-derived PAI maps and vertical profiles were able to detect trees exhibiting poor leaf development, which is an important health indicator for effective crop management in orchard settings. Future work should focus on applying UAV-derived observations to optimize crop models to enhancing decision-making tools for effective orchard management.
High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots.
Abstract Cherry is widely cultivated but remains challenging to harvest due to economic and ecological constraints, especially in developing countries such as Pakistan. Climate change, limited use of technology, and foliar diseases worsened by pesticide use further reduce productivity, particularly during fruiting. Conventional disease assessment depends on expert observation and grower experience, making it subjective and time-consuming. A comprehensive evaluation was conducted on the PlantCity dataset, which contains 5,714 high-density, full-color RGB images collected under challenging conditions and categorized into 5 classes. We compared three approaches: deep learning pre-trained, transfer learning, and a machine learning pipeline. Models were evaluated by accuracy, precision, recall, F1-score, Cohen’s Kappa, inference time, FLOPs, and throughput. Grad-CAM was used to improve interpretability. Transfer learning using DenseNet169 achieved the highest performance, with 99.80% accuracy, 99.80% precision, 99.80% recall, and a Cohen’s Kappa of 99.74%. These results were significantly higher than those obtained by other deep learning architectures and handcrafted baselines. Grad-CAM heatmaps confirmed that the models focused their attention on pathological areas. The proposed transfer-learning-based framework, particularly DenseNet169, demonstrates state-of-the-art diagnostic accuracy and features a modular structure. This design enables deployment on both high-performance servers and resource-constrained embedded devices, thereby facilitating early disease detection in precision agriculture.
In the recent era, the growth of deep learning is inevitable. Various models such as convolutional neural networks (CNNs) and transformers are used widely in images for high classification accuracy. Since the invention of transformers, researchers have widely used novel approaches using transformers to achieve an impressive accuracy. In spite of this, this paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet). ALNet consists of three major blocks: stem, core, and head. The core part is the novel classifier built as an inspiration from various pre-trained models such as ResNet, SENet (Squeeze and Excitation Network), EfficientNet, SqueezeNet, and ShuffleNet. The main objective is to build a model that has a high classification accuracy while reducing the number of parameters. This reduces the size of the model and hence makes it easy to deploy on cloud platforms and use in edge devices. The model was evaluated using 5-fold cross-validation on three different datasets. The primary dataset was a grapevine dataset with an accuracy of 99.78 percent and 100 percent in multi-class and binary classification respectively. To test the robustness of the model, a multi-class classification using the apple dataset achieved an accuracy of 99.95 percent and a binary classification with the cherry dataset achieved an accuracy of 100 percent. ALNet uses only 0.17 million parameters which is 18 times less parameters than the lightest model (SqueezeNet) and it takes only 14 seconds to train each epoch while pretrained models take 17–31 seconds. ALNet requires only 151.98 MFLOPs with a model size of 677.20 KB, making it approximately 18 times smaller than SqueezeNet. On the whole, ALNet is a highly accurate, lightweight model for plant leaf diseases prediction.
Warut Timprae · Tatsuki Sagawa · Stefan Baar · Satoshi Kondo · Yoshifumi Okada · Kazuhiko Sato · Poltak Sandro Rumahorbo · Yan Lyu · Kyuki Shibuya · Yoshiki Gama · Y. Hatanaka · Shinya Watanabe
CherryTomatoGreenhouseNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudFruitPose / keypoint estimation2D/3D reconstructionSegmentation
Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (gmax) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.
The integration of deep learning models with UAV captured images for plant disease detection has been explored in many papers and has the potential to revolutionize commercial precision agriculture, by allowing for early and efficient detection and classification of crop disease stages.In order to address the limitations posed by low-resolution aerial imaging, this paper proposes the additional integration of an Enhanced Super Resolution Generative Adversarial Network (ESRGAN) with a Convolutional Neural Network model for field monitoring through UAV captured imagery.UAVs are a cost effective method of monitoring large swaths of agricultural land; however, it is difficult to capture images of a high enough quality and clarity to be adequately analyzed by a CNN.The images typically lack the necessary resolution for accurate classification, especially for diseases with smaller, less noticable symptoms.The Real-ESRGAN model is employed to generate a dataset of high-resolution images, from low-resolution inputs, allowing the disease detection CNN to more accurately and effectively identify and classify disease stages in Armillaria afflicted cherry trees.This solution offers a solution to the problem posed by traditional UAV based approaches that enhances classification accuracy even in suboptimal conditions.Through this integrated approach, the model was able to reach an increased validation accuracy, as well as significantly decreased loss values due to the ESRGAN enhanced imagery allowing for clearer detection of early stage Armillaria symptoms.This integrated system provides a practical scalable solution for commercial agriculture, allowing for more comprehensive and efficient crop disease monitoring.Future research can be explored to optimize the architecture of this model and expand its applicability to other crops and environmental conditions, allowing more efficient precision agriculture and paving the way for more sustainable farming practices.
The PlantCity dataset addresses significant agricultural yield losses in Pakistan from plant diseases. It provides 10,667 high-resolution images of leaves from 12 key crops: apple, apricot, bean, cherry, maize, fig, grape, loquat, pear, tomato, walnut, and persimmon. The images are organized into 52 classes (41 diseased and 11 healthy) and augmented to a total of 52,273 images. Data was collected in real-field conditions in Charsadda (34.15°N, 71.74°E, typical temperature 40-44 °C) and Chitral (35.85°N, 71.79°E, typical temperature 25-30 °C) from April to July 2023-2024. The dataset enables the development of deep learning models for automated disease classification and captures a range of environmental factors, including high temperatures that can exacerbate disease symptoms. It utilizes smartphone-based computer vision to facilitate early disease identification, thereby supporting precision farming and sustainable agriculture in Pakistan.
Reproduction assets foundThe paper is a Data in Brief article describing the PlantCity plant leaf image dataset (10,667 original images, 52 classes, 12 crops, collected in Pakistan). The dataset itself is the paper's core phenotyping asset and is publicly deposited on Mendeley Data with a direct URL provided in the article.Dataset · publicon of diseases, pests, or environmental stress in plant leaves.
Data source location
Charsadda (Village Sarki) chosen for tomato and Chitral (Village Danin) for the other 11 crops, Khyber Pakhtunkhwa, Pakistan
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/w8kh2xkspx.2
Direct URL to data: https://data.mendeley.com/datasets/w8kh2xkspx/1
Related research article
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Value of the Data
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The PlantCity dataset is comprehensive, consisting of 10,667 high-resolution images across 52 classes (41 diseased, 11 healthy) from 12 crop species, collected from Charsadda (tomato disease symptoms) and Danin Chitral (selected for its agro-climatic suitability for fruOpen asset ↗Mendeley Data · 10.17632/w8kh2xkspx.2lines:1-48Plant phenotyping relevance matchCrossref · Europe PMC · checked 6 Sept 2026
Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants. This dataset was uniformly split into training, validation, and test sets to obtain consistent and unbiased model evaluations. The data pre-processing also involved pre-processing steps suitable for DL architectures to keep the input the same among all the models.We use several state-of-the-art pre-trained ConvNets models for the baselines, such as EfficientNetV2, ConvNeXt, Swin Transformer, and Vi-Transformer (ViT), to have an outlook on the performance. A new ConvNet-ViT hybrid model combines the ConvNet and ViT layers for local feature extraction and maintaining the global context. The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. Its outstanding performance of the state-of-the-art pre-trained top models positions itself as a solid model for practical agricultural use. Fusing the ConvNet and transformer frameworks jointly is beneficial for improving classification performance in image-based disease detection work.
Abstract Context Urban green spaces play a vital role in enhancing environmental quality and human well-being. However, traditional assessment methods, such as the green view index, primarily quantify green coverage while neglecting vegetation diversity, color richness, and seasonal dynamics, which are critical for urban livability. Objectives This study develops a multi-temporal and multi-perspective analysis framework for urban green space visualization, introducing the Seasonal Species-Specific Plant View Index (S3PVI) to quantify plant coverage at the species level, capturing seasonal changes and visual diversity. Methods The framework integrates computer vision, deep learning, and 3D reconstruction technologies, including structure from motion and 3D Gaussian splatting. To validate the S3PVI, case studies were conducted in Suita City, Japan, analyzing real-world seasonal vegetation patterns and testing the framework in a virtual park environment to assess its applicability in urban design. Results The S3PVI effectively captured species-specific seasonal patterns, with cherry blossoms peaking at 45.61% visibility in spring and maples at 56.78% in autumn. Comparative analysis revealed distinctive vegetation strategies between streets, with Sanshikisaido showing higher seasonal amplitude but lower consistency than Nakayoshido. Virtual simulations confirmed that multi-species schemes optimally balanced seasonal impact with year-round visual stability. Conclusions The S3PVI framework advances urban vegetation assessment by providing species-specific and seasonally dynamic visual data, supporting evidence-based urban planning for ecological sustainability and livability. Potential applications include brownfield redevelopment, virtual park planning, and urban design simulations.
Little cherry disease (LCD) and X-disease have critically impacted the Pacific Northwest sweet cherry ( Prunus avium ) industry. Current detection methods rely on laborious visual scouting or molecular analyses. This study evaluates the suitability of field asymmetric ion mobility spectrometry (FAIMS) for rapid detection of LCD and X-disease infection in three sweet cherry cultivars ('Benton', 'Cristalina', and 'Tieton') at the post-harvest stage. Stem cuttings with leaves were collected from commercial orchards and greenhouse trees. FAIMS operated at 1.5 L/min and 50 kPa, was used for headspace analysis. Molecular analyses confirmed symptomatic and asymptomatic samples. FAIMS data were processed for ion current sum (I sum ), maximum ion current (I max ), and area under the curve (I AUC ). Symptomatic samples showed higher ion currents in specific FAIMS regions ( p < 0.05), with clear differences between symptomatic and asymptomatic samples across compensation voltage and dispersion field ranges. Cultivar-specific variation was also observed in the data. FAIMS spectra for LCD/X-disease symptomatic samples differed from those for asymptomatic samples in other Prunus species, such as peach and nectarines. These findings support FAIMS as a potential diagnostic tool for LCD/X disease. Further studies with controlled variables and key growth stages are recommended to realize early-stage detection.
Rain cracking compromises quality and quantity of sweet cherries worldwide. Cracking susceptibility differs among genotypes. The objective was to (1) phenotype the progeny of a cross between a tolerant and a susceptible sweet cherry cultivar for cuticle mass per unit area, strain release on cuticle isolation, cuticular microcracking and calcium/dry mass ratio and (2) relate these characteristics to cracking susceptibilities evaluated in laboratory immersion assays and published multiyear field observations. Mass of the dewaxed cuticle per unit area and strain release upon cuticle isolation were significantly related to cracking susceptibility in lab or field. Cuticular microcracking in the stylar end region as indexed by infiltration with acridine orange was more severe in susceptible than in tolerant genotypes and significantly correlated with susceptibility to cracking in lab and field. The Ca/dry mass ratio was lower (-8%) for susceptible than for tolerant genotypes. Fruit that cracked early had less Ca than those that cracked later. Only the Ca/dry mass ratio of the stylar end region was significantly correlated with cracking susceptibility in the field. Based on stepwise regression analyses microcracking of the cuticle accounted for most of the cracking susceptibilities in field and lab (partial r2 = 0.331 to 0.338 for field vs. r2 = 0.326 to 0.453 for lab). The variability in cracking susceptibility accounted for increased to a r2 = 0.571 (lab) when adding mass of dewaxed cuticle, up to r2 = 0.421 (field) when adding the Ca/dry mass ratio in the stylar end region or up to r2 = 0.478 (field) when entering the strain release on isolation into the model. A protocol for phenotyping is suggested that allows larger progenies to be phenotyped for microcracking, DCM mass and strain release.
Reproduction assets foundThe paper's supporting information S1 Dataset contains the raw phenotyping data (cracking susceptibility, cuticle mass, strain release, microcracking, Ca/dry mass ratios) underlying all figures, publicly available as an XLSX supplement on the PLOS ONE article page. No author analysis code or trained models are reportedDataset · publicS1 Dataset. The raw data of all figures and the data on mean fruit mass of the individual genotypes are available in the S1 Dataset.Open asset ↗lines:305-314Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
ArabidopsisCherryCell / cellular structureFlowerVisualization / data management
Petal abscission involves cell death and reactive oxygen species (ROS) accumulation in the cells at the base of petals. Visualizing changes in the properties of these cells is crucial for analyzing and understanding petal abscission, a trait with important implications, especially for ornamental flower crops. This protocol describes the guidelines, experimental setups, and conditions for visualizing cell death by trypan blue staining and ROS accumulation by 3,3'-diaminobenzidine (DAB) staining in petals. Additionally, it provides instructions for staining and sectioning the entire Arabidopsis thaliana flower to give an improved view of the cells crucial for abscission. This protocol can be used to study the mechanism of petal abscission, including temporal changes at the base of petals during abscission and comparisons with mutants. Although Arabidopsis thaliana and cherry (Prunus sp.) blossoms are used as examples here, this protocol can easily be adapted for other plant species.
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( Solanum lycopersicum var. cerasiforme ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Reproduction assets foundThe paper is itself a data descriptor for a public UAV multispectral cherry tomato phenotyping dataset (calibrated aerial images, manual plant images, orthomosaics, binary masks) deposited in Dryad, with an explicit DOI and direct URL matching an allowed URL.Dataset · publicRepository name: tomatodb
Data identification number: 10.5061/dryad.63xsj3vbd
Direct URL to data: https://datadryad.org/stash/share/Wq_X7QUyGryJ-ZnmgfwRn4MtOCr4VBm_MSnhF40sv_8#readmeOpen asset ↗Dryad · 10.5061/dryad.63xsj3vbdlines:1-42Plant phenotyping relevance matchEurope PMC · checked 7 Sept 2026
Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans. The objective is to categorize 10 classes of diseases, with six diseased classes and four healthy classes, for these crops using the deep learning-based CNN-SVM model. Several pre-trained models, including VGG16, VGG19, DenseNet, Inception, MobileNetV2, MobileNet, Xception, and ShuffleNet, were also trained, achieving accuracy ranges from 53.82% to 98.8%. The proposed model, however, achieved an average accuracy of 99.09%. While the proposed model's accuracy is comparable to that of the VGG16 pre-trained model, its significantly lower number of trainable parameters makes it more efficient and distinctive. This research demonstrates the potential of the CNN-SVM model in enhancing the accuracy and efficiency of plant disease classification. The CNN-SVM model was selected over VGG16 and other models due to its superior performance metrics. The proposed model achieved a 99% F1-score, a 99.98% Area Under the Curve (AUC), and a 99% precision value, demonstrating its efficacy. Additionally, class activation maps were generated using the Gradient Weighted Class Activation Mapping (Grad-CAM) technique to provide a visual explanation of the detected diseases. A heatmap was created to highlight the regions requiring classification, further validating the model's accuracy and interpretability.
In current plant phenotyping research, the study of plant time-series images based on deep learning has received widespread attention. While such image data is relatively easy to obtain, the cost of annotation is high. One efficient method for achieving cost-effective training is through contrastive learning. Plant growth is slow, and the changes in image sequences over a period of time are small, with simple semantic information. Previous contrastive pre-training models struggled to effectively distinguish positive samples from the same image with different augmented views and similar negative samples from different images. Therefore, this paper proposes a method called self-supervised contrastive learning method for plant time-series images with a Priori Distance Embedding (PDE). The semantic information in images corresponding to different phenological stages of plants varies. This method transforms this crucial domain knowledge into prior distances for image pairs and conducts contrastive learning pre-training. The learned weights can be transferred to downstream tasks. Building upon this method, experiments were conducted on cherry time-series images to assess the quality of pre-training through a plant phenotyping image semantic segmentation task. To provide a comprehensive example of plant time-series image phenotypic analysis, this paper establishes a cherry growth temporal model, specifically including PDE pre-training, anomaly detection, semantic segmentation, and recording the results from the temporal dimension. The experiments indicate that this self-supervised contrastive learning method can be effectively applied to the pre-training of plant time-series images, demonstrating broad applicability in various computer vision studies related to plant phenotyping.
Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.
CherryLaboratory / benchtopMRI / PETLeafPhysiological trait estimationWater status / transpiration
We report on a cross-species proton-relaxometry study in ex vivo tree leaves using nuclear magnetic resonance (NMR) at 7µT. Apart from the intrinsic interest of probing nuclear-spin relaxation in biological tissues at magnetic fields below Earth field, our setup enables comparative analysis of plant water dynamics without the use of expensive commercial spectrometers. In this work, we focus on leaves from common Eurasian evergreen and deciduous tree families: Pinaceae (pine, spruce), Taxaceae (yew), Betulaceae (hazel), Prunus (cherry), and Fagaceae (beech, oak). Using a nondestructive protocol, we measure their effective proton T 2 relaxation times as well as track the evolution of water content associated with leaf dehydration. Newly developed "gradiometric quadrature" detection and data-processing techniques are applied in order to increase the signal-to-noise ratio (SNR) of the relatively weak measured signals. We find that while measured relaxation times do not vary significantly among tree genera, they tend to increase as leaves dehydrate. Such experimental modalities may have particular relevance for future drought-stress research in ecology, agriculture, and space exploration.
This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an Unmanned Aerial Vehicle with a multispectral camera that measures in the Green, Red, and Near Infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 (\({T}_{1}\)) and Treatment 2 (\({T}_{2}\)). A total of 7 photogrammetric flights were carried out for the crop monitoring and the corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance (ANOVA) showed that Ratio Vegetation Index (RVI), Green Vegetation Index (GVI), Normalized Difference Vegetation Index (NDVI), Infrared Percentage Vegetation Index (IPVI), Green Normalized Difference Vegetation Index (GNDVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) indices showed significant variation (P-value < 0.05) over time. No statistically significant difference between the two treatments was found. IPVI, NDVI, and OSAVI showed less variation in pixel values. The RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI indices proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.
ABSTRACT We report on a cross-species proton-relaxometry study in ex vivo tree leaves using nuclear magnetic resonance (NMR) at 7 μT. Apart from the intrinsic interest of probing nuclear-spin relaxation in biological tissues at magnetic fields below Earth field, our setup enables comparative analysis of plant water dynamics without the use of expensive commercial spectrometers. In this work, we focus on leaves from common Eurasian evergreen and deciduous tree families: Pinaceae (pine, spruce), Taxaceae (yew), Betulaceae (hazel), Prunus (cherry), and Fagaceae (beech, oak). Using a nondestructive protocol, we measure their effective proton T 2 relaxation times as well as track the evolution of water content associated with leaf dehydration. Newly developed “gradiometric quadrature” detection and data-processing techniques are applied in order to increase the signal-to-noise ratio (SNR) of the relatively weak measured signals. We find that while measured relaxation times do not vary significantly among tree genera, they tend to increase as leaves dehydrate. Such experimental modalities may have particular relevance for future drought-stress research in ecology, agriculture, and space exploration.
Introduction Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.
Reproduction assets foundThe paper's plant leaf disease classification experiments are built entirely on two public Kaggle image datasets explicitly cited by the authors: the PlantVillage Dataset (eight-class experiment) and the Plant Disease Prediction Dataset (three-class experiment). No author code, trained model, or supplementary deposit (Dataset · publicWe verified the effectiveness and robustness of the DeepPlantNet model by using images from the publicly available Kaggle “PlantVillage Dataset” dataset ( Dataset : https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset ).Open asset ↗Kaggle · abdallahalidev/plantvillage-datasetlines:355-366Dataset · publicWe validated our model using another common, publicly accessible Kaggle dataset, “Plant Disease Prediction Dataset,” to assess and estimate the generalizability and performance of the DeepPlantNet model ( Dataset : https://www.kaggle.com/datasets/shuvranshu/plant-disease-prediction-dataset ).Open asset ↗Kaggle · shuvranshu/plant-disease-prediction-datasetlines:729-756Plant phenotyping relevance matchCrossref · checked 14 Sept 2026
Agriculture is a dominating field that plays an essential role in the economic development of any country. In India, agriculture contributes about 17% of the total GDP. But, decreasing land under agriculture has become a prominent problem harming both, the economy and the interest of farmers. Alongside, every year farmers face many difficulties; one such is leaf disease problems. The farmers use unscientific approaches to identify leaf disease, which is a time-consuming process. This can be tackled by using plant disease detection and classification system based on ML and DL. Many researchers have used ML techniques to detect plant diseases, but most of them do not solve overfitting and testing problem. This paper initially proposes plant leaf disease detector and classifier framework using DL. Then these five Deep Convolutional Neural Network models (Vgg16, MobileNetV2, Xception, InceptionV3, and DenseNet121) are used to detect and classify plant diseases. Two datasets have been considered; First from Mendeley (plant leaf dataset), having 4590 leaf images split into twenty-two classes. Second from PlantVillage (Cherry Dataset), having 2052 leaf images split into two classes. Further, apply pre-processing techniques such as data augmentation, resizing, and rescaling to remove the problem of overfitting. Then testing and training of the proposed models have been on leaf images. To evaluate the quality and quantity of proposed models, quality matrix has been used, and the result shows that for dataset1, MobileNetV2 outperforms other existing models with 98.9% accuracy. For the Cherry Dataset, DenseNet121 outperforms other existing models with 99.9% accuracy.
In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.
This study proposed a novel framework for plant leaf disease identification. The proposed model consists of four steps including pre-processing, segmentation, feature extraction, and classification. At first, the unwanted noise and overfitting are removed, and also image contrast level is enhanced. Secondly, the Fuzzy C-Means (FCM) based Chameleon Swarm Algorithm (CSA) named as (FCM-CSA) is used for plant leaf diseased part segmentation. In the third stage, the feature extraction is performed using a fast GLCM feature extraction model. Finally, the Progressive Neural Architecture Search (PNAS) is used for plant leaf disease identification. The experimental investigations are carried out using MATLAB software with the Mendeley database. From this dataset, we have used Apple Cedar Apple Rust (ACAR), Cherry Powdery Mildew (CPM), Corn Common Rust (CCCR), Apple Healthy (AH), Grape Black Rot (GBR), Pepper Bell Bacterial Spot (PBBS), Potato Late Blight (PLB) and Tomato Leaf Mold (TLM) disease images. Different measures such as precision, recall, sensitivity, specificity, and accuracy results are used to validate the performance of the proposed model.
Plants are cultivated and consumed all over the world. They are highly nutritious and are rich in vitamins, minerals etc. However, most plants are vulnerable to biotic and abiotic diseases, which limits the yield. So, it is essential to detect and handle these diseases at the earliest to get an ample amount of produce. Computers and digital devices make this process much more effortless than manual human intervention. The general template followed by researchers is first segmenting out the diseased lesions from the leaves and then applying machine learning classifiers to differentiate between the diseases. Our proposed solution involves classification followed by lesion isolation and quantification. The techniques are automatic and require no human intervention in the segmentation steps. Additionally, most of the classification work is done on specific plant and disease combinations like cherry powdery mildew, apple rust etc., which requires retraining the classifier in the case of the introduction of a new disease-leaf pair. We tried to solve this limitation by classifying the leaf images based on the disease type, not disease-leaf pairs, as most diseases have similar infection patterns in different plants. Our study included powdery mildew, rust, and bacterial spot diseases. A classification model has been developed which classifies whether a leaf is suffering from powdery mildew, rust, bacterial spot or is healthy. A remarkable accuracy of 99.09% was observed on the test dataset. Moreover, the detection techniques are robust to various lighting conditions, leaf color patterns and symptom patterns
Highlights Asynchronous overlapping—an automatic image acquisition method for key feature regions of plant phenotypes. The distance from the plant to the camera can be characterized by the brightness in the grayscale image. Asynchronously acquire daytime RGB and nighttime grayscale images of the plant to use the proposed algorithm. In the test of the plant images, the IoU is 0.8497, reaching a similar level of interactive algorithms. Abstract. Acquiring and describing plant phenotyping is an important proposition in botany and agronomy research. In this study, a computer vision-based asynchronous overlapping segmentation algorithm is proposed for automatic image acquisition of key feature regions of plant phenotyping. Firstly, day-time RGB and night-time grayscale images of infrared light filling the crop body at the same angle are asynchronously obtained using a common closed-circuit television surveillance camera. Then, thresholding and morphological filtering of grayscale images are conducted to extract the initial region contours. With this as a precondition, the algorithm adaptively finds edge paths of key feature regions in daytime RGB images. In the test of the cherry plant image, the intersection over union (IoU) of the algorithm to segment the key feature regions is 0.8497, reaching a similar level of interactive algorithms that require human involvement. The proposed method has low cost, high segmentation accuracy, and strong applicability. The proposed method can independently realize the acquisition of the key feature regions of plant image phenotypes and can be applied to large-scale agricultural production.
This paper reports a new hydrogel extraction technique for detecting blue fluorescent substances in plant leaves. These blue fluorescent substances were extracted by placing a hydrogel film on the leaf of a cherry tomato plant infected with Ralstonia solanacearum; herein, chlorogenic acid was confirmed to be a blue fluorescent substance. The wavelength at the maximum fluorescence intensity of the film after the hydrogel extraction was similar to that of the methanolic extract obtained from the infected cherry tomato leaves. Chlorophyll was not extracted from the hydrogel film because no fluorescence peak was observed at 680 nm. Accordingly, the blue fluorescence of the substances extracted from the hydrogel film was not quenched by the strong absorption of chlorophyll in the blue light region. This hydrogel extraction technique can potentially detect small amounts of blue fluorescent substances and the changes in its amount within the leaves of infected plants. These changes in the amount of blue fluorescent substances in the early stages of infection can be used to detect presymptomatic infections. Therefore, hydrogel extraction is a promising technique for the noninvasive detection of infections before onset.
This paper reports a new hydrogel extraction technique for detecting blue fluorescent substances in plant leaves. These blue fluorescent substances were extracted by placing a hydrogel film on the leaf of a cherry tomato plant infected with Ralstonia solanacearum ; herein, chlorogenic acid was confirmed to be a blue fluorescent substance. The wavelength at the maximum fluorescence intensity of the film after the hydrogel extraction was similar to that of the methanolic extract obtained from the infected cherry tomato leaves. Chlorophyll was not extracted from the hydrogel film because no fluorescence peak was observed at 680 nm. Accordingly, the blue fluorescence of the substances extracted from the hydrogel film was not quenched by the strong absorption of chlorophyll in the blue light region. This hydrogel extraction technique can potentially detect small amounts of blue fluorescent substances and the changes in its amount within the leaves of infected plants. These changes in the amount of blue fluorescent substances in the early stages of infection can be used to detect presymptomatic infections. Therefore, hydrogel extraction is a promising technique for the noninvasive detection of infections before onset.
ElQadi, M. M. · Dyer, A. G. · Vlasveld, C. · Dorin, A.
CherryFlowerClassification2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Some ecological phenomena are visually engaging and widely celebrated. Consequently, these have the potential to generate large footprints in the online and social media image records which may be valuable for ecological research. Cherry tree blooms are one such event, especially in Japan where they are a cultural symbol (Sakura, ). For centuries, the Japanese have celebrated Hanami (flower viewing) and the historical data record of the festival allows for phenological studies over this period, one application of which is climate reconstruction. Here we analyse Flickr social network site data in an analogous way to reveal the cherry blossoms seasonal sweep from southern to northern Japan over a twelve-week period. Our method analyses data filtered using geographical constraints, multi-stage text-tag classification, and machine vision, to assess image content for relevance to our research question and use it to estimate historic cherry bloom times. We validated our estimated bloom times against official data, demonstrating the accuracy of the approach. We also investigated an out of season Autumn blooming that has gained worldwide media attention. Despite the complexity of human photographic and social media activity and the relatively small scale of this event, our method can reveal that this bloom has in fact been occurring over a decade. The approach we propose in our case study enables quick and effective monitoring of the photogenic spatiotemporal aspects of our rapidly changing world. It has the potential to be applied broadly to many ecological phenomena of widespread interest.
Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online .
Reproduction assets foundThe paper's sweet cherry and soybean leaf image datasets are publicly available at http://mfcis.online/, and the full MFCIS analysis pipeline code (with Docker files and requirements) is publicly available at https://github.com/WeizhenLiuBioinform/mfcis under a BSD-3-Clause license.Code · publicAll the code and Docker files are available at the source code repository.
Code availability
Project name: Multifeature combined plant cultivar identification system
Project home page: https://github.com/WeizhenLiuBioinform/mfcisOpen asset ↗WeizhenLiuBioinform/mfcislines:185-202Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Epicormic branches arise from dormant buds patterned during the growth of previous years. Dormant epicormic buds remain just below the surface of trees, pushed outward from the pith during secondary growth, but maintain vascular connections. Epicormic buds can be activated to elongate into a new shoot, either through natural processes or horticultural intervention, to potentially rejuvenate orchards and restructure tree architecture. Because epicormic structures are embedded within secondary growth, tomographic approaches are a useful method to study them and understand their development. Results We apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree. Techniques include the flood fill algorithm to find the pith of the tree, edge detection to approximate the radius, and a conversion to polar coordinates to threshold and segment phyllotactic features. Intensity values from magnetic resonance imaging (MRI) of the trunk are projected onto the surface of a perfect cylinder to find the locations of traces in the "boundary image". Mathematical phyllotaxy provides a means to capture the patterns in the boundary image by modeling phyllotactic parameters. Our cherry tree specimen has the conspicuous parastichy pair (2,3), phyllotactic fraction 2/5, and divergence angle of approximately 143°. Conclusions The methods described provide a framework not only for studying phyllotaxy, but also for processing of volumetric image data in plants. Our results have practical implications for orchard rejuvenation and directed approaches to influence tree architecture. The study of epicormic structures, which are hidden within secondary growth, using tomographic methods also opens the possibility of studying genetic and environmental influences such structures.
Reproduction assets foundThe paper's availability statement explicitly provides authors' analysis code on GitHub and the raw MRI data on figshare, both paper-specific and publicly actionable.Code · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗eithun/cherry-phyllotaxylines:174-195Dataset · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗10.6084/m9.figshare.7409843lines:174-195Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Sweet cherry fruit cracking caused by seasonal rains is a major source of crop loss in the U.S. Pacific Northwest region and around the globe. In-field monitoring of cherry fruit surface wetness and temperature is, therefore, very important in fruit loss management. To determine the feasibility of low-resolution thermal-RGB imagery for detecting sweet cherry surface wetness, an experiment was carried out in plots of Skeena and Selah cherry varieties with Y-trellised and vertical architecture, respectively, at the Roza Farm of Washington State University, Prosser, WA. To wet cherries, 5 mm of rain was applied by running a rain simulator for 4 min (1.25 mm min⁻¹) above canopies. Rainwater samples were collected using five rain gauges to quantify the applied amount of water. The in-field sensing setup included two custom-built thermal-RGB imagers, a microclimate-measuring unit and two leaf wetness sensors. The imagers were installed at a height of 2.1 m above the ground surface and were about 20 cm from the target cherries. The leaf wetness sensors were next to the cherries in the field of view of the imagers. A custom computer vision algorithm was developed and used to identify leaves and cherries in thermal and RGB images and extract the surface temperatures. The relationship between raw and normalized surface temperature, and wetness level and duration was investigated. The applicability of normalized cherry surface and air temperature difference was also studied. The results revealed that low-resolution thermal-RGB imagery can be used for detecting cherry fruit wetness level and duration. There was also a high correlation between the surface temperature of leaves and cherry fruits during the wetness period suggesting the temperature of leaves as reliable surrogate for cherry surface wetness and temperature monitoring. By utilizing the proposed imagery-based system, decision aid tools may be developed for efficient rainwater removal to prevent fruit cracking.
Suppression of bacterial canker disease of sweet cherry caused by Pseudomonas syringae pv. syringae (Pss), utilizing resistant scion and rootstock varieties holds promise as a cost-effective management strategy. However, a reproducible and rapid method for screening large breeding populations for resistance to Pss poses a challenge. Sweet cherry cultivars Bing, Sweetheart, Regina, Moreau, Emperor Francis and Rainier were used to examine the effects of Pss isolate, inoculum concentration (1 × 102 to 1 × 108 cfu/ml), leaf age (collected from the tip, middle or base of the shoot) and inoculation assay method (attached versus detached leaf) on disease development. Disease severity was influenced significantly (P ≤ 0.05) by inoculum concentration and the virulence of the Pss isolate. An inoculum concentration of 1 × 108 cfu/ml provided the best disease response in both leaf assays and is recommended for disease screening. Also, a significant Pss isolate x cultivar effect was observed suggesting that proper selection of Pss isolate for disease screening is critical. Disease severity was significantly (P ≤ 0.05) greater for newly expanding leaves in detached assays than for leaves classified as young or old. Disease response among cultivars for attached and detached leaf assays was significantly correlated (r = 0.53, P = 0.002), but the detached leaf assay provided better separation in disease severity among cultivars. We conclude that the genotypic variation in disease response among sweet cherry germplasm can be differentiated based on a detached assay using new leaves, a highly virulent Pss isolate, and an inoculum concentration of 1 × 108 cfu/ml.
Bacterial canker is a major disease of Prunus avium (cherry), Prunus domestica (plum) and other stone fruits. It is caused by pathovars within the Pseudomonas syringae species complex including P. syringae pv. morsprunorum (Psm) race 1 (R1), Psm race 2 (R2) and P. syringae pv. syringae (Pss). Psm R1 and Psm R2 were originally designated as the same pathovar; however, phylogenetic analysis revealed them to be distantly related, falling into phylogroups 3 and 1, respectively. This study characterized the pathogenicity of 18 newly genome‐sequenced P. syringae strains on cherry and plum, in the field and laboratory. The field experiment confirmed that the cherry cultivar Merton Glory exhibited a broad resistance to all clades. Psm R1 contained strains with differential specificity on cherry and plum. The ability of tractable laboratory‐based assays to reproduce assessments on whole trees was examined. Good correlations were achieved with assays using cut shoots or leaves, although only the cut shoot assay was able to reliably discriminate cultivar differences seen in the field. Measuring bacterial multiplication in detached leaves differentiated pathogens from nonpathogens and was therefore suitable for routine testing. In cherry leaves, symptom appearance discriminated Psm races from nonpathogens, which triggered a hypersensitive reaction. Pathogenic strains of Pss rapidly induced disease lesions in all tissues and exhibited a more necrotrophic lifestyle than hemibiotrophic Psm. This in‐depth study of pathogenic interactions, identification of host resistance and optimization of laboratory assays provides a framework for future genetic dissection of host–pathogen interactions in the canker disease.
Bacterial canker is a major disease of Prunus avium (cherry), Prunus domestica (plum) and other stone fruits. It is caused by pathovars within the Pseudomonas syringae species complex including P. syringae pv. morsprunorum (Psm) race 1 (R1), Psm race 2 (R2) and P. syringae pv. syringae (Pss). Psm R1 and Psm R2 were originally designated as the same pathovar; however, phylogenetic analysis revealed them to be distantly related, falling into phylogroups 3 and 1, respectively. This study characterized the pathogenicity of 18 newly genome-sequenced P. syringae strains on cherry and plum, in the field and laboratory. The field experiment confirmed that the cherry cultivar Merton Glory exhibited a broad resistance to all clades. Psm R1 contained strains with differential specificity on cherry and plum. The ability of tractable laboratory-based assays to reproduce assessments on whole trees was examined. Good correlations were achieved with assays using cut shoots or leaves, although only the cut shoot assay was able to reliably discriminate cultivar differences seen in the field. Measuring bacterial multiplication in detached leaves differentiated pathogens from nonpathogens and was therefore suitable for routine testing. In cherry leaves, symptom appearance discriminated Psm races from nonpathogens, which triggered a hypersensitive reaction. Pathogenic strains of Pss rapidly induced disease lesions in all tissues and exhibited a more necrotrophic lifestyle than hemibiotrophic Psm. This in-depth study of pathogenic interactions, identification of host resistance and optimization of laboratory assays provides a framework for future genetic dissection of host-pathogen interactions in the canker disease.
Bacterial canker is a major disease of cherry and other stone fruits caused by several pathovars of Pseudomonas syringae . These are P.s pv. morsprunorum race 1 (Psm R1), P.s pv. morsprunorum race 2 (Psm R2) and P.s pv. syringae (Pss). Psm R1 and R2 were originally designated as races of the same pathovar, however phylogenetic analysis has revealed them to be distantly related. This study characterised the pathogenicity of P. syringae on cherry and plum, in the field and the laboratory. The field experiment identified variation in host cultivar susceptibility to the different pathogen clades. The cherry cultivar Merton Glory exhibited a broad resistance to all clades, whilst cultivar Van showed race-specific resistance. Psm R1 may be divided into a race structure with some strains pathogenic to both cherry and plum and others only pathogenic to plum. The results of laboratory-based pathogenicity tests were compared to results obtained on whole-trees. Only cut shoot inoculations were found to be sensitive enough to detect cultivar variation in susceptibility. Measuring population growth of bacteria in detached leaves reliably discriminated pathogens from non-pathogens. In addition, symptom appearance discriminated Psm races from non-pathogens which triggered a rapid hypersensitive response (HR). The pathogen Pss rapidly induced disease lesions and therefore may exhibit a more necrotrophic lifestyle than hemi-biotrophic Psm races. This in-depth study of pathogenic interactions, identification of host resistance and optimisation of laboratory assays, will provide a framework for future genetic dissection of virulence and host resistance mechanisms.
(1) Background: The aim of the study was to use innovative sensor technology for non-destructive determination and prediction of optimum harvest date (OHD), using sweet cherry as a model fruit, based on different ripening parameters. (2) Methods: Two cherry varieties in two growing systems viz. field and polytunnel in two years were employed. The fruit quality parameters such as fruit weight and size proved unsuitable to detect OHD alone due to their dependence on crop load, climatic conditions, cultural practices, and season. Coloration during cherry ripening was characterized by a complete decline of green chlorophyll and saturation of the red anthocyanins, and was measured with a portable sensor viz. spectrometer 3-4 weeks before expected harvest until 2 weeks after harvest. (3) Results: Expressed as green NDVI (normalized differential vegetation index) and red NAI (normalized anthocyanin index) values, NAI increased from -0.5 (unripe) to +0.7 to +0.8 in mature fruit and remained at this saturation level with overripe fruits, irrespective of variety, treatment, and year. A model was developed to predict the OHD, which coincided with when NDVI reached and exceeded zero and the first derivative of NAI asymptotically approached zero. (4) Conclusion: The use of this sensor technology appears suitable for several cherry varieties and growing systems to predict the optimum harvest date.
Marcos Carrasco-Benavides · Marco Mora · Gonzalo Maldonado · Jeissy Olguín-Cáceres · Eduardo von Bennewitz · Samuel Ortega-Farías · John Gajardo · Sigfredo Fuentes
ABSTRACT A study was carried out during two growing seasons to evaluate the performance of a digital photography method to estimate the leaf area index ( LAI D ) of cherry tree cultivars. The trial comprised 10 ‘Bing’ and 10 ‘Sweetheart’ trees where actual leaf area index ( LAI A ) was obtained by defoliation. Estimations of LAI D were obtained by the batch processing of images of the canopy of the same trees which were obtained using a conventional digital RGB camera. Comparisons of LAI A and LAI D averages resulted in a good level of agreement for ‘Sweetheart’ for the two growing seasons (percentage of mean absolute error: MAE% = 10.4%). For ‘Bing’, LAI D was accurate in the first growing season (MAE% = 17.7%), but underestimated by 44% (MAE%) in the second growing season, presumably due to differences observed in the clumping index and the light extinction coefficient. Results evidenced the robustness of this simple method for determining the leaf area index of cherry trees.
Leaf area index (LAI) is a critical parameter in plant physiology for models related to growth, photosynthetic activity and evapotranspiration. It is also important for farm management purposes, since it can be used to assess the vigor of trees within a season with implications in water and fertilizer management. Among the diverse methodologies to estimate LAI, those based on cover photography are of great interest, since they are non-destructive, easy to implement, cost effective and have been demonstrated to be accurate for a range of tree species. However, these methods could have an important source of error in the LAI estimation due to the inclusion within the analysis of non-leaf material, such as trunks, shoots and fruits depending on the complexity of canopy architectures. This paper proposes a modified cover photography method based on specific image segmentation algorithms to exclude contributions from non-leaf materials in the analysis. Results from the implementation of this new image analysis method for cherry tree canopies showed a significant improvement in the estimation of LAI compared to ground truth data using allometric methods and previously available cover photography methods. The proposed methodological improvement is very simple to implement, with numerical relevance in species with complex 3D canopies where the woody elements greatly influence the total leaf area.