Leaves of terrestrial plants possess heterogeneous anatomical structures composed of multiple cell layers. Although biochemical differences among leaf tissues have been inferred from protein analyses and anatomical studies, direct comparisons of dynamic photochemical responses among tissues while preserving their spatial context remain challenging. Leaves experience intrinsically heterogeneous environments because incident light enters primarily from above and propagates through complex internal leaf structures. Therefore, analyzing photosynthetic activity at the tissue and cellular levels is essential for understanding how photosynthesis operates within structurally heterogeneous leaves. Here, we combined live leaf-section imaging with microscopic Imaging-PAM chlorophyll fluorescence measurements to analyze photochemical responses at the tissue level in living leaf sections. In dorsiventral dicot leaves, palisade tissues exhibited a higher effective PSII quantum yield [Y(II)] and more rapid induction of regulated energy dissipation [Y(NPQ)] than spongy tissues, indicating higher photosynthetic capacity and photoprotective activity. In contrast, rice leaves, which lack palisade-spongy differentiation, showed uniform photochemical responses along the adaxial-abaxial axis. In the C4 plant finger millet, mesophyll and bundle sheath cells displayed distinct photochemical responses consistent with their functional differentiation in C4 photosynthesis. These results demonstrate that photosynthetic responses are spatially organized according to leaf anatomical structure. Although section-based measurements do not reproduce the native optical and gas environments of intact leaves, they enable the comparison of intrinsic tissue-specific photochemical properties under approximately equivalent illumination. The present approach enables tissue-resolved chlorophyll fluorescence analysis in living leaves, providing a new framework for investigating how leaf architecture shapes the spatial organization of photosynthetic activity.
Soil contamination represents a growing environmental concern, particularly in areas such as the "Land of Fires" (Campania region, Italy). This study evaluates the potential of Zea mays L. as a bioindicator of soil contamination through a multiscale approach. In bin-like containers under natural conditions, maize plants were grown on untreated (C) and artificially contaminated (T) soil with a mixture of heavy metals (Pb, Zn, Cr) and benzo[a]pyrene. Analyses were conducted at leaf, canopy, and field scales, integrating eco-physiological, spectral, and UAV measurements. At the leaf level, contamination induced functional alterations in photochemical and spectral traits that were strongly dependent on the growth stage, being most pronounced early in the season and progressively attenuating thereafter. At the canopy level, contaminated plants developed less height and leaf area, together with lower chlorophyll- and canopy-density-sensitive spectral indices, consistent with reduced vegetative vigor under contamination stress. Solar Induced Fluorescence (SIF) showed no residual difference between treatments once normalized for biomass production at the end of the growing season, in agreement with leaf-level active fluorescence measurements taken on the same date. At the field scale, UAV-based classification achieved superior performance with hyperspectral relative to multispectral data in distinguishing contaminated areas. Taken together, these results show that, under the experimental exposure conditions adopted here, the detectability of soil pollution by maize progressively shifts from early, transient physiological signals to persistent structural differences, while UAV hyperspectral data provided the highest discrimination between treatments. By tracking how leaf-level functional alterations translate into canopy- and field-scale structural separability, this study provides a scale-explicit assessment of maize as a promising bioindicator of soil contamination.
Broad adoption of chlorophyll fluorescence kinetics in crop phenotyping remains limited by inconsistent parameter definitions and insufficient physiological validation. This study employed a longitudinal phenomic approach to evaluate the diagnostic value of rapid chlorophyll a fluorescence kinetics across diverse wheat genotypes throughout a spring growing season. By combining JIP-test parameters with detailed growth analysis, we built a statistical framework quantifying the predictive power and unexplained variance of biophysical parameters derived from the JIP-test, with an emphasis on performance-estimating indices. Results show that conventional parameters, such as maximum quantum yield of primary photochemistry, remain stable during vegetative growth and are highly sensitive to terminal senescence, yet display considerable noise under transient environmental fluctuations. Conversely, integrative indicators, particularly the total performance index, revealed clear seasonal trends and strong correlations with growth dynamics. By grouping JIP-test parameters functionally, this study offers a rational framework for selecting biophysical indices suited to specific breeding or research goals.
In situ monitoring of plant responses to stress is one of the most challenging aspects of precision agriculture, and the dynamic control of crop growth according to fluctuating environmental factors. Although fluorescence imaging provides a nondestructive approach for monitoring stress-related biomarkers, its performance is often hindered by the low abundance of endogenous signaling molecules and strong tissue autofluorescence. Here, we report a microneedle-integrated sensing platform that enables sensitive detection of endogenous hydrogen peroxide (H 2 O 2 ) in living plants. The platform incorporates a second near-infrared fluorescent nanoprobe composed of Er 3+ -doped lanthanide nanoparticles emitting at 1550 nm and Mo-doped polymetallic oxomolybdates serving as the H 2 O 2 -responsive unit. Embedding the nanoprobe into custom-fabricated microneedles allows precise positioning on plant midribs for continuous monitoring of H 2 O 2 dynamics. Under stress conditions, the system successfully visualized spatiotemporal fluctuations of H 2 O 2 in living tomato, spinach, and tobacco plants. This work establishes a strategy for early stress diagnosis and developing universal plant health monitoring technologies.
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Plant phenotyping relevance matchCrossref · checked 5 Sept 2026
Chlorophyll fluorescence provides sensitive information on plant photochemical responses, but its measurement requirements can limit high-throughput application. This study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying. A Photochemical Status Index (PSI) was constructed by principal component analysis from five highly correlated JIP-test energy-flux variables (RC/CS, ABS/CS, TRo/CS, ET2o/CS, and RE1o/CS). The dataset comprised 50 aggregated species-by-soil-moisture-stage observations representing ten species and five sequential soil-moisture stages. Eleven RGB-derived variables were evaluated, and a partial least-squares regression model was assessed using nested leave-one-species-out validation, with all data-dependent procedures repeated within each outer training fold. PC1 explained 96.5% of the shared variation among the fluorescence-derived fluxes. The predictors g, GLI, ExG, ExGR, and CIVE were retained in all ten outer folds. The final model yielded a pooled out-of-fold R2 of 0.469, an RMSE of 1.585, and an MAE of 1.183. However, species-specific R2 ranged from −0.179 to 0.959, and a calibration slope of 0.509 indicated prediction-range compression. These findings provide proof-of-concept evidence of moderate RGB-based approximation of fluorescence-derived photochemical status, but inconsistent species transferability and the common soil-moisture/time gradient require external validation before practical deployment.
Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.
Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H2O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions.
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.
Matuszynska, A. · Sansa, O. · Adekoya, F. J. · Akinyemi, O. O. · Anokye, E. · Bashir, O. B. · Boyny, Z. Z. F. · Chukwuka, M. K. · Corvest, E. · Dada, A. O. · DellAcqua, M. · Ehemba, G. L. · Finkbeiner, A. J. · Hamabwe, S. · Hodehou, D. A. T. · Kacheyo, O. · Kamfwa, K. · Mhango, K. J. · Abdullahi, W. M. · Munduwe, G. · Ntukidem, S. · Obisesan, O. K. · Odesina, I. S. · Ogechi, N.-U. · Olaoye, O. D. · Olayinka, M. M. · Osei-Bonsu, I. · Rilwan, K. O. · Stival, L. · Tehar, Z. · Tende, R. M. · To, J. · Ugochukwu, U. K. · Unger, A. · van Aalst, M. · Vrbic, D. · Zhang, C. · Theeuwen, T. P. J. M. · Kramer, D. M. · Kromdijk, J.
Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.
Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.
Abstract Plants employ non-photochemical quenching (NPQ) to protect their photosynthetic apparatus from photodamage. The response latency of NPQ following changes in light intensity is thought to significantly decrease photosynthetic efficiency. The amount of NPQ is commonly quantified from chlorophyll-fluorescence techniques using the Stern–Volmer equation, which requires fully closed reaction centres (RCs) of photosystem II, yielding NPQ in the absence of photochemical quenching ( $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed ). However, in nature, NPQ and photochemical quenching are normally present simultaneously. Therefore, to obtain a full understanding of this process, NPQ should also be explored when the RCs are open. Here we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements. A detailed comparison in Arabidopsis thaliana plants reveals that the value of $${\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Open is ~35% lower than that of $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed . This difference is consistently observed across all measurements and is seen both upon closing ( $${\rm{NPQ}}^{\rm{Open}}\to {\rm{NPQ}}^{\rm{Closed}}$$ NPQ Open → NPQ Closed ) and upon reopening ( $${\mathrm{NPQ}}^{\mathrm{Closed}}\to {\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Closed → NPQ Open ) of the RCs. We show that this difference can be explained by the presence of RC-induced ‘instantaneous’ switching of the NPQ quenching rate. This means that, in plants, NPQ is much more economical than is widely believed, it is large when its presence is needed, and it decreases instantaneously when the need disappears.
Reproduction assets foundThe paper's custom ultrafast fluorescence analysis code (ICA-based PSII/PSI deconvolution and NPQ calculations) is explicitly deposited by the authors on GitHub, alongside the original data contributions.Code · publicr(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Funding
This work was supported by ‘Nanoscale regulators of photosynthesis’ NWO research project (project number: OCENW.GROOT.2019.86).
Data availability
The original contributions presented in the study are available via GitHub at https://github.com/L-Ramakers/Heimdall .
Code availability
The custom analysis code used in the study is available via GitHub at https://github.com/L-Ramakers/Heimdall .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afOpen asset ↗L-Ramakers/Heimdalllines:88-125Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 15 Sept 2026
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.
Ground-level ozone (O 3 ) adversely affects rice physiology and is associated with yield reductions. This study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production in China's primary rice-growing region-the Middle and Lower Reaches of the Yangtze River (MLYR)-from 2019 to 2023. We fused Sentinel-5P TROPOMI total ozone column (TOC) data, a harmonized multi-satellite solar-induced chlorophyll fluorescence (SIF) product (LHSIF), high-precision rice distribution maps, and ERA5 meteorological reanalysis data. In addition to SIF, we examined multiple vegetation indicators (chlorophyll content, leaf area index, and vegetation indices) to capture broad physiological responses. A bidirectional fixed-effects panel model was employed to control for spatiotemporal confounders, revealing a significant inhibitory effect of O 3 on photosynthesis (β = -1.334 × 10 -5 , p 3 concentrations would increase regional SIF by 36.36%, while a commensurate 10% reduction in annual exposure could elevate rice yields by approximately 8.4%. This spaceborne remote sensing approach provides a robust and transferable methodology for the precise regional monitoring of ozone stress and for informing targeted mitigation strategies to safeguard crop productivity.
The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.
Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, TN, USA. Two hundred transgenic switchgrass plants (Panicum virgatum L. "Performer") were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a switchgrass ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high- and low-volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Reproduction assets foundThe paper's Data Availability statement deposits all sampling data, modeling code, and simulation results on the Virginia Tech Data Repository (DOI 10.7294/25733604), which is an allowed URL. This directly covers the paper's pollen concentration measurements and Lagrangian stochastic dispersal modeling. Other URLs (e.gDataset · publicAll sampling data, modeling code, and simulation results underlying this manuscript are made available on the Virginia Tech Data Repository at https://doi.org/10.7294/25733604 .Open asset ↗Virginia Tech Data Repository · 10.7294/25733604lines:201-219Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Rapid, non-destructive phenotyping is vital for early nutrient deficiency detection in plant research and agriculture. Nitrogen (N) and magnesium (Mg) deficiency are hard to distinguish with instruments measuring only chlorophyll content since both deficiencies lead to loss of chlorophyll. This study evaluated the suitability of a commercially available dual-excitation fluorescence sensor (measuring chlorophyll (Chl) and epidermal UV absorbing compounds) for identifying nutrient deficiencies in maize seedlings. Both N and Mg deficiencies in maize seedlings caused a decrease in the Chl index and an increase in the flavonol (Flav) index, although the response of the Flav index was much weaker under Mg deficiency. This happened in spite of a much larger increase in sugar concentrations under Mg deficiency. Furthermore, the nitrogen balance index (NBI) detected N deficiency earlier in leaves developing under nutrient deficiency than in those present before treatment application. Spatiotemporal analysis revealed distinct patterns of Flav index increase: N deficiency caused a marked increase in upper (younger) leaves, whereas Mg deficiency initiated Flav index increases at the tips of lower (older) leaves. Utilizing green- and red-light excitation of chlorophyll to infer epidermal anthocyanins turned out to be not straightforward in nutrient-deficient maize leaves. Taken together, these findings reveal advantages and limitations of Chl fluorescence in diagnosing nutrient stress and underscore the importance of understanding spatial-temporal nutrient dynamics for accurate early detection. Leaf age should be considered for fertilization decisions based on the NBI. A strategy is suggested how anthocyanins can be detected without problems.
Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Plant phenotyping relevance matchCrossref · Europe PMC · checked 14 Sept 2026
Sebastian Tonn · Mon-Ray Shao · Jos de Wit · Rami Mousa · Iñigo Bañales · Roy A M van Beekveld · Arjen N Bader · Henriëtte D L M van Eekelen · Ric C H de Vos · Eefjan Breukink · Guido Van den Ackerveken
Abstract Effective plant disease phenotyping is crucial for resistance breeding, but traditional visual assessment is often inaccurate and inefficient. This is particularly challenging when breeding lettuce (Lactuca sativa) for resistance to downy mildew, given the obligate biotrophic lifestyle of the causal pathogen Bremia lactucae. We discovered that B. lactucae-infected lettuce exhibits patches of increased blue-green fluorescence (BGF) under UV-A excitation from 6 d post-inoculation, preceding visible symptoms. Co-localization of BGF with hyphae, visualized with trypan blue, indicates that BGF is induced by downy mildew colonization. We therefore investigated its potential for non-invasive disease detection and quantification, as well as the underlying physiological changes. Using a custom imaging system, we demonstrate that BGF leaf area correlates with downy mildew severity and can be automatically quantified via a U-Net-based convolutional neural network, enabling early, objective disease assessment. Exploring transcriptomic and metabolomic changes associated with BGF, we found that induction of the phenylpropanoid pathway led to accumulation of caffeoylquinic acids, whose fluorescence spectra overlap with that of BGF tissue, supporting the hypothesis that these compounds contribute to the fluorescence signal. BGF imaging offers a powerful tool for phenotyping in lettuce breeding and for identifying quantitative resistance traits that support durable downy mildew resistance.
Controlled environment agriculture (CEA) is essential for resilient crop production but faces high energy demands and operational costs. While high-throughput phenotyping (HTP) provides critical biological feedback to optimize these systems, conventional HTP platforms remain prohibitively expensive, infrastructure-heavy, and technically complex for widespread adoption. This review examines the emerging shift toward “affordable phenomics”, an approach integrating low-cost, open-source microcontrollers and Internet-of-Things (IoT) devices to continuously capture dynamic plant physiological data. By utilizing customizable tools such as modular chlorophyll fluorometers and wearable sensors, researchers and commercial growers can non-destructively monitor key traits like photosynthetic efficiency and water status in real time. Coupling these accessible sensing networks with artificial intelligence (AI)-driven analytics allows static environmental controls to transition into dynamic, plant-centered feedback systems. We synthesize recent advancements in affordable sensor technologies and review how temporal AI modeling extracts biologically meaningful features from longitudinal datasets to direct adaptive lighting and irrigation strategies. Furthermore, we critically assess current technological limitations, including sensor calibration, signal noise, cross-platform data standardization, and edge-versus-cloud computation tradeoffs. Finally, we highlight essential future research directions, particularly the development of robust edge-computing frameworks and predictive crop digital twins, demonstrating how affordable phenomics offers a scalable, data-driven pathway to improve resource-use efficiency in modern agriculture.
Hydrogen peroxide (H 2 O 2 ) is an important signaling molecule in plants under stress, and its level can be stimulated by abiotic stress and oxidative stress, which will seriously affect plant growth and development. Additionally, the presence of excessive residual H 2 O 2 in food can pose significant health risks to humans, because intake of H 2 O 2 can lead to serious pathological conditions. Therefore, it is necessary to develop a simple and efficient method to detect H 2 O 2 in both plants and food. In this paper, we designed a fluorescence probe NBP, which has the advantages of high selectivity, low detection limit (80 nM) and long emission wavelength (648 nm). The imaging effect of exogenous H 2 O 2 was realized in the roots of Platycodon grandiflorum . By exploring the interplay between H 2 O 2 , plant metals, and drought stress, we can observe the up-regulation of H 2 O 2 in the roots of Platycodon grandiflorum under adverse conditions, and the root 3D imaging study could be realized. Then we combined the fluorescence probe with a smartphone, which enables on-site detection of residual H 2 O 2 in various milk samples. In addition, we investigated the fluorescence imaging of endogenous and exogenous H 2 O 2 in living cells using NBP. Therefore, this study provides a new way to assess the oxidative stress risk of Platycodon grandiflorum roots under abiotic stress, which is expected to improve plant production and has broad application prospects in food sample detection.
Plant-driven lighting control has been proposed as a strategy to regulate supplemental light-emitting diode (LED) intensity according to real-time plant physiological status. This study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control. The model incorporated light intensity, CO 2 concentration, air temperature, vapor pressure deficit, short-term light history, and diurnal effects. In a greenhouse validation experiment, supplemental lighting was regulated using either direct chlorophyll fluorometer measurements of Φ PSII (sensor-based control) or Φ PSII values predicted by the machine learning model (ML-based control), and compared with a constant photosynthetic photon flux density (PPFD) treatment. Both sensor- and ML-based control stabilized photochemical activity across the photoperiod relative to constant PPFD. Although plant growth did not differ among treatments, sensor-based ETR control achieved the highest energy use efficiency for LED lighting in this study. These findings demonstrate the feasibility of integrating predictive ML models into plant-based lighting control systems and indicate that sensor-based biofeedback control improved the energy-use efficiency of greenhouse supplemental lighting without compromising crop growth.
Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (R p 2 =0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.
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 matchCrossref · checked 15 Sept 2026
Accurate monitoring of nitrogen nutrition is critical for optimizing cotton production. Traditional machine learning-based inversion models have limited effectiveness for precision monitoring. Multisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity. Cotton plants subjected to different nitrogen treatments were investigated. A two-year pot experiment was conducted to collect main-stem leaf images to construct an image pretraining dataset for model transfer. In a field experiment conducted over one year, main-stem leaf data were collected using hyperspectral, chlorophyll fluorescence, and digital camera sources, thereby providing a multisource dataset for training monitoring models. Two architectures—a neural network (NN) and an interpretable deep forest (DF), which are suitable for small-sample spectral, fluorescence, image color, and texture-sequence features—were constructed to improve the accuracy of nitrogen content inversion. Additionally, a two-dimensional sliding-window processing method was introduced into the DF multigranularity scanning module, and a transfer-learning-based two-dimensional convolutional NN was employed to directly model small-sample two-dimensional images. Building upon the outcome, multilayer fusion models were constructed, with corresponding fusion strategies designed for homogeneous sequence inputs and heterogeneous image–sequence inputs. The results showed that NN and DF can effectively handle limited sample sizes and outperform traditional machine learning models. Among the fusion models, the optimal secondary decision-level fusion model achieved an R² of 0.926 on the independent test set, indicating good performance under small-sample conditions. This study provides a methodological reference for the precise monitoring of crop phenotypic parameters under small-sample conditions.
Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor the presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified the channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap.
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 · publicThe segmentation and histograms were acquired using MATLAB script ( https://github.com/NIB‐SI/Nuclei‐segmentation ). The parameters used in the script to achieve appropriate segmentation are listed on GitHub, Case 1 ( https://github.com/NIB‐SI/Nuclei‐segmentation ).Open asset ↗NIB‐SI/Nuclei‐segmentationlines:255-341Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Published1 Jul 2026Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for PhotobiologyCited by 0 · OpenAlex ↗
Accurately retrieving Sun-Induced Fluorescence (SIF) is critical for monitoring plant physiological status, yet the signal is significantly distorted by light reabsorption and scattering within the canopy. While empirical models exist for the far-red region of the spectrum, accurately accounting for the photon escape fraction in the complete Chlorophyll Fluorescence (ChlF) emission range remains challenging. Based on our previous work under monochromatic conditions, in this work we present a photophysical framework to estimate the chlorophyll fluorescence escape fraction (f esc ) across the full chlorophyll emission spectrum (600-800 nm) under polychromatic excitation. The methodology integrates experimental radiance measurements of Bistorta amplexicaulis with an algorithm to decouple reflectance from emission. To evaluate the model's robustness in the field, we conducted a global sensitivity analysis using a synthetic dataset generated by coupling the SMARTS atmospheric radiative transfer model with the PROSAIL canopy model. Our results demonstrate that failing to account for canopy light reabsorption and scattering can underestimate fluorescence yields by approximately 25%. We identified distinct drivers for f esc in SIF-relevant bands: f esc in the red region (687 nm) is primarily governed by chlorophyll content and Leaf Area Index (LAI) due to intense fluorescence reabsorption, while f esc in the far-red region (760 nm) is dominated by canopy structure and leaf inclination (LIDFa). This study provides a practical and robust estimation method for f esc at the canopy level, offering a key tool for improving the accuracy of SIF-based photosynthetic efficiency assessments in both environmental and agronomic remote sensing applications.
Lijuan Ma · Muhammad Fraz Ali · Xiaotian Ren · Wanrui Han · Xinhua Lv · Shengnan Wang · Shengyan Pang · J Zhang · Ning Ding · Haowei Feng · Yongqiao Zhang · Tingting Wu · Rui Wang · Xiang Lin · Dong Wang
Improving nitrogen use efficiency (NUE) in wheat is critical for addressing the dual challenges of global food security and environmental sustainability. Globally, only 42%-47% of applied nitrogen (N) fertilisers taken up by crops, with remainder lost to the environment, driving soil and water pollution, greenhouse gas emissions, and ecological imbalances. This review provides a comprehensive synthesis and integrative framework- integrating agronomic practices, advanced remote sensing and genomic approaches to enhance wheat NUE. We first examine the physiological basis of NUE, emphasising the synergy between photosynthetic carbon assimilation and N metabolism, the critical role of Rubisco in carbon-nitrogen coupling, and the temporal dynamics of N uptake, transport, and remobilisation throughout the wheat growth cycle. The temporal mismatch between source-sink N partitioning during grain filling emerges as a major physiological constraint limiting NUE in modern high-yielding varieties. We then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches. Through integration of multispectral imaging, LiDAR, thermal infra-red sensing, and solar-induced chlorophyll fluorescence, coupled with three-dimensional radiative transfer models and machine learning algorithms, these technologies enable non-destructive, real-time monitoring of crop N status while overcoming spectral-structural ambiguity and saturation limitations. From a genomic perspective, we synthesise recent progress in quantitative trait loci mapping and genome-wide association studies (GWAS), identifying key genetic loci controlling root architecture, N uptake transporters (NRT/AMT families), and grain filling efficiency. Multi-omics integration-spanning genomics, transcriptomics, and metabolomics-reveals temporal genetic networks distinguishing short-term nitrogen signalling responses from long-term adaptive remodelling, with genes such as TaNAC2-5A, TaNPF6.2, and QMrl-7B emerging as promising targets for molecular breeding. High-throughput phenotyping platforms enable time-series GWAS analysis, capturing developmental dynamics and genotype × environment interactions that traditional approaches miss. Finally, we discuss sustainable N management strategies, including enhanced efficiency fertilisers, precision application technologies, and soil health optimisation. By integrating these multidisciplinary approaches within a Genotype × Environment × Management framework, this review provides a roadmap for developing climate-smart, N-efficient wheat varieties and precision N management systems that simultaneously enhance productivity, reduce environmental footprints, and ensure sustainable agricultural intensification.
Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll measurements, it is destructive and temporally limited, whereas portable optical meters such as the CCM-300 enable rapid, non-destructive measurement of the chlorophyll fluorescence ratio (CFR) but require species- and season-specific calibration. This study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom. Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. RF regression achieved the highest predictive performance within the calibration dataset, although substantial uncertainty remained at the leaf level; a simple linear model was therefore adopted for cross-year projection due to its stability under extrapolation. Applying this calibration to daily 2022 CFR measurements generated a continuous "virtual acetone" trajectory, enabling qualitative comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines; however, senescence, defined as the initiation of sustained post-peak decline, occurred earlier during the warmer and drier 2022 season. Mixed-effects modelling identified positive effects of temperature and wind speed on CFR in 2022, while generalised additive modelling of the 2023 dataset revealed a non-linear seasonal decline under comparatively mild conditions. Because cross-year projections rely on a low-fit linear calibration, interannual differences are interpreted primarily in terms of relative seasonal trajectory shape and timing rather than absolute chlorophyll magnitude.
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 · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Fluorescence Lifetime Imaging Microscopy (FLIM) is becoming a key technique for live-cell multiplexing and label-free detection of endogenous fluorescence in animal systems. Its potential in plant biology, however remains largely unexploited, despite its integration into a number of commercial microscopy setups. Here, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores. Lifetime imaging of different fluorescent reporters targeted to distinct organelles (nucleus, plasma membrane, endoplasmic reticulum, etc.) and subsequent analysis of the decay curves using different modes allowed us to simultaneously discriminate up to four spectrally overlapping fluorophores solely by lifetime differences in specific subcellular compartments. Remarkably, fluorophores with lifetimes differing by as little as 0.1 ns can be reliably discriminated using one of these modes, namely Phasor-based analysis. Moreover, we show that the same fluorophores exhibit compartment-specific lifetime shifts, enabling Phasor separation of identical tags residing in different organelles. Finally, we extended the Phasor approach to label-free imaging of endogenous plant fluorescence. Together, these results establish FLIM-Phasor as a versatile, multiplex-capable tool for plant cell biology, opening new avenues for imaging strategies that yield higher content information at both cellular and tissue-level resolution.
First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.
First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Published1 Jun 2026Spectrochimica acta. Part A, Molecular and biomolecular spectroscopyCited by 0 · OpenAlex ↗
Wheat stripe rust, caused by Puccinia striiformis f. sp. Tritici (Pst), represents a significant threat to global wheat production. Early detection, particularly during the asymptomatic phase, is critical for effective disease management. Hyperspectral sensing can detect subtle physiological alterations associated with initial infection; However, its effectiveness is frequently limited by substantial background interference from normal plant growth. In this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR). To mitigate background the interference, generalized two-dimensional correlation spectroscopy (2D-COS) was employed, utilizing infection time as the perturbation variable. This approach surpasses conventional dimensionality reduction techniques such as principal component analysis (PCA) and the chemometric feature selection algorithm known as competitive adaptive reweighted sampling (CARS). Through this methodology, six feature bands exhibiting distinct absorption changes were identified. Analysis of synchronous and asynchronous 2D-COS correlation features from 1 to 6 days post-inoculation (dpi), enabled effective discrimination between spectral variations attributable to growth and those specific to disease responses. The biological significance of these spectral dynamics was empirically validated using steady-state chlorophyll fluorescence imaging and destructive biomass measurements. This combined evidence confirmed that Pst-induced chloroplast functional impairment strictly precedes macroscopic tissue structural collapse. This process effectively suppressed background noise while preserving critical infection-related signals. Subsequently, three classifiers-support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost)-were evaluated using the extracted 2D-COS features. Asynchronous features generally produced superior classification performance, with XGBoost achieving the highest accuracy (86.79%) and area under the receiver operating characteristic curve (AUC) (94.12%). Compared to conventional methods like PCA and CARS, 2D-COS more effectively attenuated growth-related interference and accentuated early disease signatures. These results demonstrate that the integrated framework of "multiplicative scatter correction (MSC) + Asynchronous Correlation Features + XGBoost" offers substantial potential for accurate, non-destructive, and early diagnosis of wheat stripe rust.
In NASA's controlled-environment plant growth systems, early and autonomous detection of crop stress is critical for sustaining food production during long-duration space missions. Hyperspectral imaging (HSI) has proven effective for early stress detection, yet the molecular processes underlying diagnostically informative spectral signals remain poorly defined. Here, we present a two-stage phenomics-to-molecular framework to evaluate whether hyperspectral signatures associated with early drought detection correspond to coordinated molecular stress responses in lettuce. In the first stage, reflectance and fluorescence HSI were used to identify early drought detection windows in 'Dragoon' lettuce subjected to controlled water limitation over a 15-day treatment period with daily imaging. Classification models integrating reflectance and fluorescence outperformed single-modality models and achieved high accuracy as early as day after treatment (DAT) 4, reaching up to 97% at DAT 5. Partial least squares discriminant analysis (PLS-DA) identified predictive wavelengths concentrated in blue-green, red, and red-edge regions associated with chlorophyll absorption and photosystem II activity. In the second stage, independent transcriptomic and untargeted metabolomic profiles were integrated with hyperspectral signatures using MOFA2 to establish biological context. This analysis revealed a dominant drought axis characterized by early activation of ABA signaling, osmotic adjustment, phenylpropanoid metabolism, and lipid and membrane remodeling, with maximal molecular divergence at DAT 5, coinciding with peak hyperspectral classification performance. Notably, wavelengths optimized for early stress discrimination were systematically shifted toward shorter, optically efficient regions relative to those most strongly associated with downstream metabolic abundance, indicating that HSI primarily captures early structural and energetic consequences of molecular stress responses rather than direct biochemical composition. Together, these results demonstrate that hyperspectral imaging can function as a non-destructive, biologically interpretable molecular proxy for drought stress, providing a foundation for compact, hands-free sensing systems capable of distinguishing stress-specific plant states in space agriculture.
Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.
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.
Maarten Besten · Anna Daamen · Matyás Fendrych · Jan Willem Borst · Joris Sprakel
Field / plotChlorophyll fluorescenceCell / cellular structureWhole plant / canopy / plot / field
The advent of spatial and quantitative biology has led to immense advances in understanding the complex inner workings of plants, down to the molecular scale. Functional imaging of live plants, which enables the spatial and quantitative mapping of biochemical cues, physicochemical properties of cellular structures, and the dynamics of physical and chemical signals with unprecedented resolution, has become a key technology for advancing the mechanistic understanding of plant cell biology. In this review, we highlight progress in live functional imaging in plants through the use and development of chemical fluorescent probes, which enable plant functional imaging without requiring genetic manipulation of the study object. We explain how probes sense, target, and report on functional features within the plant cell; discuss their limitations, including toxicity; and provide case studies to exemplify how these tools can complement biological studies to unravel the complex machinery that makes plants work. We conclude by outlining the expected future development of this field and identifying key challenges that lie ahead.
Peroxynitrite (ONOO - ) serves as a critical redox signaling molecule in plant stress responses and ferroptosis, yet real time monitoring within complex plant matrices remains challenging. To address this, we synthesized a ratiometric nanosensor (DA) through molecular self-assembly. The DA particles (258 nm in diameter) exhibited enhanced sensitivity driven by an excited-state intramolecular proton transfer (ESIPT)-triggered restricted intramolecular motion mechanism, resulting in distinctive aggregation-induced emission (AIE) behavior. This nanoscale configuration improved tissue penetration and eliminated the aggregation-caused quenching (ACQ) effect commonly observed in traditional rhodamine derivatives. Meanwhile, the DA probe featured a large Stokes shift of 167 nm and an ultra-low limit of detection (LOD) of 6.4 nM. Leveraging these optical advantages, the nanosensor enabled real-time visualization of ONOO - dynamics in plant tissues and quantitative assessment of ONOO - accumulation under cadmium (Cd 2+ ), sodium chloride (NaCl), and erastin induced stress. This work represents the first application of an ESIPT-AIE hybrid probe for ONOO - detection in plants, providing a powerful analytical platform for elucidating oxidative stress mechanisms and advancing strategies to enhance crop resilience.
Effectively imaging the variation of heavy metal induce stress (HMIS) in plant is significantly important for stress resistance research in the fields of environmental and plant biology. However, due to the absence of distinctive parameter to reveal the relationship between HMIS and plant homeostasis, the reported fluorescence sensors fail to assess HMIS. Herein, a new fluorescent sensor (quinoline-based viscosity probe, QVP) with prominent-responsive viscosity was first developed for evaluating HMIS in plants. Spectral experiments indicate that QVP exhibited selectivity, sensitive, photochemical stability, and pH adaptability for viscosity detection. Motivated by the robust detection capacities, QVP was further applied for clear fluorescence imaging of viscosity changes of plant cell (onion epidermis and scallion bulb) induced by HMIS (Cu 2+ , Au 3+ and Ag + ). Notably, the cellular viscosity was positively correlated with Cu 2+ concentration. More importantly, the sensor QVP had good penetration within plant tissues and enabled viscosity imaging of root hairs, leaves and other tissues. This work not only provides a novel molecular tool for understanding HMIS resistance of the plant by investigating the dynamic change of intracellular viscosity, but also provides an additional dimension for evaluating crop stress resistance.
Quantifying the kinetics of net CO2 assimilation (A) and stomatal conductance (gs) under fluctuating light typically relies on gas exchange measurements, which are slow and thus unsuited for high-throughput phenotyping. As a result, faster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale. However, first the relationship between non-steady-state parameters must be examined in greater detail. In this study, we aimed to determine whether variations in non-steady-state values of chlorophyll fluorescence and leaf temperature reflect differences in key gas exchange traits under fluctuating light conditions. Here, the correlations between the times required for a change in non-steady-state A, gs, operating efficiency of PSII (ΦPSII), and leaf temperature (Tleaf) during stepwise changes in light intensity were evaluated across nine plant species. Both steady-state and non-steady-state photosynthetic traits varied significantly among species. Overall, we found significant positive correlations between non-steady-state A and ΦPSII for time to 50% and 90% of final steady-state values (t50; r2 = 0.70) and (t90; r2 = 0.33). The t90 of gs and that of Tleaf were also significantly correlated after both increases (r2 = 0.45) and decreases (r2 = 0.61) in light intensity. Our findings suggest that the times required for a change in ΦPSII (particularly t50) and Tleaf (particularly t90) can be used as indicators of dynamic A and gs, respectively, facilitating faster phenotyping of the complex processes of photosynthesis and stomatal conductance kinetics in the future.
Reproduction assets foundThe paper's primary gas exchange, chlorophyll fluorescence, and leaf temperature phenotyping data are explicitly deposited in the WUR data repository (DOI 10.17887/WUR01-TMWYJN), stated in the Data availability section. No author analysis code repository is stated; the agricolae R package is a generic library, not a论文-Dataset · publicThe primary data and associated metadata are publicly available through the WUR data repository at https://doi.org/10.17887/WUR01-TMWYJN .Open asset ↗WUR data repository · 10.17887/WUR01-TMWYJNlines:406-446Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 15 Sept 2026
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.
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.
Plant photosynthesis operates under naturally fluctuating light, yet its dynamic responses across timescales remain incompletely understood. Here, we apply sinusoidal light modulation as a controlled periodic input and analyze the response in the frequency domain, enabling quantitative system identification of photosynthetic dynamics. Using a minimal biochemical model of photosynthetic electron transport and regulation, we show that photosynthetic performance under fluctuating light differs systematically from that under constant illumination, even when the mean photon flux density is identical. Large-amplitude oscillations generate higher harmonics and alter time-averaged chlorophyll fluorescence, oxygen evolution, and non-photochemical quenching (NPQ), demonstrating that fluctuating light acts not merely as a perturbation but as a distinct physiological regime. For sufficiently small perturbations, the system behaves approximately linearly and can be characterized by transfer functions and Bode plots. We identify two dynamic regimes separated by a characteristic timescale of approximately 10 s. In the high-frequency domain, the response is governed by constitutive photochemical processes and reflects local steady-state properties, including the redox state of the plastoquinone pool. In the low-frequency domain, adaptive regulatory feedback dominates, particularly NPQ, which reshapes both the amplitude and phase of the photosynthetic response. Characteristic frequency-response features, including gain transitions and phase extrema, provide direct information about physiologically relevant quantities such as effective relaxation times and regulatory coupling strengths. We further introduce the concept of regulation fingerprints, defined as ratios of transfer functions between regulated and unregulated systems. These fingerprints reveal distinct spectral signatures of fast PsbS-dependent and slower zeaxanthin-dependent NPQ, enabling their quantitative separation and providing experimentally testable predictions for regulatory dynamics. Together, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light. More broadly, they suggest that fluctuating illumination, often regarded as experimental noise, can instead serve as a structured probe of photosynthetic function in both laboratory and field environments.
Carbon monoxide (CO) functions as a critical signaling molecule in both mammalian inflammation and plant stress responses. However, existing techniques face challenges in real-time monitoring of CO dynamics across biological kingdoms. Here we developed Z2CO, a xanthene-based red-emitting fluorescent probe constructed on a Pd(0)-triggered Tsuji-Trost allylic cleavage mechanism. Upon CO recognition, Z2CO generates a distinct turn-on fluorescence signal at 625 nm within 10 min. The probe exhibits favorable properties including an 80 nm Stokes shift, low detection limit (0.193 μM, 3σ/k criterion), excellent water solubility, and minimal cytotoxicity, making it suitable for complex biological applications. Using Z2CO, we successfully visualized endogenous CO generation in pulmonary tissues of lipopolysaccharide-induced bacterial pneumonia mice and quantitatively evaluated anti-inflammatory drug efficacy. Furthermore, we extended Z2CO to plant systems, achieving real-time monitoring of CO dynamics in cadmium-stressed edible sprouts and brassica rapa. These investigations provide direct evidence for CO involvement in heavy metal-triggered signal transduction networks. Collectively, Z2CO constitutes a versatile tool for elucidating CO-mediated physiological and pathological processes across animal and plant systems.
Climate change-induced drought increasingly constrains water management in mixed-species urban gardens, requiring scalable and non-destructive approaches. This study proposes an integrated framework combining chlorophyll fluorescence, RGB image indices, and machine learning to classify plant drought response patterns. Ten garden plant species were evaluated under varying soil moisture conditions. Hierarchical cluster analysis integrating fluorescence parameters and RGB indices identified three physiologically defined response clusters, and their reproducibility using RGB indices alone was assessed. A total of 1,629 samples were augmented to 1,881 using the synthetic minority over-sampling technique (SMOTE) to address class imbalance. A support vector machine (SVM) model with a radial basis function kernel, using green leaf index (GLI), normalized green-red difference index (NGRDI), blue-green pigment index (BGI), and soil moisture (%) as predictors, achieved an accuracy of 0.91 and a Kappa coefficient of 0.84. In contrast, PLS-DA showed lower performance (accuracy 0.79, Kappa 0.65), indicating limited separability under linear assumptions. These results demonstrate that RGB indices combined with nonlinear models were able to reproduce physiologically defined drought response patterns under the given conditions. As a proof of concept, this study demonstrates the potential of the proposed framework; however, its generalizability is limited by the controlled greenhouse setting, the relatively small number of species, and the lack of external validation in heterogeneous field environments. The framework may provide a cost-effective approach for classifying plant drought responses and has the potential to support the grouping of plants with similar water requirements, which could contribute to improved irrigation management in mixed-species gardens under further validation.
Reproduction assets foundThe paper explicitly states that the authors' analysis code (data processing, feature extraction, SVM/PLS-DA modeling) is publicly deposited on Zenodo with a DOI matching an allowed URL. The phenotype datasets are only said to be in the manuscript/supplementary files, so the code deposit is the qualifying paperSpecificCode · publicThe code supporting the findings of this study, including data processing, feature extraction, and machine learning modeling is available at Zenodo: https://doi.org/10.5281/zenodo.19127295 .Open asset ↗Zenodo · 10.5281/zenodo.19127295lines:98-116Plant phenotyping relevance matchCrossref · checked 13 Sept 2026
Abstract The plant plasma membrane is a highly dynamic structure that is crucial for cell compartmentalization, the maintenance of (bio)chemical gradients, signaling and cell growth and responses to stress. In plants, plasma membranes are tightly connected to the cell walls that encase them. These cell walls can act as diffusion barriers and prevent the use of a wide range of synthetic fluorescent probes that have been developed to study animal cell membranes, which lack a cell wall, with live functional imaging. Here, we introduce LipoTag, a minimal chemical motif that, upon chemical conjugation, transforms hydrophobic fluorophores into water-soluble, membrane-targeted probes that can permeate plant cell walls to reach their intended location. LipoTag uses a localized positive charge in combination with a short aliphatic spacer to direct cargo to the plasma membrane. We used LipoTag to develop a suite of membrane-specific fluorescent probes that work in walled organisms beyond the plant kingdom. In addition, we used LipoTag to develop functional reporters for the quantitative imaging of membrane density, lipid order and membrane oxidation in living plant tissues. LipoTag forms a modular platform for exploring the plant plasma membrane with a suite of contemporary imaging modalities.
Sushil S. Changan · Pratapsingh S. Khapte · Priti S. Rathod · S SREEDEVI CHAVAN · Vijaysinha Kakade · Amrut S. Morade · Yogesh P. Khade · S. Gurumurthy · Chetan S. Sonawane · Ajay Kumar Singh · K. Sammi Reddy
Desiccation tolerance is a critical adaptive trait that enables plants to survive extreme water loss, yet its physiological basis in tomato and its wild relatives remains poorly understood. In this study, chlorophyll a fluorescence imaging was used as a reliable tool to evaluate photosystem II (PSII) response to progressive desiccation. The analysis was conducted in cultivated tomato (Solanum lycopersicum) and five wild relatives (Solanum chilense, Solanum habrochaites, Solanum peruvianum, Solanum pimpinellifolium, and Solanum pennellii). Detached leaves were subjected to controlled desiccation for up to 50 h. During this period, tissue moisture content (TMC), relative water content (RWC), PSII photochemical efficiency [Fv/Fm; maximum quantum yield (QY_max)], minimal fluorescence (F0), maximal fluorescence (Fm), and variable fluorescence (Fv) were monitored to assess changes in photosynthetic performance. Desiccation caused a significant, moisture-dependent decline in PSII efficiency across all species, with QY_max showing a strong linear relationship with RWC (R2 = 0.80–0.90). Interspecific variation was evident as S. chilense, S. habrochaites, S. peruvianum, and S. pimpinellifolium exhibited rapid PSII impairment, while S. lycopersicum showed moderate tolerance. In contrast, S. pennellii maintained higher PSII stability, with 50% loss of efficiency occurring only at lower RWC (30–35%). Overall, chlorophyll fluorescence imaging effectively captured functional diversity in desiccation tolerance, highlighting S. pennellii as a valuable genetic resource for improving drought resilience in tomato.
Monitoring mercury ion (Hg 2+ ) accumulation and its phytotoxicity in plants requires analytical methods that provide both spatial and functional information beyond simple destructive quantification. We report HBTD-Hg , a dual-target fluorescent probe engineered to anchor to the cell membrane and exhibit a selective fluorescence turn-on response to Hg 2+ . This design enables simultaneous in situ imaging of Hg 2+ distribution and real-time assessment of membrane integrity in live plant tissues. The probe quantifies Hg 2+ with a detection limit of 49.7 nM. Importantly, it allows for the nondestructive tracking of Hg 2+ uptake dynamics directly through leaf imaging. Furthermore, it directly visualizes the ensuing Hg 2+ induced loss of cellular membrane integrity, including distinct vesiculation. This work provides a versatile tool for the real-time assessment of heavy metal stress, effectively linking environmental ion detection to observable cytological damage in plants.
Root exudation mediates the delivery of plant primary and secondary metabolites into soil, where they regulate plant–microbe interactions and terrestrial carbon cycling. Conventional exudate analyses quantify total root-released carbon yet obscure the spatial origin and rhizosphere influence of individual compounds. Here, we develop a rhizobacterial biosensor platform, named Suc-MAPP, to map local exudate profiles along the surface of colonized root tissues. Focusing on sucrose, we engineered sfGFP-based, sucrose-responsive gene circuits in Pseudomonas putida KT2440 for live imaging of exudate concentrations in the micromolar range. These biosensors reveal spatially structured sucrose exudation patterns across eudicots and monocots and implicate photoassimilated source–sink dynamics as a major determinant. We further apply this platform to phenotype exudation modulated by synthetic gene circuitry in Arabidopsis thaliana , identifying genetic design rules for graded sucrose release and quantifying how engineered export sculpts rhizosphere assembly of a defined bacterial community. Together, these results establish programmable rhizobacterial biosensors as tools to spatially resolve plant–environment carbon exchange in situ and provide a framework for extending this approach to diverse exudate targets.
Evaluating the drivers of variation in plant thermal tolerance limits requires a clearer understanding of how methodological matters can lead to different tolerance estimates. Chlorophyll fluorometry – to measure the temperature-dependent change in F V / F M – is a well-established approach to derive tolerance thresholds of photosystem II (PSII) in plants, but one-off, time-specific thermal exposures do not consider the fundamental dose-dependent effect of heat. The resurgent thermal death time (TDT) approach integrates both the temperature intensity and the exposure duration to derive time-based critical temperature thresholds and sensitivity parameters. We build upon this foundation to develop a protocol for evaluating thermal load sensitivity (TLS; non-lethal heat stress) of PSII in plants. Through five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery. There were dramatic changes in tolerance threshold estimates based on thermal load (i.e. dose-dependent) effects on F V / F M , and strong effects of light intensity during heat and the presence of light post-heat. We offer recommendations pertaining to method implementation and discuss future empirical avenues. Appraising cumulative heat stress will enhance the utility of thermal tolerance estimates – the TLS approach outlined here moves us toward a new standard.
Accurate prediction of grain yield is essential for enhancing food security, particularly in the context of climate change. Although remote sensing indices have been extensively utilized to monitor vegetation growth and estimate crop yields, there has been limited research comparing their effectiveness for predicting grain yield, especially across different growth stages. This study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages at Shangshan Rice Research Station in Zhejiang Province, China. The results indicated that SIF exhibited the strongest and most consistent correlation with grain yield ( R 2 = 0.34 to 0.75), followed by NIR V ( R 2 = 0.34 to 0.71). SIF also demonstrated advantages in capturing the dynamic changes of GPP during the reproductive period. During both the vegetative and reproductive stages, leaf area index (LAI) showed significant correlations with NDVI, NIR V , and SIF, whereas leaf chlorophyll concentration exhibited comparatively weaker associations with these indicators. These findings provide valuable insights for improving crop yield forecasts using remote sensing, thereby contributing to enhanced agricultural management and food security strategies under climate change.
Chlorophyll fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength chlorophyll fluorescence light detection and ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech ( Fagus sylvatica ) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress, and can be measured using simple, non-invasive methods. Therefore, it is a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we present a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for remote plant stress assessment and photosynthesis research. The sensor utilizes laser light to induce ChlF and employs an in-phase/quadrature (I/Q) lock-in amplification method to isolate weak fluorescence signals from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions while simultaneously determining leaf distance. Our results demonstrate that the sensor can reliably detect ChlF at distances up to 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water and high-light stress demonstrate the sensor's ability to detect changes in ChlF indices due to plant stress.
Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Traditional methods for identifying salt tolerance levels in soybean varieties are often cumbersome, time-consuming, and labor-intensive. These challenges are further exacerbated by the limited utility of chlorophyll fluorescence imaging phenotype data, which are insufficiently diverse and difficult to analyze. Additionally, the corresponding parameter text data have not been fully explored and utilized. In this study, salt stress experiments were conducted on 178 soybean varieties, and a multimodal dataset comprising chlorophyll fluorescence images and corresponding textual data was constructed using a chlorophyll fluorescence imaging instrument. A novel gated mechanism network for learnable image-text interaction (Mm-VitnNet) is proposed, which enables global cross-modal interaction between image and text data. The model introduces a gated mechanism to dynamically regulate the fusion intensity of cross-modal information and incorporates two learnable tokens that focus on feature learning for each individual modality. This approach effectively mitigates interference between modalities while preserving modality-specific features, thereby enhancing model performance. The proposed model demonstrates an accuracy rate of 98.97%, significantly outperforming typical models: it improves by 1.09 and 2.33 percentage points compared to CNN-based models such as EfficientNetV2-s (97.88%) and MobileNetV2 (96.64%), respectively, and by 3.21 and 2.60 percentage points compared to Transformer-based Swin Transformer_tiny (95.76%) and hybrid models like MobileViT_S (96.37%), respectively. The model has 10.22M parameters and a computational cost (FLOPs) of 1.84G, which is significantly lower than models like VGG and ResNet50, and only slightly higher than some lightweight CNNs, achieving an effective balance between accuracy and efficiency. The improved model demonstrates notable performance in identifying samples with varying salt tolerance levels, even under limited computational resources, ensuring reliable classification performance. Moreover, this multimodal non-destructive identification method based on chlorophyll fluorescence technology offers an efficient and feasible approach for assessing the salt tolerance levels of soybeans, while also advancing agricultural phenotyping towards greater precision and intelligence.
Summary Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo . Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap. Significance statement Multiplexing genetically encoded biosensors in plants has been limited by overlapping fluorescent signals and strong autofluorescence. This study presents an optimized framework for linear unmixing and provides a MATLAB-based organelle segmentation tool, allowing precise quantification of multiple fluorescent reporters in vivo and advancing real-time visualization of complex cellular processes in plants.
Reproduction assets foundThe paper deposits raw confocal image data on Zenodo (10.5281/zenodo.19691651) and a MATLAB nuclei segmentation/quantification script on GitHub. Only the GitHub repository URL appears in the allowed URL list, so the code asset is reported; the Zenodo image deposit is noted but cannot be listed without a matching URL.Code · publici (ORCID: 0000-0002-6235-2816)
14
15 DATA AVAILABILITY
16 Raw image data supported with metadata were deposited to Zenodo:
17 10.5281/zenodo.19691651and can be opened with LAS X available at https://www.leica-
18 microsystems.com/products/microscope-software/p/leica-las-x-ls/downloads/. MATLAB script
19 was deposited to GitHub: https://github.com/NIB-SI/Nuclei-segmentation.
20 FUNDING
21 This research was funded by the Slovenian Research and Innovation Agency (research core
22 funding No. P4-0165, P4-0463, projects J4-1777, J4-60073, J4-70169 and ARIS program for
23 young researchers).
24 CONFLICT OF INTEREST
25 The authors declare no conflicts of interest. This article does not contain any Open asset ↗NIB-SI/Nuclei-segmentationpdf-layout-page:1 lines:1-34Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
This paper investigated the feasibility of snapshot multispectral fluorescence imaging for nondestructive identification of cold stress in pepper plants. Fluorescence spectra were obtained by exciting the plant with a 405 nm ultraviolet LED. The plants were grown under three temperature conditions: 17 °C (control), 10 °C (moderate cold stress), and 5 °C (severe cold stress). Raw fluorescence spectra extracted from the demosaiced snapshot images were used as inputs for a deep-learning pipeline consisting of feature extraction, an encoder-decoder GRU, and a multilayer perceptron (MLP), and the results were compared with conventional machine learning classifiers, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and a Gaussian support vector machine (G-SVM). Tukey's HSD test indicated that the proposed deep-learning model achieved the highest cross-validation accuracy and consistently produced superior classification metrics (accuracy of 85.7%, precision of 85.3%, recall of 85.3%, F1-score of 85.2). The trained model was further applied to hyperspectral cubes to generate classification maps; however, moderate misclassification was observed, consistent with the overall prediction performance.
ArabidopsisChlorophyll fluorescenceMicroscopyLiDAR / point cloudCell / cellular structureRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.
Raymond Wightman · Gareth Evans · Aram Gurzadyan · Ankiit Ahluwalia · Siyu Miao · Henrik Jonsson · Katharina Schiessl
Laboratory / benchtopChlorophyll fluorescenceMicroscopyCell / cellular structureFlowerRootTissueVisualization / data management
Abstract Cryo‐scanning electron microscopy (CryoSEM) permits the preparation and detailed imaging of bulky samples while keeping them in a hydrated state. For plant biology, cryofractures give information on cell ultrastructure and tissue organisation within a much larger context that is the whole organ or organism. To date, a method to locate fluorescence reporters on the cryofracture has not been reported. Our approach uses a stereofluorescence microscope with an 80 mm working distance and a high‐zoom ratio to image the fracture through a viewing port of the cryopreparation chamber while the sample is still frozen and under vacuum. We have applied this method to look at fluorescent reporters of auxin transport and signalling in plant shoot apices and seedlings, the expression of a poorly characterised gene in the young floral pedicel and nitrogen‐fixing rhizobial bacteria, expressing GFP, in root nodules. This method is applicable to any cryopreserved bulky sample that has a fluorescent output and paves the way for correlative light‐electron microscopy for cryoSEM‐based imaging.
Introduction Hydrogen peroxide (H 2 O 2 ) functions as a key signaling molecule in plants responding to stress. Although numerous detection methods have been developed, simple and non-destructive techniques for the semi-quantitative monitoring of H 2 O 2 in plant tissues remain scarce. Methods In this study, we developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues, and conducted spectroscopic and in vivo toxicity tests. Furthermore, under experimentally controlled stress conditions, we utilized this probe to detect H 2 O 2 levels in four distinct plant types exposed to salt, waterlogging, cadmium, and drought stresses. Additionally, H 2 O 2 was detected in a grafting model under non-experimentally controlled stress conditions. Results The results showed that the probe demonstrated excellent selectivity, a strong linear correlation (R2 = 0.9849), and a low detection limit of 0.6450 μmol/L. Importantly, it exhibits good biocompatibility with plant tissues and effectively minimizes detection errors caused by transient H 2 O 2 fluctuations induced by environmental changes. Consequently, it provides more accurate and stress-reflective H 2 O 2 measurements. Under experimentally controlled stress conditions, the changes in relative fluorescence intensity conformed to the typical response patterns observed when plants experience graded levels of stress. Notably, even under complex grafting conditions without imposed stress gradients, applying the probe to bottle gourd (Lagenaria siceraria) rootstocks with different graft compatibility produced fluorescence dynamics consistent with the typical H 2 O 2 responses of compatible and incompatible rootstocks, and the distribution of relative fluorescence intensity within the population underscored the importance of prescreening plants for biological studies. Pearson correlation and Bland-Altman analyses confirmed good agreement between our method and the commercial assay kit. Discussion These results demonstrate that the LWS probe enables H 2 O 2 detection and, in combination with the IVIS in vivo imaging system, can screen individual plants differing in stress responses more effectively than other sensors. This non-destructive approach preserves the structural integrity of plant samples, enabling follow-up physiological, biochemical, and genomic analyses on the same specimens. This method provides a reliable prescreening platform for investigating plant stress responses at the biological level.
Abstract Accurate tracking and measurement of pollen dispersal in the atmosphere are essential for assessing cross-pollination risks, particularly in the case of genetically engineered (GE) crops. We conducted a series of unique release-recapture field studies with GE switchgrass in Oliver Springs, Tennessee, USA. Two hundred transgenic switchgrass plants ({\it Panicum virgatum L.} `Performer') were planted at the center of a clear-cut field, with one block of 100 plants expressing orange fluorescent protein (OFP) under a maize ubiquitin promoter (PvUBI1) and another block of 100 plants expressing OFP driven by a maize pollen-specific promoter (Zm13). Pollen was sampled from the atmosphere using fixed (ground-based) and mobile (drone-based) sampling devices at different distances from the source field, with Lagrangian Stochastic dispersal simulations run for sampling periods using high-resolution wind measurements. The pollen emission rate was estimated by combining simulated and measured pollen concentrations, and strong diurnal trends were observed. Diurnal emission rate trends were positively correlated with wind speed, temperature, and vapor pressure deficit, while negatively correlated with relative humidity. In low-wind meandering conditions, incorporating changing wind direction into the dispersal modeling improved pollen emission rate estimation and model-measurement comparisons. This study assesses the effectiveness of high and low volume pollen samplers in relation to source strength up to 1 km from the source, enhancing understanding of pollen measurement techniques. Additionally, it is a proof-of-concept for drone-based pollen sampling and GMO pollen tracking using fluorescence measurements. Results from our experiments have significant implications for cross-pollination risk assessment, prediction, and management of airborne allergens.
Reproduction assets foundThe authors state that all sampling data, modeling code, and simulation results from this switchgrass pollen dispersal study are publicly available in a Virginia Tech figshare repository. The GitHub 3D-printing files are cited prior work (Powers et al. 2018), not a paper-specific asset.Dataset · public737 Statements and Declarations
738 Data and code availability
739 All sampling data, modeling code, and simulation results are made available in the
740 Virginia Tech Data repository:
741 https://figshare.com/s/54a308163b60865d55bf.
742 Competing interests
743 The authors have no competing interests to declare.
744 Funding
745 This work is supported in part by the Biotechnology Risk Assessment Program, project
746 award no. 2019-33522-29989, from the U.S. Department of Agriculture’s National
747 Institute of Food and Agriculture.
748 References
749 AdamovOpen asset ↗figsharepdf-layout-page:28 lines:1-46Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana , a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A, qE Z, qE L, qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Reproduction assets foundThe paper's fluorescence lifetime/pigment phenotyping data and the NPQ model code are both publicly deposited on Zenodo (DOI 10.5281/zenodo.16755870), per explicit Data availability and Code availability statements.Dataset · publicThe data supporting the findings of this study are available within the article and at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code · publicThe codes for NPQ models used in this study are available at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
Photosynthetic light-harvesting complexes mediate light absorption and energy dissipation. By modulating the photosystems' absorption cross-section, they affect both photosynthetic activity and non-photochemical quenching (NPQ). These processes are often studied by spectrally integrated chlorophyll fluorescence, masking their associated spectral information. We explore in Aspen and Arabidopsis npq mutants how qE affects the development of NPQ spectra under two contrasting conditions: in the absence and the presence of photoinhibition. We introduce a new parameter, the development of new emitting species (NESD), during time- and spectrally resolved NPQ inductions, and develop a pipeline to resolve PSII energy-partitioning heterogeneity. LHCII, PsbS, and zeaxanthin are required for NESD. Combining gas exchange, P700 oxidation, and spectrally resolved kinetics, we show that under photoinhibitory conditions, NES can develop even without PsbS or zeaxanthin, producing sustained quenching independent of photoinhibition of PSII or PSI. Furthermore, the absence of LHCII and CURVATURE THYLAKOID 1 leads to increased photoinhibition, indicating that long-term photoprotection relies on LHCII and thylakoid plasticity, whereas PsbS and zeaxanthin mainly facilitate LHCII-dependent quenching. Finally, we show the limitations of traditional parameters in discriminating between photoinhibition and photoprotective sustained quenching and propose time-resolved monitoring of CO₂ assimilation and Y(II) for their accurate assessment.
ABSTRACT Early detection of herbicide‐induced stress is essential for optimising weed control, improving crop safety and advancing precision agriculture practices. While non‐destructive imaging technologies offer great potential for rapid stress diagnosis, direct comparisons of their performance across species, herbicides and application rates remain scarce. This study systematically compared three high‐throughput imaging methods: chlorophyll fluorescence imaging, multispectral imaging and 3D multispectral scanning, for their ability to detect and classify early physiological and morphological responses to bentazone (HRAC 6) and glyphosate (HRAC 9) in a weed (velvetleaf, Abutilon theophrasti ) and a crop species (common bean, Phaseolus vulgaris ). Plants were imaged daily, immediately before treatment and for five consecutive days after herbicide application. Chlorophyll fluorescence parameters, particularly NPQ, q P and F s ′, emerged as the earliest and most sensitive indicators, detecting stress within 24 h of treatment. Stepwise discriminant analysis revealed that intermediate time points (48–72 h after application) achieved the highest classification accuracies, reaching up to 100%, with chlorophyll fluorescence as the dominant trait for selection. Multispectral traits, such as reflection in red and far red, and saturation, together with morphological traits like total leaf area and leaf inclination, provided complementary information and enhanced discrimination accuracy among herbicides. These findings highlight the superiority of chlorophyll fluorescence imaging for detecting early herbicide stress, while demonstrating the added value of integrating spectral and morphological traits. The results support the development of multi‐sensor phenotyping pipelines for rapid, accurate and field‐adaptable diagnostics in both weed and crop management.
Accurate retrieval of plant functional traits is critical for monitoring crop growth and improving agronomic management. Canopy structural parameters, such as leaf area index (LAI) and leaf inclination distribution function (LIDFa), strongly influence inversion accuracy. Quantifying canopy structural uncertainties and developing strategies to improve retrieval accuracy are crucial. In this study, we developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data. Using both simulation modelling and field measurements in NEON STER crop field, we introduced multi-level prior noise and evaluated how uncertainties in LAI and LIDFa propagate into the retrieval of chlorophyll content (Cab), maximum carboxylation rate (Vcmax), and fluorescence quantum efficiency (fqe). To assess the influence of canopy structure and improve retrieval accuracy, three inversion strategies—Prior-Matched (PM), Regularized (RI), and No-Prior (NP)—were designed and tested for their accuracy and robustness. The results showed that second-order Sobol’ indices (S2) captured interactions among canopy structural parameters and functional traits, particularly between Cab-LAI, Cab-LIDFa and fqe-LAI, with sensitive spectral ranges at 680–740 nm (fluorescence) and 600–720 nm (reflectance). Error amplification analysis under six noise levels showed that structural uncertainties significant amplified reflectance and fluorescence variations, with red-edge shifts (ΔRE) and 740 nm fluorescence changes (ΔF740) being most sensitive. Incorporating prior canopy structure information improved inversion accuracy by up to 7.93 % in R² and reduced RMSE by 21.25 %, although this advantage diminished under high noise levels. LAI uncertainty had a greater impact than LIDFa, and additive noise introduced more uncertainty than multiplicative noise. Comparison of the inversion strategies revealed that the RI strategy achieved higher accuracy (simulated data: R²=0.954; measured data: R²=0.799) and greater robustness to noise than the PM strategy. These findings demonstrate the value of integrating canopy structure into computational inversion models to enhance the reliability of remote sensing trait retrieval, supporting precision agriculture and sustainable crop production.
Serotonin, widely recognized as a mammalian pineal hormone, is also present in plants, yet its in vivo dynamics and physiological roles remain poorly understood due to the absence of real-time sensing tools. Herein, we report nitrogen-doped carbon quantum dot (N-CQD) nanosensors (∼5 nm; quantum yield 36%) for the selective detection and visualization of serotonin in plant systems. The sensing mechanism involves static-dominated mixed fluorescence quenching accompanied by a blue shift, corroborated by UV-Vis spectral changes, Stern-Volmer analysis, and fluorescence lifetime decay. The nanosensor exhibits a low detection limit of 0.391 µM and a linear response range of 4.74-75 µM. Using Arachis hypogaea seedlings as a model, stronger and more consistent serotonin-dependent fluorescence responses were observed compared with those in other plant species, enabling reliable in vivo monitoring. Real-time sensing revealed a condition-dependent regulatory role for serotonin, including growth inhibition under non-stress conditions and growth enhancement under stress, indicating a dual function in stress adaptation. Fluorescence microscopy further confirmed the intracellular association of serotonin with N-CQDs, providing direct visual evidence of its localization. This work establishes a nanosensor-based platform for real-time detection of serotonin in plants and advances understanding of serotonin-mediated signalling in plant growth and stress responses.
Haley Schuhl · Keely E. Brown · Hudanyun Sheng · Parag K. Bhatt · Jorge Gutierrez · Dominik Schneider · Anna Casto · Lucia Acosta‐Gamboa · Joe Ballenger · Fabio Barbero · Jackson Braley · Autumn M. Brown · Leonardo Chavez · Shannon S Cunningham · Malinda Dilhara · Adam M. Dimech · Joseph G. Duenwald · Annika Fischer · Jared Gordon · Chloe Hendrikse · Gabriela L Hernandez · John G. Hodge · Martina Huber · Brandon M. Hurr · Sanaz Jarolmasjed · Karina Medina Jiménez · Samuel Kenney · Grant Konkel · Alexander Kutschera · Sunita Lama · Matthew Lohbihler · Argelia Lorence · Collin Luebbert · Nathaniel Ly · Heather C. Manching · Annarita Marrano · Susan Meerdink · Nicholas M. Miklave · Pavan Mudrageda · Katherine M. Murphy · J. David Peery · Ronald Pierik · Seth Polydore · Caleb Robey · Tess S. Rogers · Thia Schultz · Eliza Seigel · Dhiraj Srivastava · Stephan Summerer · Josh Sumner · Chong Teng · Adriane E. Thompson · José C. Tovar · Tim van Daalen · Mark Watson · John Wheeler · Mark C. Wilson · Kaitlyn Ying · Alina Zare · Yutai Zhou · Gehan Malia · Noah Fahlgren
Abstract 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 deposits the authors' analysis scripts on GitHub (danforthcenter/plantcv-4-paper), which directly reproduces this paper's phenotyping analyses.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:1-97Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Lipid droplets (LDs) play pivotal roles in crop physiology, stress adaptation, and product quality by serving as dynamic reservoirs of energy and essential nutrients. However, the lack of rapid and accurate methods for on-site quantitative detection of LDs has hindered their comprehensive analysis in agricultural systems. Herein, we report the rational design and synthesis of three near-infrared (NIR) fluorescent probes, YD-1 , YD-2 , and YD-3 , for the sensitive and specific detection of LDs in crops. These probes feature a hydrophobic donor-π-acceptor (D-π-A) framework comprising a 7-diethylaminoquinoline electron donor and distinct electron-accepting groups, including (5,5-dimethylcyclohex-2-en-1-ylidene)malononitrile (DCM), 4-fluorophenyl, and phenyl moieties. Among them, YD-1 exhibited the most pronounced fluorescence response to LDs, displaying intense NIR emission within LDs and remarkable quenching in non-LD regions, enabling high-fidelity LD imaging. TD-DFT calculations revealed that the superior sensitivity of YD-1 originates from its efficient intramolecular charge transfer (ICT) process, which is highly responsive to the polarity differences between LDs and their surroundings. YD-1 was successfully applied to monitor LDs dynamics in living cells, zebrafish under a high-fat diet, and soybeans at different growth stages. Furthermore, a custom-built mobile fluorescence analysis device was developed and coupled to probe YD-1 to achieve rapid, on-site quantitative detection of LDs in crop samples. This work provides a powerful analytical platform for on-site monitoring of LDs dynamics, offering insights into crop lipid metabolism and quality control.
Valentin Michels · Simon De Cannière · Gina Lopez · Maximilian Weigand · Kevin Warstat · Sabine Seidel · Onno Muller · Uwe Rascher · Harry Vereecken · Andreas Kemna
MaizeField / plotChlorophyll fluorescenceRootStem / branchPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyPhotosynthesis / fluorescenceWater status / transpiration
The soil-plant-atmosphere continuum ( SPAC ) plays a critical role in the distribution of water and nutrients in terrestrial ecosystems. To understand the complex and rapid dynamics within the SPAC , it is necessary to observe its components with sub-daily resolution. While measurements of above-ground processes are frequently employed, monitoring of the below-ground part remains scarce due to its inaccessibility. In this study, we monitored water and nutrient transport processes in a maize field over several months. The rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity. Stem water transport and photosynthetic activity were measured with sapflow sensors and a fluorescence sensor, respectively, while atmospheric conditions were measured with a weather station. Timeseries were analyzed using cross and coherence wavelet analysis. Electrical imaging results revealed spatially and temporally resolved daily variations in subsurface conductivity and polarization properties, suggesting a sensitivity to water and ion uptake processes. Conductivity development was strongly correlated with SWC dynamics controlled by evaporation and water uptake of plants. Wavelet power showed that belowground polarization diurnality was consistent with a typical growth pattern of maize, and disappeared shortly after harvest. Cross wavelet analysis of sun-induced fluorescence, sapflow density, photosynthetically active radiation, and vapor pressure deficit revealed lags caused by environmental conditions, highlighting the coupling of plant activity to the atmosphere. Our results show that sEIT is a valuable tool to study rhizosphere processes and may aid in the holistic modeling of the SPAC .
Close-range spectral imaging provides technical support for leaf detection during the early infection process of SCLB (southern corn leaf blight). However, due to the randomness of pathogen infection and the low visibility of early lesions, the temporal spectral signals obtained using this technique have poor continuity and low sensitivity. To improve the ability of temporal spectral signals to detect early-stage infection, this study proposes a signal extraction method based on reverse temporal spectral image matching, and a signal decoupling method based on DSO-CWT (decomposition of scale-optimized continuous wavelet transform) is also proposed to improve signal sensitivity. First, the spectral images were preprocessed. Image entropy was used to quantify the changing patterns of symptoms in early SCLB infection. Second, a temporal spectral signal extraction method based on ASpanFormer (adaptive span transformer) reverse temporal spectral image matching is proposed. The calculation results of LPIPS (learned perceptual image patch similarity) indicates that the average matching error between adjacent periods is less than 0.2, which suggests that this method can enhance the extraction accuracy of weak temporal spectral signals in early infection. Third, the T-test was used to evaluate the detection sensitivity of temporal spectral signals at different early stages of infection. The results showed that the temporal spectral signal still had low sensitivity for detecting different stages of infection. Therefore, a temporal signal decoupling method based on DSO-CWT is proposed, which enhances the detection sensitivity of temporal spectral signals by performing time–frequency domain conversion. Finally, a diagnostic model for the early SCLB infection was established by fusing fluorescence and reflectance spectral signals. After DSO-CWT processing, the accuracy of the modelling set improved from 47.62% to 94.22%, and the accuracy of the validation set was improved from 47.62% to 91.27%. This study improves the extraction accuracy and detection sensitivity of temporal spectral signals for early SCLB infection by using signal extraction based on reverse temporal spectral image matching and deep signal decoupling based on DSO-CWT, providing new insights for early detection of SCLB infection.
Far-red light (FR, 700-800 nm) can enhance photosynthesis by stimulating photosystem I (PSI). However, during chlorophyll fluorescence (CF) measurements using pulse-amplitude modulation (PAM) fluorometry, unusually high quantum yields of photosystem II ( Φ PSII ) have been observed under high FR light intensities, raising concerns about measurement artifacts. To test this, we constructed light response curves for sweet basil ( Ocimum basilicum L.) grown under light-emitting diode (LED) light (R:G:B = 44%:18%:38%) with varying photosynthetic photon flux densities (PPFD, 0-1,000 μmol m -2 s -1 ) and FR fractions (0, 0.26, 0.45, and 0.63). FR treatments consistently increased Φ PSII , but when total photon flux density (TPFD, 400-800 nm) exceeded 1,000 μmol m -2 s -1 , Φ PSII rose abruptly. Nonfluorescent reference tests using white and black paper confirmed that FR induced spurious fluorescence signals, likely due to spectral overlap between FR photons and the PAM detection range (680-760 nm). Tilting the LED panel to reduce reflected FR eliminated the abrupt Φ PSII peak but introduced unexpectedly increased Φ PSII across treatments, likely due to probe-induced shading. These findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology. Accurate and reliable assessment of photosynthetic performance under extended spectral lighting conditions requires careful management of lighting geometry and FR intensity.
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Plant phenotyping relevance matchEurope PMC · checked 5 Sept 2026
The legume-rhizobium symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (P nifH ) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) systems. We show that P nifH -driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and P nifH -driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify P nifH -driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobium symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. Importance The legume-rhizobium symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (P nifH ) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate ( Lotus japonicus-Mesorhizobium japonicum ) and indeterminate ( Pisum sativum-Rhizobium leguminosarum ) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm'), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s -1 , autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.
To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye's parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates' "multi-scale parallelism-global modulation-long-range integration," multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network's backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems.
Reproduction assets foundThe paper's Data Availability Statement states the original code is openly available in IEEE DataPort at the allowed DOI URL, making the authors' analysis code a paper-specific public asset.Code · publicThe original code presented in the study are openly available in IEEE DataPort at https://dx.doi.org/10.21227/v32y-mv49.Open asset ↗IEEE DataPort · 10.21227/v32y-mv49html-lines:829-851Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Chemical imaging at high spatiotemporal resolution is crucial for advancing plant sciences and biotechnology. We demonstrate optical nanosensors for subcellular imaging of signaling molecules (H 2 O 2 ) and lipid corona formation in plant tissues at high spatial ( 2 O 2 waves (100 μM) from plant mesophyll to stomata and pavement cells. Ca 2+ induced higher endogenous H 2 O 2 in mesophyll cells, whereas organelle electron transport chain disruptors and salt stress generated similar H 2 O 2 across all leaf cell types. Furthermore, the nanosensor quenching kinetics in photosynthetic mesophyll (0.018 s -1 ) and epidermal (0.004 s -1 ) cells enabled the detection of plant lipid corona formation. Optical nanosensors elucidate spatiotemporal dynamics of plant signaling molecules and advance our understanding of biocorona formation.
Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Here, we describe an in vivo dye-tracking method for measuring phloem transport velocity in seedlings, leaves, and petioles and potentially other translucent plant tissues. The method requires measurement of the fluorescent signal of a phloem-mobile dye using sensitive photo-sensors placed externally to the plant. Following dye application, velocity is determined by either following a dye pulse or using laser fluorescence bleaching. Velocity is estimated by dividing the distance traveled by the dye by the time it takes to travel. This method can be used to measure phloem transport velocity on intact plants with minimal disturbance and has the potential to be used under a variety of growth conditions. Because there are large differences among species in their anatomy, this method should be optimized for individual plants and tissue types.
Biofeedback control of light‐emitting diode (LED) lighting based on real‐time photosynthetic performance offers a promising framework for plant‐responsive light management in controlled environment agriculture (CEA). While the short‐term feasibility of electron transport rate (ETR)‐based light regulation has been demonstrated, its long‐term performance remains untested. This study evaluated the ETR‐based biofeedback lighting control system over an entire crop cycle of lettuce under three target ETR levels (55, 90, and 125 μmol m⁻² s⁻¹) in a climate‐controlled growth chamber. The system continuously monitored the quantum yield of photosystem II (ΦPSII) and adjusted photosynthetic photon flux density (PPFD) every 15 min to maintain the target ETR, used as an indirect proxy for carbon assimilation. Target ETRs were maintained within ±2.5% with minimal variability among replicates, and the corresponding average PPFDs (means ± standard deviations) were 183.5 ± 5.4, 316.1 ± 14.3, and 457.3 ± 23.5 μmol m⁻² s⁻¹, respectively. Despite stable environmental conditions, the system dynamically responded to both diurnal and long‐term acclimation in terms of photosynthetic efficiency. PPFD was reduced during the early photoperiod, when ΦPSII was high, and increased in the late photoperiod to compensate for the decline in ΦPSII. Under the target ETR of 125 μmol m⁻² s⁻¹, ΦPSII increased over time, enabling a 14% reduction in PPFD while maintaining a stable ETR, highlighting the potential for reduced light input as plants acclimated. These results demonstrate the long‐term feasibility and stability of plant‐responsive, CF‐based biofeedback lighting control for precise and replicable regulation of photochemical energy input in CEA crop production.
Histochemical staining and microscopy-based techniques have been widely used to detect, quantify, and analyze the morphology of arbuscular mycorrhizal fungi (AMF) in roots. Here, we describe a traditional standardized method for staining of AMF in colonized roots using trypan blue, along with possible modifications to adapt the protocol to specific needs, such as root type or reducing the use of toxic reagents. We also summarize common approaches for quantifying arbuscular mycorrhizal colonization. In addition, we present a simple fluorescent staining protocol, using wheat germ agglutinin-Alexa Fluor conjugates, for high-resolution imaging of fungal colonization patterns and arbuscule morphology in roots. Finally, we describe a GUS staining method for localizing the promoter activity of plant genes potentially involved in mycorrhization, using transformed mycorrhizal hairy roots carrying promoter-GUS fusions.
Biofeedback control of light-emitting diode (LED) lighting based on real-time photosynthetic performance offers a promising framework for plant-responsive light management in controlled environment agriculture (CEA). While the short-term feasibility of electron transport rate (ETR)-based light regulation has been demonstrated, its long-term performance remains untested. This study evaluated the ETR-based biofeedback lighting control system over an entire crop cycle of lettuce under three target ETR levels (55, 90, and 125 μmol m -2 s -1 ) in a climate-controlled growth chamber. The system continuously monitored the quantum yield of photosystem II (Φ PSII ) and adjusted photosynthetic photon flux density (PPFD) every 15 min to maintain the target ETR, used as an indirect proxy for carbon assimilation. Target ETRs were maintained within ±2.5% with minimal variability among replicates, and the corresponding average PPFDs (means ± standard deviations) were 183.5 ± 5.4, 316.1 ± 14.3, and 457.3 ± 23.5 μmol m -2 s -1 , respectively. Despite stable environmental conditions, the system dynamically responded to both diurnal and long-term acclimation in terms of photosynthetic efficiency. PPFD was reduced during the early photoperiod, when Φ PSII was high, and increased in the late photoperiod to compensate for the decline in Φ PSII . Under the target ETR of 125 μmol m -2 s -1 , Φ PSII increased over time, enabling a 14% reduction in PPFD while maintaining a stable ETR, highlighting the potential for reduced light input as plants acclimated. These results demonstrate the long-term feasibility and stability of plant-responsive, CF-based biofeedback lighting control for precise and replicable regulation of photochemical energy input in CEA crop production.
ChickpeaChlorophyll fluorescenceSeed / grainPhysiological trait estimationGrowth / development / phenology
Seed germination is a critical physiological process that transforms a quiescent seed into a metabolically active seedling and is also a crucial factor in determining maximum crop production. This transition is influenced by various intrinsic and extrinsic factors. Interestingly, reactive oxygen species (ROS) plays an important role in breaking seed dormancy by oxidation of biomolecules, weakening of the testa and degradation of endosperm. Similarly, molecular internal oxygen is also considered vital for the transition of dormancy to seed germination. However, it is essential to establish a correlation between the internal oxygen and the generation of ROS during seed germination. This chapter details protocols for imaging internal oxygen concentrations using VisiSens and fluorescent detection of ROS using H 2 DCFDA in chickpea seeds, complemented by qPCR analysis of key ROS-related genes (RBOH, AOX 1, UCP 1, and NADH dehydrogenase). These findings from these methods help advance our understanding of the inverse relationship between molecular oxygen and ROS dynamics during seed germination.
Imaging technologies have become indispensable tools in modern plant phenotyping, transforming visual information into measurable traits essential for analyzing morphology, physiology, biochemistry, and micro- to nanoscale structures. This concise review summarizes recent advances by dividing plant imaging into two major categories: (1) physiological and biochemical, which includes hyperspectral, multispectral, and fluorescence hyperspectral imaging, as well as terahertz imaging, surface-enhanced Raman scattering, and carbon dot-based techniques; and (2) structural and morphological, encompassing RGB, thermal, light detection and ranging (LiDAR), confocal microscopy, and optical coherence tomography. Together, these modalities deliver insights from the canopy to the molecular level, enabling precise monitoring of plant stress, disease, and developmental traits. By integrating these multimodal imaging techniques with artificial intelligence, the review highlights key developments, current challenges, and future perspectives in plant measurement and analysis.
Wheat rusts are the most important diseases leading to substantial yield losses. Early and precise detection of wheat rusts for early mitigation and disease control is imperative. This review summarizes the advances in high-throughput phenotyping (HTP) approaches for rust detection. Additionally, various genomic interventions leading to the development of rust resistance in wheat are discussed in detail. High-throughput phenotyping (HTP) approaches enable early, non-destructive, and repeatable detection of wheat diseases. However, they need initial investment, expertise, and computational resources. RGB imaging achieves ~80% accuracy by capturing infected leaf coloration, while hyperspectral and fluorescence imaging can predict rust with over 90% accuracy, 3–8 days before visible symptoms. LiDAR, UAVs, and robotic platforms automate large-scale field phenotyping, and spectral indices (NDVI, PRI), thermal, and chlorophyll sensors detect early physiological changes. AI and machine learning models, including CNNs and SVMs, enhance diagnostic precision and reduce bias, while mobile apps, lateral flow devices, and IoT-based systems facilitate affordable, real-time rust detection and forecasting. Genomic interventions complement phenotyping, with marker-assisted selection (MAS) enabling precise tracing of rust resistance genes, and genomic selection (GS) allowing early multi-trait prediction. QTL mapping and GWAS identify major and minor resistance loci, while introgression from wild relatives and MAS reduce linkage drag and introduce novel alleles. Transgenic approaches, RNA interference (RNAi), and CRISPR/Cas9 gene editing enhance resistance through targeted gene modification, and gene pyramiding combines multiple loci for durable protection. Wheat pan-genome resources further support precise trait targeting, and speed breeding integrated with MAS, GS, or gene editing accelerates rust-resistant line development. Efficient seed system pathways ensure rapid dissemination, adoption, and resilience. The development and commercialization of rust-resistant wheat varieties under harsh climatic conditions are crucial for mitigating yield losses, reducing fungicide use, safeguarding farmer livelihoods, and ensuring sustainable food security.
Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture.
Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.
The conversion of glucosinolates (GSLs) into chemopreventive isothiocyanates (ITCs) primarily relies on plant myrosinase (MYR) or specific bacteria. MYR dynamics are deeply involved in plant defense systems, gut microbiota metabolism, and complex interactions and regulation across species. A set of activity-based probes was developed to track MYR in vivo by biomimicking natural GSL with robust sensitivity and selectivity. The dynamics and heterogeneous distribution of MYR in distinct sections and species were captured via fluorescence imaging of live plants. Specifically, under herbivore challenge to leaves, a systemic, long-distance upregulation of MYR activity in root tissues has confirmed cross-species MYR regulation in plant defense. Furthermore, for the first time, quantitative visualization of the dynamic metabolic competition of GSL and sugar has confirmed the metabolic priority of sugar in gut microbiota and colonized zebrafish in vivo. The competitive metabolism is involved in the crosstalk during cross-species microbes and host-microbe interactions. Tracking MYR regulation across species by the designed probes has offered rich insights into the dynamic interplay among diet, microbiota, and host health.
Main conclusion Antarctic plants employ distinct cold acclimation strategies: Deschampsia antarctica uses general membrane-chloroplast stabilization while Colobanthus quitensis relies on chloroplast-focused tolerance mechanisms. The two native vascular plants of Antarctica, Deschampsia antarctica and Colobanthus quitensis, persist in one of the most extreme terrestrial environments on Earth, where episodic freeze-thaw cycles are frequent even during the growing season. Survival under such conditions necessitates not only tolerance to freezing alone but also effective recovery from freeze-induced injuries-a composite trait referred to as freeze-thaw stress tolerance (FTST). Yet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics. Here, we employed a standardized, ice-nucleation-controlled freeze-thaw protocol and assessed FTST using two independent physiological indicators: electrolyte leakage (membrane integrity) and chlorophyll fluorescence (Fv/Fm; PSII function). We further validated the LT 50 values-the temperature causing 50% injury-through post-thaw recovery (PTR) assays, and examined total soluble sugar dynamics as a metabolic indicator of recovery capacity. D. antarctica exhibited coordinated enhancements in both membrane and chloroplast resilience following cold acclimation, with LT 50 values from both metrics closely aligned. In contrast, C. quitensis demonstrated a chloroplast-centered acclimation strategy, characterized by pronounced improvement in Fv/Fm-based LT 50 , while electrolyte-leakage based estimates remained largely unchanged. PTR results and sugar profiling supported the biological relevance of Fv/Fm as a more reliable FTST marker in C. quitensis. Together, these findings reveal distinct, species-specific acclimation frameworks to freeze-thaw stress; a global stabilization strategy in D. antarctica and a chloroplast-focused tolerance mechanism in C. quitensis, underscoring divergent evolutionary pathways for polar plant survival.
Low-temperature stress severely restricts the geographical distribution and growth of Juncao (Cenchrus fungigraminus), causing growth retardation and yield reduction. Thus, rapid nondestructive monitoring of low-temperature stress is crucial for accurate severity assessment and timely agronomic intervention. The traditional temperature threshold method often misjudges due to individual plant differences. To address this, this study developed a nondestructive method integrating chlorophyll a fluorescence (ChlF), visible-near infrared (Vis-NIR) spectroscopy, and machine learning (ML) algorithms for precise identification of stress levels. Firstly, under gradient temperature treatments, ChlF parameters and Vis-NIR spectral data were synchronously collected from Juncao leaves. Then, a stress classification criterion was established via ChlF parameters and an unsupervised learning algorithm to calibrate the Vis-NIR dataset. Finally, identification models were constructed based on Vis-NIR data and ML algorithms. Results showed that most ChlF parameters were closely correlated with Juncao's physiological and biochemical indicators. All samples were classified into three categories-no stress, mild stress, and severe stress-using ChlF parameters combined with the K-means clustering algorithm. SHapley Additive exPlanations (SHAP) analysis revealed the maximum photochemical efficiency of PSII as the top contributing classification indicator. Clustering reliability was validated by significant intergroup differences (P < 0.05) in ChlF transients, antioxidant enzyme activities, malondialdehyde (MDA) content, photosynthetic pigments, and SPAD values. Specifically, with increasing stress intensity, ChlF induction kinetic curves, Vis-NIR reflectance curves, chlorophyll a, chlorophyll b, total chlorophyll, and SPAD values decreased, while superoxide dismutase (SOD), peroxidase (POD), and MDA content generally increased. Among all combinations, Savitzky-Golay (SG) smoothing of Vis-NIR data combined with a one-dimensional convolutional neural network (1D-CNN) exhibited the optimal and robust performance, with a test set accuracy of 90.00 ± 0.73 %. This study confirms that integrating ChlF, Vis-NIR spectroscopy, and ML enables rapid nondestructive identification of low-temperature stress severity in Juncao seedlings, providing an efficient technical tool for monitoring physiological status and chilling injury early warning of Juncao.
Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional diagnostic methods, which rely mainly on soil moisture sensor monitoring or empirical models based on meteorological data, suffer from limitations such as insufficient spatiotemporal representativeness and an inability to reflect crop physiological status in real time, leading to an annual water waste of 10–30%. Therefore, developing technologies that enable real-time, non-destructive, and precise monitoring of crop water status is crucial. In recent years, the rapid advancement of high-throughput phenotyping technology has provided revolutionary tools to address this challenge. By integrating multi-source sensors (e.g., thermal infrared and hyperspectral imaging), multi-dimensional response characteristics of crops under water stress can be rapidly acquired. This paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models. It focuses on: (1) the connotation and acquisition techniques of key water-sensitive phenotypic indicators, such as canopy temperature, spectral indices, and chlorophyll fluorescence; (2) the advantages, limitations, and fusion strategies of multi-platform data acquisition systems, including unmanned aerial vehicles (UAVs), ground mobile platforms, and satellite remote sensing; and (3) the construction methods, performance evaluation, and practical application cases of diagnostic models based on machine learning (e.g., Random Forest, XGBoost), deep learning (e.g., CNN, LSTM), and mechanism-coupled models. The innovation of this review lies in its systematic integration of the entire technological chain—"phenotyping acquisition → model construction → decision-making"—while identifying current research challenges, including field environmental complexity, model generalization capability, data barriers, and interpretability. Future development pathways are proposed, focusing on low-cost sensing, explainable AI, multi-source data fusion, and cloud-edge collaborative decision systems. This review aims to provide a systematic theoretical and practical reference for water management in precision irrigation and smart agriculture.
Introduction Microplastics (MPs), ubiquitous and insidious pollutants pervading agricultural systems, pose an escalating threat to global food security. This makes the development of nondestructive methods for the early detection of MPs stress in rice seedling an urgent scientific imperative. Method Rice seedlings were cultivated under exposure to polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs at concentrations of 0 (control), 10, and 100 mg/L. Based on the stress-induced alterations in root exudates composition, a novel detection method for MPs stress in rice seedlings was developed using excitation-emission matrix fluorescence (EEMF) spectra combined with deep learning. Results Analysis of the original EEMF spectra revealed discernible differences. Feature extraction was performed using both the peak method and the PARAFAC method. Spectral changes in seedlings exposed to the low MP concentration (10 mg/L) were relatively minor compared to the control group. In contrast, exposure to the high concentration (100 mg/L) induced significant alterations in humic acid-like and amino acid-like substances. Subsequently, enhanced Vision Transformer (VIT) models were developed utilizing three distinct data representations: full EEMF spectra, emission spectra at specific excitation wavelengths, and extracted characteristic fluorescence values. The optimal model achieved 100% classification accuracy. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to evaluate feature importance, identifying both humic acid-like and marine humic acid-like components as major contributors to the model's predictions. Conclusion In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.
Seed viability is crucial for ensuring crop quality and yield. However, existing nondestructive detection methods, which primarily rely on spectroscopic techniques and simple data fusion strategies, often suffer from limited accuracy and reliability. To address these limitations, this study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability. With regard to enhancing the prediction accuracy of seed viability, a grouped hyperspectral image fusion (GHIF) strategy was proposed to more effectively integrate complementary information from visible-near-infrared hyperspectral imaging (VisNIR-HSI) and fluorescence hyperspectral imaging (Fluo-HSI) datasets. With respect to improving model interpretability, eight biochemical components in the embryo of maize seeds were measured, and two key biochemical indicators—catalase (CAT) activity and malondialdehyde (MDA) content—were identified and validated as highly correlated with seed viability and predictable from spectral data. Building on these findings, a two-stage detection model was constructed. In the first stage, the two key biochemical indicators were predicted from the fused data using regression models. In the second stage, seed viability was determined using a dual-threshold strategy based on the predicted biochemical values. Experimental results showed that the proposed method achieved 90 % classification accuracy, comparable to direct spectral models while offering greater interpretability. This approach provides a reliable and explainable solution for nondestructive seed viability evaluation.
Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m⁻² sr⁻¹ nm⁻¹ year⁻¹). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R² ranging from 0.324 (p < 0.01) to 0.947 (p < 0.001). A strong correlation with tower-based GPP (R² = 0.55, p < 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF’s superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at https://doi.org/10.6084/m9.figshare.27075145.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
An innovative, non-destructive, and rapid method was introduced to quantify hydroxytyrosol content in olive leaves by means of advanced photonic technologies and chemometrics. At the heart of this approach is a custom-made, pocket-sized fluorometer with LED excitation at 375 nm and a miniaturized spectrometer for emission detection in the visible range, designed to measure intact olive leaves with ease and precision. Four Italian olive cultivars, namely Frantoio, Leccino, Leccio del Corno, and Moraiolo, were studied, with nine leaf samplings conducted over the 2022–2024 period. Simultaneously, hydroxytyrosol concentration was measured using HPLC-DAD-MS on the same samples to establish the reference data. Chemometric analysis was applied to the spectroscopic and HPLC-DAD-MS results. The Orthogonal Partial Least Squares (OPLS) regression method was successfully used to build a predictive model to quantify hydroxytyrosol concentrations ranging from 200 to 7000 mg/kg. The model achieved very good regression coefficients of 0.9 for calibration and 0.83 for cross-validation, with root mean square errors of 600 mg/kg and 720 mg/kg, respectively. This non-destructive method, combined with the portability of the pocket-sized fluorometer, is an innovation for high-throughput screening of olive leaves. It offers significant potential for evaluating olive leaves before their use in tea production or as raw material for dietary supplements, making it highly appealing for both the agriculture and health industries.
Endogenous ethylene production occurs across biological kingdoms, yet its pathophysiological roles remain incompletely defined. In situ detection of ethylene is impeded by its inherent volatility and chemical inertness. Here, we report BORh, a new turn-on fluorescent probe that operates via ethylene-triggered displacement of a rhodium quencher from a BODIPY-rhodacycle scaffold, liberating the intensely fluorescent BOET. BORh exhibits high sensitivity and selectivity, broad pH tolerance, and negligible cytotoxicity. In mammalian PC12 cells, it permits real-time visualization of both exogenously supplied and in situ generated ethylene. Within photosynthetic systems, BORh overcomes cell wall barriers and chlorophyll autofluorescence, enabling in situ monitoring of ethylene dynamics in algae ( Chlamydomonas reinhardtii ) and higher plant ( Arabidopsis thaliana and Allium cepa ) tissues. Remarkably, BORh-enabled fluorescence imaging revealed synchronous upregulation of ethylene and reactive oxygen species (ROS) under plant abiotic stress. H 2 O 2 exhibited concentration-dependent biphasic regulation of ethylene biosynthesis, whereas ethylene exerted no reciprocal effect on ROS generation. These findings establish ROS as upstream regulators of ethylene biosynthesis within plant stress signaling cascades. BORh emerges as a robust chemical tool for spatiotemporal dissection of ethylene biochemistry, offering new insights into ROS-ethylene crosstalk during plant stress responses and paving the way for future investigations of ethylene function in mammalian pathophysiology.
In nearly all plants, pores on the leaf surface called stomata are essential for photosynthesis and gas exchange. The shape and distribution of stomata on the leaf varies widely between plants and is directly connected to photosynthetic efficiency. However, our understanding of the factors, both genetic and environmental, that exert subtle but significant effects on stomatal morphology is limited by the time required to manually annotate stomata in large imaging datasets. Here, we present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images. First, we establish QuickSpotters ability to automatically and accurately annotate mature stomata across developmental time. We also introduce an optional, speedy proofreading utility, StomEdit, that allows the researcher to quickly validate and correct machine-generated annotations. We use QuickSpotter and StomEdit to quantify how stomatal morphology evolves at the population level during cotyledon development and demonstrate how the programs can be used to extract subtle differences in stomatal development following pharmacological treatments. Finally, we describe PairCaller, a pair-calling classifier that accompanies QuickSpotter and can be used to identify stomatal clusters, a physiologically relevant and widely studied developmental phenotype. Taken together, our suite of programs facilitates quantitative analyses of stomatal development at scale, enabling high-throughput analyses of leaf phenotypes under varied conditions.
Haley Schuhl · Keely E. Brown · Hudanyun Sheng · Parag K Bhatt · Jorge Gutierrez‐Merino · Dominik Schneider · Anna Casto · Lucia Acosta‐Gamboa · Joe Ballenger · Fabio Barbero · Jackson Braley · Autumn Brown · Leonardo Chavez · Shannon S Cunningham · Malinda Dilhara · Adam Dimech · Joseph G. Duenwald · Annika Fischer · Jared Gordon · Chloe Hendrikse · Gabriela L Hernandez · John G. Hodge · Martina Huber · Brandon M. Hurr · Sanaz Jarolmasjed · Karina Medina‐Jiménez · Samuel Kenney · Grant Konkel · Alexander Kutschera · Sunita Lama · Matthew Lohbihler · Argelia Lorence · Collin Luebbert · Nathaniel Ly · Heather C. Manching · Annarita Marrano · Susan Meerdink · Nicholas M. Miklave · Pavan Mudrageda · Katherine M. Murphy · J. David Peery · Ronald Pierik · Seth Polydore · Caleb Robey · T. Michael Rogers · Thia Schultz · Eliza Seigel · Dhiraj Srivastava · Stephan Summerer · Josh Sumner · Chong Teng · A. Thompson · José C. Tovar · Tim van Daalen · Mark Watson · John J. Wheeler · Mark C. Wilson · Kaitlyn Ying · Alina Zare · Yutai Zhou · Malia Gehan · Noah Fahlgren
ABSTRACT 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. CORE IDEAS PlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. PlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. The software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science.
Chlorophyll Fluorescence (ChlF) provides valuable biophysical insights into the photosynthetic status of plants, serves as an indicator of plant stress and can be measured using simple, non-invasive methods. Therefore, it can be a powerful tool for remote sensing and large-scale vegetation monitoring. In this study, we introduce a novel, lightweight, and portable dual-wavelength Chlorophyll Fluorescence Light Detection and Ranging sensor (ChloroFLiDAR) designed for photosynthesis research and remote plant stress assessment. The sensor utilizes modulated laser light to induce ChlF and employs an I/Q lock-in amplification method to isolate the signal from background noise, thereby enhancing measurement sensitivity. This approach enables accurate ChlF measurements under varying ambient light conditions, along with measurement of the distance to the leaves. Our results demonstrate that the sensor can detect ChlF at a distance of 10 m with an integration time of 65.5 μs. Additionally, experiments on European beech (Fagus sylvatica) seedlings subjected to water stress and high light intensity demonstrated the sensor's ability to detect changes in ChlF indices due to plant stress.
Field / plotChlorophyll fluorescenceLeafPhysiological trait estimationGrowth / development / phenologyPhotosynthesis / fluorescencePigment / colour / senescence
Abstract Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll concentrations, it is destructive and temporally limited. In contrast, portable optical meters such as the CCM-300 enable rapid, non-destructive measurements of chlorophyll fluorescence ratio (CFR), but their calibration against extracted pigments is often species- and season-specific. This study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. Random Forest regression achieved the best predictive accuracy (R² = 0.51, RMSE = 0.51 mg cm⁻²), although a simple linear model was adopted for cross-year projection due to its stability. Applying this calibration to daily 2022 CFR measurements generated a “virtual acetone” chlorophyll time series, allowing comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines, but senescence occurred approximately ten days earlier in the warmer, drier 2022 season.Mixed-effects modelling of the 2022 data indicated positive effects of temperature (β = 0.0029 ± 0.0012 SE) and wind speed (β = 0.0053 ± 0.0021 SE) on CFR, whereas day of year and precipitation were not significant. A generalised additive model for 2023 explained 90% of deviance (adj. R² = 0.89) and revealed significant nonlinear effects of temperature, rainfall, and wind speed. Together, these results demonstrate that the CCM-300 can provide a robust non-destructive proxy for total chlorophyll when properly calibrated, and that Acer campestre chlorophyll dynamics are highly sensitive to interannual climatic variability.
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 · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Nitric oxide (NO) serves as a crucial signaling molecule regulating plant growth and stress responses, and its dynamic monitoring is crucial. This work presents a red-emitting aggregation-induced emission (AIE) supramolecular fluorescent sensor (β-CD/AIENAP) constructed through β-cyclodextrin encapsulation of organic AIE-active molecules (AIENAP). The extension of the material's conjugated structure red shifts the emission wavelength and improves the tissue penetration ability. Meanwhile, β-CD encapsulation restricts intramolecular motion, thereby enhancing fluorescence while improving biocompatibility and cell permeability. Density functional theory calculations verify both the luminescence mechanism and NO-responsive characteristics. The developed probe demonstrates rapid NO response (2 min) via specific triazole formation, exhibiting a large Stokes shift (180 nm), exceptional selectivity, and ultrahigh sensitivity (LOD = 77 nM). Through confocal imaging technology, the sensing system successfully realized the dynamic tracking of the dynamic spatial and temporal distribution of endogenous NO in plants, and systematically studied the NO response characteristics under different abiotic stresses in plants. Exogenous NO application experiments further validate stress resistance regulation. This study provides not only a novel nanosensor for plant NO detection but also an essential tool for analyzing NO signaling transduction and crop stress resistance mechanisms.
As the main volatile organic compounds (VOCs), isoprene plays a dual role in plant stress protection and air pollution. However, its spatiotemporal dynamic monitoring in plants is insufficient, which limits environmental risk assessment. In this study, by systematically investigating the effects of the introduction of alkyne groups, maleimide groups and conjugated structures on the performance of probes, a probe (TPCM-π-M), with excellent two-photon properties was prepared. It showed good linear response in 1-240 ppm range with a detection limit of 0.2 ppm, enabling accurate detection of isoprene in various plant samples. In addition, the spatial distribution of endogenous isoprene in deep plant tissues and dynamic visual monitoring of isoprene under abiotic stress were achieved through two-photon imaging, overcoming the shortcomings of traditional single-photon imaging such as insufficient penetration depth. In particular, the dynamic regulation mechanism of plant isoprene metabolism under abiotic stress was revealed through the carotenoid/photorespiration inhibition model, and the correlation between the isoprene content in different flowers and their stress response ability was confirmed. This study provides technical support for analyzing the role of isoprene in plant metabolism and environmental adaptation, and has theoretical and applied value in plant physiology, pollution monitoring and biomedical imaging.
The legume-rhizobia symbiosis is a cornerstone of sustainable agriculture due to its ability to facilitate biological nitrogen fixation. Still, real-time visualization and quantification of this interaction remain technically challenging, especially across different host backgrounds. In this study, we systematically evaluate the efficacy of the nitrogenase system nifH promoter (PnifH) in driving expression of distinct fluorescent reporters; superfolder yellow fluorescent protein (sfYFP), superfolder cyan fluorescent protein (sfCFP), and various red fluorescent proteins (RFPs) within root nodules of determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) systems. We show that PnifH-driven sfYFP and sfCFP yield strong, uniform, and reproducible fluorescence in nodules of both systems, facilitating reliable quantification of nodulation traits and strain occupancy. In contrast, RFPs including monomeric (mScarlet-I, mRFP1, mARs1) and multimeric (AzamiRed1.0) variants exhibited weak or inconsistent signals in pea. Notably, fluorescent labeling did not impair rhizobial competitiveness for root nodule occupancy, and PnifH-driven sfYFP and sfCFP reporters enabled robust multiplexed imaging in single-root and split-root assays. In the lotus, mScarlet-I worked robustly and facilitated a tripartite strain labeling system. Complementing our molecular toolkit, we established a deep learning-based analytical pipeline for high-throughput, automated quantification of nodulation traits, validated against standard ImageJ analysis. Altogether, our results identify PnifH-driven sfYFP and sfCFP as robust, broadly applicable reporters for legume-rhizobia symbiosis studies, while highlighting the need for optimized red fluorophores in some contexts. The integration of validated promoter-reporter constructs with state-of-the-art computational approaches provides a scalable framework for dissecting the spatial and competitive dynamics of plant-microbe mutualisms. IMPORTANCEThe legume-rhizobia symbiosis is central to sustainable agriculture through its capacity for biological nitrogen fixation, yet tools for real-time, quantitative visualization of this interaction remain limited. Here, we demonstrate that the nifH promoter (PnifH) effectively drives expression of superfolder yellow (sfYFP) and cyan (sfCFP) fluorescent proteins in both determinate (Lotus japonicus-Mesorhizobium japonicum) and indeterminate (Pisum sativum-Rhizobium leguminosarum) nodules. These reporters enable robust, reproducible fluorescence without impairing rhizobial competitiveness, supporting multiplexed imaging and quantitative nodulation analyses. By contrast, red fluorescent proteins exhibited host-dependent variability, underscoring the need for improved red fluorophores. Integration of validated promoter-reporter constructs with a deep learning-based image analysis pipeline establishes a scalable framework for high-throughput assessment of nodule occupancy and symbiotic dynamics. This work provides a practical molecular and computational toolkit for dissecting plant-microbe mutualisms across diverse host systems.
Crop organ-level nitrogen (N) dynamics (accumulation and transport) are strongly associated with final quality and yield. Conventional crop N monitoring methods either have high uncertainty (crop model) or limited capacity to diagnose N status in stems and grains (remote sensing tools). Data assimilation overcomes the shortcomings of crop model and remote sensing tools, but whether it can accurately simulate nitrogen dynamics at the field scale remains unknown. We aimed to develop a novel dual assimilation framework coupling a crop model and unmanned aerial vehicle (UAV) remote sensing, incorporating fluorescence information to enhance the monitoring of crop N dynamics. Firstly, the selection of WOFOST parameters was based on the sensitivity analysis results, and the calibration was conducted through optimization algorithm. Next, machine learning and multi-task neural network (MDNN) were employed to construct the inversion models of four state variables (leaf area index, LAI; leaf dry matter, LDM; leaf N accumulation, LNA; soil moisture content, SMC) based on UAV multispectral data. Meanwhile, a fluorescence operator was constructed using machine learning to capture the complex relationship between fluorescence parameters (actual photochemical efficiency, ΦPSⅡ) and state variables. Finally, the remote sensing inversion results and ΦPSⅡ were incorporated into the dual assimilation framework to update WOFOST. The results showed that MDNN outperformed traditional machine learning in the remote sensing inversion tasks for four state variables. The joint assimilation of LAI, LDM, and LNA improved the simulation accuracy of organ N accumulation. The dual assimilation strategy significantly enhanced the monitoring performance for N accumulation in leaves, stems, and grains (R²: 0.76–0.84, 0.68–0.80, and 0.70–0.75; NRMSE: 15.04–18.74 %, 16.00–25.34 %; 20.40–23.60 %). The treatment of 30 mm irrigation combination with 200 kg ha⁻¹ N fertilizer exhibited the highest N transport (71.22 %) and contribution (60.12 %) to grain. Overall, the dual assimilation framework demonstrated robust performance in monitoring organ-level N dynamics for wheat, providing a promising approach for acquiring spatially variable information about N accumulation and transport.
ABSTRACT The development of remote sensing methods to estimate plant functional diversity is limited by mismatches between ecology and remote sensing sampling schemes, and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” encompassing a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions: Q1. How should remote sensing characterize functional diversity in large surfaces (sites)? Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples should be used instead. Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale? Accounting for background effects is the key. Optical traits (remote sensing estimates of plant traits) are the best estimators for plant functional diversity. Other variables succeed when filtered out of the soil pixels; their combination did not yield additional advantages. Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements? Leaf area index (LAI) is a better proxy of abundance than the pixel for Q Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution. Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity? The estimation error decreased with LAI and stabilized at values above 1 m²/m². Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution? Optical traits, fluorescence, and reflectance factors were the most robust variables. Still, field data resolution needs to be degraded to match the sensor’s resolution. We found a relative spatial resolution threshold of ∼30 % (where the pixel is around three times larger than the plants). Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.
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 · publicvariables, we used the “pyGNDiv” package (https://github.com/JavierPachecoLabrador/pyGNDiv-Open asset ↗JavierPachecoLabrador/pyGNDiv- · pyGNDivpdf-page:11 lines:1-60Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Viral infections represent a critical threat to cultivated plant species. In papaya cultivation, two viral diseases-papaya mosaic (caused by papaya ringspot virus type P-PRSV-P) and papaya sticky disease (caused by a virus complex of papaya meleira virus-PMeV, and papaya meleira virus-PMeV2)-are prevalent and capable of devastating entire plantations, incurring substantial economic losses. Current diagnostic practices rely on visual identification of symptoms and elimination of infected plants (roguing). Monitoring photosynthetic efficiency in orchards prone to PRSV-P and PMeV2 coinfection may allow early intervention, mitigating productivity losses and reducing fruit quality. This study aimed to evaluate chlorophyll a fluorescence as a biomarker for photosynthetic impairment and symptom severity in papaya infected with PRSV-P and/or PMeV2 and to explore the feasibility of early detection of the infection by these dual pathogens, as an exploratory study under field conditions. Chlorophyll a fluorescence revealed details about the physiology of plants coinfected with the complex of PMeV2 and PRSV-P: the electron motive force within PSII decreases in infected plants and in those without visual symptoms of infection, being proportional to the age and developmental stage of the plants. A slowdown in the multiple electron transfer turnover of PSII and a decrease in the efficiency of the redox reactions of photosystem I were observed in plants with or without visual detection of infection. The evidence generated suggests that the chlorophyll a fluorescence technique can be used to monitor the pathophysiological state of plants under biotic stress.
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.
Published9 Oct 2025Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for PhotobiologyCited by 0 · OpenAlex ↗
The widespread use of herbicides such as atrazine and paraquat, although essential for weed control, can unintentionally impact non-target plant species, compromising agricultural productivity and environmental health. Understanding the physiological responses induced by these compounds is critical for developing more sustainable agricultural practices. Despite the extensive use of chlorophyll fluorescence techniques, most studies typically average signals across tissues, overlooking localized and heterogeneous stress patterns. To address this gap, the present study evaluated the use of time-resolved chlorophyll-a fluorescence imaging as a non-invasive, spatially resolved technique for the assessment of herbicide-induced stress in chicory (Cichorium intybus) leaves. The results revealed that chlorophyll fluorescence parameters, particularly F v /F m , F 0 , Fₘ, and ϕNPQ, detected heterogeneous stress patterns depending on the herbicide across the leaf. Atrazine exposure increased F 0 , indicating a blockage in the electron transport chain, while paraquat decreased F 0 , suggesting chlorophyll degradation. Both herbicides reduced F m and altered ETR, yet through distinct physiological mechanisms. These findings demonstrate that time-resolved fluorescence imaging can distinguish specific modes of herbicide action with high sensitivity. Notably, the technique enabled detection of physiological alterations before the appearance of visible symptoms, highlighting its potential for early stress diagnosis. This work establishes time-resolved fluorescence imaging as a powerful diagnostic tool, capable of discriminating specific herbicide actions and for monitoring plant stress at a fine spatial scale. Furthermore, its compatibility with automated and AI-based analysis platforms highlights its potential for advancing precision agriculture, optimizing herbicide application, and promoting sustainable farming practices.
Fluorescence lifetime measurements offer crucial insights into molecular interactions and fluorophore microenvironments, with significant potential to advance research in areas like photosynthesis and field phenotyping. Currently, these measurements are largely confined to controlled laboratory settings. To overcome this limitation, we developed a portable fluorescence lifetime imaging system utilizing a dual-tap CMOS sensor. Acknowledging the unique noise characteristics of CMOS sensors and the temperature-dependent noise challenges inherent in on-site measurements, we propose a dedicated denoising method for CMOS-based fluorescence lifetime images. This procedure significantly improves the accuracy of fluorescence lifetime data and enhances image clarity, enabling on-site measurements of intact plants.
Sustainable and low-nicotine production of tobacco requires rapid and accurate on-site assessment of the leaf nitrogen (N) status. This issue can be supported by fluorescence-based sensors, which are promising tools for precision N management. We then aimed to 1) evaluate the suitability of the Multiplex® fluorescence sensor (Mx) to predict, at an early stage, the final nicotine content of tobacco leaves; 2) develop a model for in-season tobacco foliar N estimation using the Partial Least Square (PLS) multivariate regression technique; and finally, 3) test the effectiveness of a Mx map-based Variable Rate Nitrogen Fertilization (VRNF) in reducing the spatial variability in leaf Nitrogen Balance Index (NBI), that is the N status, of a commercial field of Virginia Bright tobacco. The NBI measured by the Mx about two months after transplanting was found to linearly relate to the nicotine content measured after curing (R² = 0.72, P < 0.001) over a nicotine range of 0.25 – 4.12 %. NBI, defined as the ratio between the leaf chlorophyll (SFRR) and Flavonoids (FLAV) indices better related to nicotine than the single SFRR and FLAV indices (R² = 0.47, P < 0.001 and R² = 0.52, P < 0.001, respectively. Furthermore, the NBI estimated the actual leaf N content before flowering better (R² = 0.33) than single SFRR and FLAV indices (R² = 0.28), over a range of 21 – 37.6 mgg⁻¹. Leaf fluorescence sensor indices were thus combined with growth stages and weather variables across diverse varieties and sites. The resulting PLS model successfully predicted leaf N (R² = 0.72, RMSEP = 2.73 mgg⁻¹ and relMAE = 7.75 %) over a range of 20.6–28.0 mgg⁻¹. The most significant variables, primarily related to solar radiation, were identified for a robust general model development. Finally, the spatial pattern of the NBI was mapped over a 2.04 ha commercial plot of the ITB 6118 variety, and used to produce a three-zone prescription map. Two weeks after the intervention of VRNF based on the defined prescription map, the overall NBI variability had dropped from 23.5 % coefficient of variation (CV) to 7.9 % CV. Our results show the feasibility of using the Mx sensor for precision fertilization of Virginia Bright tobacco and highlight its potential to support future developments aimed at more sustainable production of plants with reduced nicotine content.
Abstract Background Arbuscular mycorrhizal fungi (AMF) are ancient soil symbionts that form mutualistic associations with approximately 80% of terrestrial plant species. They enhance host nutrient and water acquisition in exchange for photosynthetic carbon. Current AMF research relies on field trials, compartmented cultivation and pot cultures‒methods that are time-consuming (months to years) and unable to monitor dynamic nutrient transport, thus limiting efficient strain screening. Results We developed a real-time fluorescence imaging platform integrating sterile symbiotic microchambers with photodiode array detection. This system enables non-invasive, quantitative tracking of nutrient flux at plant-fungal interface. Distinct AMF strains exhibit significant differences in fluorescence kinetics—such as accumulation rate and peak intensity—providing measurable indicators of transport efficiency. The platform allows high-throughput functional screening of AMF strains, dramatically accelerating the identification of high-performance symbionts. Conclusion Our method overcome the temporal and technical limitations of conventional AMF screening approaches. By enabling simultaneous real-time monitoring and high-throughput analysis, it shortens screening cycles and establishes a standardized framework for (1) precision breeding of efficiency AMF strains, (2) mechanistic study of nutrient exchange, and (3) development of sustainable microbial inoculants.
Significance Statement Early studies noting uneven spatial distribution of progeny genotypes after pollination support a hypothesis where differences in pollen tube growth rate can bias inheritance. We used computer vision and statistical analysis to show alleles reducing maize pollen fitness are likely to produce statistically significant increasing, decreasing, or curvilinear spatial patterns from the apex of the inflorescence to the base, suggesting that differential pollen tube growth is not the only mechanism at play. Summary Often, more pollen grains land on recipient flowers than there are ovules to fertilize. Consequently, the haploid male gametophyte engages in post-pollination competition, one way that pollen genotype can influence inheritance. The maize ( Zea mays subsp. mays L.) inflorescence (ear), with its elongated stigma and style structures (silks), has a conspicuous spatial heterogeneity, with longer silks at the base of the ear than those at the apex. To evaluate the hypothesis that alleles with reduced pollen fitness influence the spatial distribution of progeny genotypes along the ear, we developed an updated phenotyping platform that maps mutant Ds-GFP kernel phenotypes on the ear via an implementation of the Faster R-CNN machine vision model (EarVision.v2) and a statistical pipeline that evaluates the relationship between kernel position and transmission ratio (EarScape). In our dataset (1384 ears) representing 58 Ds-GFP alleles, none with Mendelian inheritance (0/48) showed any significant pollen-conditioned spatial trend. In contrast, 50% of alleles with a pollen-specific transmission defect (5/10) exhibited significant spatial effects. An insertion into a gene encoding a putative actin-binding protein, base-to-apex gradient1* ( bag1* ), conditions increased mutant transmission at the ear apex relative to the base. Surprisingly, mutant alleles of two other pollen-expressed genes can generate the opposite pattern, decreased mutant transmission toward the ear apex; and two mutant alleles of the sperm-cell attachment factor, gamete expressed2 ( gex2 ), can produce ears with transmission highest at both base and apex. We conclude that pollen fitness mutants have relatively common but heterogenous effects on the spatial distribution of progeny genotypes.
Reproduction assets foundThe paper's maize ear phenotyping assets are publicly available: the EarVision.v2 repo contains the training images with bounding-box annotations and the trained Faster R-CNN model, and the EarScannerUtilities repo contains the ear-scanning/projection code. The EarScape spatial-analysis repo (with coordinate .xml filesCode · publica license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
540 Varifocal Lens 1080P USB Camera with H.264 High DeYinition Sony IMX323 Webcam. The
541 code for scanning ears, generating projections, and uploading those into cloud storage was
542 also updated and is available at https://github.com/fowler-lab-osu/EarScannerUtilities.
543 The set of ear projections used for the training set included 409 examples from the
544 summer Yield seasons of 2018, 2019 and 2022, encompassing images generated from three
545 different digital cameras and two different versions of the MES. For this training set,
546 projections were manually annotated usingOpen asset ↗fowler-lab-osu/EarScannerUtilitiespdf-layout-page:20 lines:1-56Plant phenotyping relevance matchEurope PMC · checked 14 Sept 2026
Wiebe B, Moran ME, Seeley M, Senft R, Schuessler A, Thomson E, Aparecido L, Cooper HF, Gehring C, Hultine K, Koepke DF, Martin RE, Posch BC, Richardson A, Whitham TG, Allan GJ, Asner GP, Doughty C.
Photosystem II (PSII) is among the most thermally sensitive components of photosynthesis, and emerging evidence suggests that that plants in diverse biomes face increasing risk of PSII damage under future climate change. However, uncertainties in the distribution and drivers of PSII thermal tolerance (Tcrit) limit our ability to predict thermal risk in plant communities across spatial scales. Here, we evaluate whether intraspecific variation in Tcrit corresponds to leaf reflectance spectra (400-2500nm) to identify mechanisms associated with Tcrit in field conditions and evaluate the potential of its remote estimation using novel remote sensing platforms. We measured Tcrit using temperature response curves of minimal fluorescence (Fo) along with corresponding leaf reflectance spectra in two foundation tree species: Populus fremontii (US Southwest) and Metrosideros polymorpha (Hawai‘i). P. fremontii was sampled under both moderate ( 45ºC) heat. Consistent spectral signatures of Tcrit emerged across species and sampling conditions, with the strongest signatures in P. fremontii under extreme heat. These signatures allowed Tcrit estimation (R²=0.24-0.30; RMSE<1.0ºC) and classification of high- versus low-Tcrit (71-77% accuracy) in P. fremontii. Across both species, Tcrit tended to increase with spectral indices reflecting higher chlorophyll content and lower carotenoids, nonphotochemical quenching, and leaf water content. These findings suggest that variation in PSII thermal tolerance is linked to fundamental biochemical properties of leaves, which are reflected in their optical traits. As climate extremes intensify, spectral screening and scaling of Tcrit via remote sensing may support improved conservation, management, and thermal risk assessment in vulnerable ecosystems.
Sericulture is the multi- dimensional activity and Mulberry (Morus spp) is the sole food for silkworm Bombyx mori. It is very important to study the physiology of mulberry for the betterment of sericulture productivity and screening of better performing lines to withstand biotic and abiotic stress. It is necessary to monitor the crop growing status continuously and non-destructively to make decisions as to changed environmental conditions. High-throughput screening, defined as the automation and scaling of experimental analyses, enables rapid, reproducible, and large-scale measurements of plant traits. Importantly, these approaches allow continuous and non-destructive monitoring of crop growth, providing valuable insights for adaptive management under variable environmental conditions. Recent technological advances have introduced a wide range of high-throughput tools into mulberry research. Phenotyping platforms such as leaf area meters, chlorophyll fluorescence imaging, and portable photosynthetic systems allow rapid assessment of photosynthetic efficiency and stress responses. High-throughput sequencing methods, including RNA-Sequencing and genome-wide association studies (GWAS), have deepened genetic insights, while genome editing technologies like CRISPR/Cas9 open avenues for targeted improvement. Remote, hyperspectral, and multispectral sensing technologies enable large-scale monitoring of canopy health, nutrient status, and early stress detection. This review synthesizes how these tools facilitate early stress detection, genotype screening, and integration with molecular datasets for precision breeding. Case studies highlight their use under drought, waterlogging, nutrient imbalances, etc. This review concludes that high-throughput phenotyping not only enhances physiological understanding but also offers a pathway to accelerated mulberry improvement programs, bridging the gap between research and practical sericulture applications.
Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate-resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought-heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches-genomics, transcriptomics, proteomics, and metabolomics-to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA-SnRK2 signaling cascade, HSF-HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This "pixels-to-proteins" paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties.
Photosynthetic light harvesting complexes (LHC) are involved in light absorption and energy dissipation. By modulating the photosystems absorption cross section, they affect their photosynthetic activity and non-photochemical quenching (NPQ) capacity. These processes have been widely studied by spectrally integrated chlorophyll fluorescence methods, which mask their associated spectral information. We explored in aspen and Arabidopsis npq mutants how the absence of these components affects the development of NPQ spectra under two contrasting conditions: in the absence and presence of photoinhibition. We proposed a new parameter to estimate the development of new emitting species (NESD) during time-spectrally resolved NPQ inductions and a pipeline to disentangle PSII energy partitioning heterogeneity. We demonstrate that LHCB, PsbS and zeaxanthin is required for NESD. By combining gas exchange with spectrally resolved kinetics, we show that under photoinhibitory conditions, however, NES develops in the absence of PsbS and zeaxanthin, and the resulting sustained quenching occurring independently of photoinhibition. Furthermore, we found that in the absence of LHCB and Curvature Thylakoid 1 a significant increase in photoinhibition was observed. This suggest that in the long term effective photoprotection requires the presence of LHCB and thylakoid plasticity, while PsbS and zeaxanthin play a major role in catalyzing LHCII-dependent quenching.
Agriculture stands as a foundational element of life, closely linked to the progress and development of society. Both humans and animals depend on agriculture for a wide range of essential services, such as producing oxygen and food, along with vital raw materials for clothing, medicine, and other necessities. Given agriculture’s vital role in supporting individual well-being and driving global progress, protecting and ensuring the long-term sustainability of agriculture is essential. This is crucial for securing resources and maintaining environmental balance for future generations. In this context, in our review we have examined the various factors that can interfere with the normal physiological and developmental functions of plants and crops. These factors, referred to scientifically as stressors or stress conditions, include a wide range of both biotic and abiotic challenges. In this work we have systematically addressed all the major categories of stress that plants may encounter throughout their lifecycle. Additionally, because plants tend to exhibit recognizable physiological or biochemical responses to stress, we have cataloged the associated stress indicators. These indicators were identified through various assessment techniques, including both destructive and non-destructive approaches. A significant advancement highlighted in our review is the integration of Machine Learning (ML) algorithms with non-destructive methodologies, which has substantially enhanced the accuracy, scalability, and real-time capability of plant stress detection. These ML-enhanced systems leverage high-dimensional data acquired through remote sensing modalities, such as hyperspectral imaging, thermal imaging, and chlorophyll fluorescence. These ultimately help in enabling the early identification of biotic and abiotic stress signatures. Through advanced pattern recognition, feature extraction, and predictive modeling, ML facilitates proactive anomaly detection and stress forecasting, thereby mitigating yield losses and supporting data-driven precision agriculture. This convergence represents a significant step toward intelligent, automated crop monitoring systems. Finally, we conclude the article with a concise discussion of the potential positive roles that certain stress conditions may play in enhancing plant resilience and productivity.
Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (Brassica napus), and analyzed using MATLAB 2021b to quantify NDI, ExG, Fd/Fₘ, and plant leaf temperature. NDI, ExG and Fd/Fₘ decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in Fd/Fₘ, while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 h enabled the diagnosis of herbicide MOAs with 89.6 % accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 %. The indices acquired at 6 h, and Fd/Fₘ and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 %, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.
CottonChlorophyll fluorescencePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
A thorough understanding of the biochemical, stomatal, and mesophyll components that limit photosynthetic induction is crucial for targeted improvement of crop productivity. However, compared with biochemical activation and stomatal conductance (gs), mesophyll conductance (gm) remains underexplored in induction studies. The fluorescence method (the variable J method) is a valid and widely accessible tool for gm measurement under steady-state conditions. Here, we experimentally validated the applicability of the fluorescence method under nonsteady-state conditions, demonstrating comparable induction kinetics of gm with the well-established carbon isotope method. Building on this validation, we combined the fluorescence method with gas-exchange measurements to comprehensively examine the induction kinetics of photosynthetic rate (A) and its associated components in a set of historical cotton (Gossypium hirsutum L.) cultivars. Our results showed no significant effect of the year of cultivar release on A during induction, suggesting that dynamic photosynthesis has not benefited from past selection efforts in cotton. Nonetheless, significant among-cultivar variations were observed in all measured induction traits, hinting at breeding opportunities for leveraging dynamic photosynthetic variation to boost crop productivity. Through induction-period-integrated limitation analysis, we further identified gs as the single most important limiter of photosynthetic induction across all cotton cultivars. Moreover, the analysis also demonstrated that accurately accounting for gm kinetics is essential for the unbiased acquisition of mechanistic insights into nonsteady-state photosynthetic physiology. We recommend that future induction studies incorporate gm measurements whenever possible to strengthen the knowledge base necessary for genetically enhancing dynamic carbon gain and crop yield in the field.
Biotic and abiotic stresses can disrupt plant metabolic processes. This leads to the excessive accumulation of hydrogen peroxide (H₂O₂) in plants, which in turn induces oxidative stress. Therefore, detection of H₂O₂ is critical to understanding plant growth. In this study, we developed a naphthalene-based fluorescein near-infrared fluorescent probe (NAPF-AC) for the sensitive and selective detection of H₂O₂. Upon exposure to H₂O₂, the probe undergoes disruption of its push-pull electronic structure, triggering an intramolecular charge transfer process that allows for fluorescence-based detection. NAPF-AC exhibited excellent linearity (R² = 0.998) over a wide concentration range of H₂O₂ (0.1 to 100 μM), with a limit of detection (LOD) as low as 0.05 μM. In addition, NAPF-AC was successfully used for the in-situ detection of H₂O₂ in plant tissues. This study provides a powerful tool for studying H₂O₂ dynamics in plants and offers new insights into the mechanisms regulating plant growth and stress responses.
The danger of heavy metal pollution has drawn the global concern of researchers in recent decades, especially multiple heavy metal pollution. Heavy metal pollution induced stress may cause oxidative stress and redox balance disruption in living organism, and further trigger other secondary stresses. Fluorescent imaging analysis is considered to be effective method for real-time visualization of complex bioactive molecules in situ due to their non-destruction, high sensitivity and specificity. It is still relatively rare that bifunctional fluorescent probe for imaging redox-correlated molecules under heavy metal stress. Herein, we designed and synthesized a smart dual responsive fluorescence probe (D-P) with dual detection sites for the individual detection of HClO and SO₂ derivatives with different channels. The probe D-P was applied to effectively dual monitor the dynamic change of HClO and SO₂ derivatives in plant, zebrafish and human cells. Importantly, probe D-P successfully observed dynamic changes of HClO and SO₂ derivatives mediated redox status in living organisms under single and combined exposure to heavy metal stress, and further during DTT-induced ER stress and hypoxia and ischemia stress, confirming that probe D-P is a powerful tool to real-time image of the dynamic balance of HClO and SO₂ derivatives for assessing the redox homeostasis under environmental stress, which can be monitored and adopted as the early warning in environmental stress conditions and disease.
Carbon monoxide (CO) is known as a highly toxic gas being emitted during industrial activities. Recent studies confirmed the roles of CO in various biological processes, such as bacterial pneumonia and plants response to abiotic stresses, that can be evaluated in situ using fluorescence probe technology. In this work, a Nile Red-based near-infrared fluorescence probe (Z1CO) is developed to evaluate CO production in animal and plant systems. The probe Z1CO is designed by conjugating allyl formate moiety that responds to CO specifically to Nile Red fluorophore. In the presence of CO with palladium chloride (PdCl₂), Tsuji–Trost reaction facilitates the cleavage of the allyl formate moiety, resulting in bathochromic-shift of absorption spectra and enhancement of the emission at 665 nm within 60 min. Z1CO features several advantages, including near-infrared emission, large Stokes shift (75 nm), high selectivity, low detection limit (0.84 μM) and low cytotoxicity in vitro, enabling its applications in monitoring CO production in animal and plant systems. Visualization of CO production in lipopolysaccharide (LPS)-induced acute pneumonia mouse model, monitoring of CO-mediated inflammatory treatment responses and CO functions in plants response to cadmium ion (Cd² ⁺)-induced abiotic stress are then demonstrated. This work thus provides a new tool for evaluating the roles of CO in animal and plant systems.
This study proposes a nondestructive technique for the presymptomatic detection of pathogenic infections of plants, aiming to effectively prevent and control plant diseases in agriculture. The present and previous studies indicated that an increase in blue-fluorescing substances, including chlorogenic acid, in tomato leaves is a promising biomarker of infection with the pathogenic soil bacterium Ralstonia solanacearum. A soft and adhesive polyvinyl alcohol (PVA) hydrogel conformably adhered to the hydrophobic surface of the tomato leaf with a complex topography, mediated by non-volatile glycerol, enabling effective extraction of blue-fluorescing substances in a nondestructive manner. The fluorescence intensity of the PVA hydrogel increased a few days before the appearance of visible symptoms of bacterial wilt. This technique is expected to become a fundamental technology for the early detection of plant diseases.
Global climate change and urbanization have posed challenges to sustainable food production and resource management in agriculture. Vertical farming, in particular, allows for high-density cultivation on limited land but requires precise control of crop height to suit vertical farming systems. Tomato, a globally significant vegetable crop, urgently requires mutant varieties that suppress indeterminate growth for effective cultivation in vertical farming systems. In this study, we utilized the CRISPR-Cas9 system to develop a new tomato cultivar optimized for vertical farming by editing the Gibberellin 20-oxidase ( SlGA20ox ) genes, which are well known for their roles in the "Green Revolution". Additionally, we proposed a volumetric model to effectively identify mutants through non-destructive analysis of chlorophyll fluorescence. The proposed model achieved over 84 % classification accuracy in distinguishing triple-determinate and slga20ox gene-edited plants, outperforming traditional machine learning methods and 1D-CNN approaches. Unlike previous studies that primarily relied on manual feature extraction from chlorophyll fluorescence data, this research introduced a deep learning framework capable of automating feature extraction in three dimensions while learning the temporal characteristics of chlorophyll fluorescence imaging data. The study demonstrated the potential to classify tomato plants customized for vertical farming, leveraging advanced phenotypic analysis methods. Our approach explores new analytical methods for chlorophyll fluorescence imaging data within AI-based phenotyping and can be extended to other crops and traits, accelerating breeding programs and enhancing the efficiency of genetic resource management.
Reproduction assets foundThe authors state that the dataset and source code used in this study are publicly available on GitHub, which qualifies as a paper-specific public code asset for the CF 3D-CNN phenotyping analysis.Code · publicThe dataset and source code used in this study are available at https://github.com/youzh-all/CF_3D-CNN .Open asset ↗https://github.com/youzh-all/CF_3D-CNN · CF_3D-CNNlines:358-392Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
Published13 Aug 2025Journal of Agriculture Aquaculture and Animal ScienceCited by 0 · OpenAlex ↗
Setaria viridis (green foxtail) has become a principal C4 model for functional genomics and translational crop research because its short life cycle and reliable transformation protocols enable rapid production of transgenic lines. Linking those genotypes to traits, however, demands a multilayered phenotypization strategy. This narrative review synthesizes current approaches that span traditional morphology, developmental staging, and precision measurement of vegetative and reproductive traits. It describes frameworks for assessing physiological performance, such as gas-exchange assays and chlorophyll-fluorescence imaging, that translate genetic changes into functional outcomes. Recent advances in automated, high-throughput phenotyping platforms are outlined, illustrating how time-series imaging accelerates large-scale trait discovery. The review also examines experimental designs for evaluating abiotic-stress responses, highlights biochemical assays and metabolite-profiling techniques that reveal underlying metabolic adjustments, and details molecular-marker systems that couple genotype with phenotype. By integrating these complementary methods, researchers can build comprehensive genotype–phenotype maps in S. viridis, thereby streamlining gene validation and informing next-generation plant-biotechnology applications.
The redox state of the plastoquinone pool (PQ-redox) acts as a central element in a variety of intracellular signal pathways. Several methods for determining PQ-redox have been established. Although some of these methods may be quantitative, such as those based on liquid chromatography, they are typically sensitive to sample preparation. Here, we critically evaluate the use of fast chlorophyll a fluorescence induction kinetics (the so-called OJIP transient) for semi-quantitative PQ-redox estimation in green algae (Chlorella vulgaris) and cyanobacteria (Synechocystis sp. PCC 6803). The method, based on the evaluation of relative fluorescence yield at the J-step of the OJIP transient (VJ, VJ), has already been reported; however, thus far, it has been used mostly for studying dark-acclimated leaves, which limits its range of application. Here, we show that the OJIP transient can be used for semi-quantitative estimation of PQ-redox in algal and cyanobacterial cell cultures, in addition to plants. We further show that it can reflect PQ-redox in both dark-acclimated and light-acclimated samples. Our systematic comparison of Multi-Color PAM, AquaPen, and FL 6000 fluorometers demonstrates that accurate measurement of VJ and VJ parameters in suspension cultures requires low culture density and a high-intensity saturation pulse. We further show that with increasing light intensity to which the cells are exposed, the state of photosystem II (PSII) changes due to light-induced reduction of quinone A (QA-) and conformational changes, which in turn influence both the sensitivity and dynamic range of the VJ parameter towards PQ-redox estimation. A comparison of fluorescence transients in Chlorella and Synechocystis revealed high homeostatic control over PQ-redox in Synechocystis, maintained by terminal oxidases present at the thylakoid membrane. While we discuss certain limitations, our systematic assessment suggests that the OJIP method has great potential to become a routine tool for semi-quantitative PQ-redox estimation under a wide range of experimental conditions in green algae and cyanobacteria.
• A high-precision digital platform for plant phenotyping and cultivation was developed. • LED illumination based multispectral imaging system to study crop plant responses. • Different multispectral signatures were induced by plant abiotic and biotic factors. Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g. , multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.
Salinity is one of the major abiotic stresses affecting rice production, but the levels of salinity in a given field are not constant across the growing season. Since the level of salinity in a rice field can fluctuate, fast recovery from salinity stress may be a useful trait to improve rice productivity in salinity-prone areas. To develop a protocol to screen for salinity recovery, seedling stage hydroponic experiments were conducted to measure salinity recovery over time through both destructive and high-throughput image-based phenotyping. Seven rice varieties were included that had previously been classified as tolerant or susceptible to salinity. Following exposure to seedling stage salinity, plants were transferred to solution with no added salt and allowed to recover. Green leaf area and relative growth rates (RGR) of salinity tolerant varieties and one salinity sensitive variety initiated recovery (i.e. started to increase) after 4 days of salt stress removal and required 6 days to completely recover (i.e. to resume a similar RGR to that observed in the no-salt control treatment), while the other salinity sensitive varieties took more time to recover. An optimal recovery period of 6 days after salt stress removal was identified for screening. Based on RGR and chlorophyll fluorescence values, some salinity sensitive varieties recovered while their Na+ contents remained high. Therefore, salinity tolerance may not necessarily correspond to salinity recovery ability. The protocol optimized here can be scaled up to screen diversity panels and populations and used for genetic mapping of the seedling stage salinity recovery trait.
Canopy spectral information, such as Sun-Induced chlorophyll Fluorescence (SIF) and hyperspectral reflectance, are closely associated with photosynthesis and canopy structure. These spectral indicators provide valuable insights into the actual growth status of crops, thereby guiding management practices in agricultural ecosystems. While considerable efforts have been devoted to simulating the processes of photosynthesis and crop growth, comprehensive and mechanistic modeling of canopy spectral information, integrated with these processes, remains underexplored in traditional crop models. Considering the recent advances in remote sensing observations which are mostly emitted or reflected signals, being able to accurately reproduce the canopy spectra is also advantageous to enhancing the model applicability. In this study, we propose an ecohydrological model (namely the Weishan model) with an integration of a water-carbon-energy fluxes module, a carbon allocation module, a reflectance spectrum module, and a SIF spectrum module for both C₃ (winter wheat) and C₄ crops (summer maize). Comprehensive model calibration and validation have been conducted based on the eddy covariance observations over a typical winter wheat-summer maize rotation cropping cropland in the North China Plain. Validation results highlight the capability and applicability of our ecohydrological model in reproducing the variation of water-carbon fluxes (i.e., evaporation, transpiration, averaged soil moisture, and gross primary productivity), crop growth variables (i.e., leaf area index and end-of-season crop yield), and canopy spectral information (i.e., top-of-canopy SIF, reflectance at near-infrared, red, and blue bands, and vegetation indices). Our model is capable of simulating canopy spectra through mechanistic representations of photosynthesis (e.g., utilizing the Farquhar biochemical model, the Ball-Berry stomatal model, and the energy balance model) and crop dynamics (e.g., phenology, leaf dynamics, carbon allocation and partitioning, biomass accumulation, and yield formation). This comprehensive framework enables the model to effectively disentangle the complex interactions among these processes within a changing environmental context. Furthermore, the model’s ability to accurately reproduce canopy spectra highlights its potential to leverage remote sensing observations to enhance the model performance. We emphasize the functionality and future applicability of our model in advancing ecohydrological and agricultural research.
Drought is one of the main factors affecting mung bean production in China. Screening drought-resistant germplasm resources and cultivating drought-resistant varieties are of great significance to the development of the mung bean industry in China. Combined with chlorophyll fluorescence imaging technology, this paper proposes a lightweight mung bean drought resistance identification network model based on YOLOv8, referred to as CSCA-YOLOv8. The model uses StarNet to replace the backbone network of YOLOv8 to reduce the size of the model. The C2f_Star module is introduced in the neck structure instead of the original C2f module. Then, in order to enhance the network's attention to the key regions in the feature map, the Context Anchor Attention Mechanism (CAA) module is also introduced into the fourth C2f_Star module. Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. Finally, the SIoU loss function is used to replace CIoU to accelerate the convergence of the model. In the actual data analysis, we used the collected 4808 chlorophyll fluorescence images of the natural mung bean population under drought stress to make the Mungbean Drought Datatset(MDD) and made classification labels for each image according to different drought resistance levels, which were 0, 1, 2, 3, 4 and 5. We also verified the excellent performance and generalization performance of the model using the collected MDD dataset. The final experimental results show that compared with the YOLOv8s baseline model, the number of parameters of our proposed algorithm is reduced by 24%, the floating point number is reduced by 35%, and the accuracy is improved by 2.52%, which supports the deployment on embedded edge devices with limited computing power. Therefore, our proposed algorithm has great potential in the field of drought resistance identification and germplasm selection of mung bean.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' MDD chlorophyll fluorescence image dataset (4808 mung bean drought-resistance images with labels) and their CSCA-YOLOv8 source code on a public GitHub repository, making both directly actionable paper-specific assets.Code · publicThe dataset and source code are
available on Github.Open asset ↗pdf-page:3 lines:1-51Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Hydropersulfides (RSSH) are an important class of reactive sulfur species (RSS) involved in a variety of physiological processes in biological systems. However, selective detection of RSSH is challenging since the persulfide group (-SSH) exhibits reactivity akin to biologically abundant biothiols. To address this issue, we designed CRBA as a ratiometric fluorescent probe for RSSH by integrating the nucleophilic ring-opening of an N -benzoylaziridine and spirolactamization of a rhodol. The ratiometric sensing of RSSH is realized by modulating Förster resonance energy transfer (FRET) in the coumarin-rhodol dyad. Nucleophilic attack of the N -benzoylaziridine moiety in CRBA by RSSH affords the corresponding secondary amide ring-opened product, which subsequently undergoes a spontaneous intramolecular spirolactamization to disrupt the π-conjugation structure of the rhodol. The tandem reaction decreases the intramolecular FRET efficiency within the probe and causes a clear dual-emission signal change. Probe CRBA possesses outstanding selectivity toward RSSH over biothiols and H 2 S, and is capable of tracking RSSH levels in Arabidopsis thaliana roots. Moreover, we observed the upregulation of RSSH levels in heavy metal (HM)-induced stress of A. thaliana with probe CRBA , and revealed the correlation between S -persulfidation and H 2 S levels in living plants.
This paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches. It highlights that traditional methods, such as visual inspection, microbiological isolation, culturing, and molecular and serological techniques, are often limited by being time-consuming, subjective, or requiring specialized expertise and lab processing. These limitations can lead to significant crop yield losses, economic setbacks, and threats to food security. The review then discusses modern, non-destructive sensor technologies, which are crucial for detecting diseases in their early stages, often before visible symptoms appear. These technologies include: * Hyperspectral Imaging (HSI): Captures detailed "spectral fingerprints" of plants to detect subtle physiological changes. * Multispectral Imaging (MSI): Uses a limited number of spectral bands, often including near-infrared (NIR), to identify abnormal plant conditions more cost-effectively than HSI. * Thermal Imaging: Detects temperature fluctuations in plants caused by physiological changes during infection. * Chlorophyll Fluorescence Imaging (CFI): A non-invasive technique that detects early stress responses by analyzing chlorophyll emissions. * LiDAR and Drones: Used for aerial analysis of crop health, enabling early diagnosis and monitoring of large agricultural areas. Finally, the paper details how Artificial Intelligence (AI) and Deep Learning (DL) have revolutionized this field through automated, highly accurate diagnostic capabilities. The document covers various deep learning architectures, including Convolutional Neural Networks (CNNs) like AlexNet, VGG, ResNet, and YOLO, which are used for image classification, feature extraction, and real-time disease localization. It also mentions the use of semantic segmentation models like U-Net for pixel-level disease mapping, and the role of transfer learning and explainable AI (XAI) in improving model performance and transparency. The review concludes with an emerging paradigm of federated learning for decentralized, privacy-preserving model training.
Abstract Water management in urban gardens is increasingly complex due to diverse plant species and growing drought stress under climate change. This study proposes a non-destructive method to classify drought responses of mixed garden plant species using RGB image indices and a support vector machine (SVM) model. Chlorophyll fluorescence responses were used to evaluate photosynthetic stress, while image-derived indices—green leaf index (GLI), normalized green-red difference index (NGRDI), and blue-green pigment index (BGI)—were analyzed to assess drought responses. Hierarchical clustering grouped species into three response clusters based on fluorescence and image patterns. Principal component analysis (PCA) identified NGRDI, GLI, and BGI as key variables, with NGRDI and GLI showing strong correlations with soil moisture content and BGI distinguishing cluster-specific responses. An SVM model was constructed using RGB indices and soil moisture content as input features, achieving a classification accuracy of 88.3% and an F1 score of 0.85 through five-fold cross-validation. This approach supports efficient water management and plant selection, and can be extended to precision irrigation strategies using machine learning.
An accurate yet rapid assessment of pungency and color values is pivotal for the commercial processing of pepper, underscoring the critical need for high-throughput detection techniques. This study investigates a rapid method for determining the pungency and color value of paprika via LED-induced fluorescence spectroscopy. A simultaneous detection instrument was developed with a proposed fluorescence correction method to reduce the impact of excitation light reflected by the sample. The corrected spectra could improve the performance of pungency prediction. The best pungency model achieved a R c 2 of 0.941, R cv 2 of 0.935, RMSEC of 6813.41 SHU and RMSECV of 6959.97 SHU. The best color value model achieved an R c 2 of 0.862, R cv 2 of 0.822 RMSEC of 1.233 and RMSECV of 1.402. This study offers a practical solution for simultaneous quality assessment suitable for industrial deployment, with the potential to extend this methodology to diverse cultivars.
The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms.
Ethylene is a volatile and low-reactivity plant hormone that is indispensable for regulating plant growth and fruit ripening. However, real-time and highly sensitive detection of ethylene in plants remains a significant challenge. Herein, we developed orange-emitting carbon dots (e-CDs) through solvothermal synthesis, integrating Grubbs catalyst-mediated olefin metathesis with the inherent adsorption properties of carbon dots to specifically recognize and turn-on response to a nonpolar ethylene molecule. By employing this innovative design, our fluorescent probe achieves a detection limit of 0.12 ppm and delivers a rapid (within 3 min), highly selective response in complex biological matrices. Utilizing these capabilities, we monitored endogenous ethylene release during fruit ripening and achieved spatially resolved fluorescence imaging of ethylene in both fruits and Arabidopsis leaves. This versatile sensor platform not only surpasses the sensitivity of previously reported small-molecule sensors but also broadens the applicability of carbon dots to nonpolar gas detection, providing a powerful new tool for advancing agricultural practices, food preservation, and fundamental plant physiology research.
Early and accurate recognition of abiotic stress types is essential for accelerating the selection of stress-tolerant varieties and implementing effective management strategies. This study is motivated by the socio-economic relevance of vineyard and by the increasing need for stressor-specific fingerprint(s) to support the reliable identification of stress type (e.g., drought or salinity) within a high-throughput plant phenotyping domain. This paper presents a reanalysis of physiological and phenotyping data from drought and salt stress experiments in Vitis vinifera focussing the maximum photosynthetic efficiency ( F v / F m ) and leaf Dark Green color. The reanalysis suggests that salt-stressed vines might suffer additional (non-stomatal) limitations curbing net photosynthetic rate (Pn) severely as drought stress does at equivalent stomatal conductance ( g s ) levels. Through a Principal Component (PC) Analysis, physiological and colorimetric response variables were decomposed revealing that F v / F m and Dark Green dominates the non-stomatal PC (∼80%) clustering data between salt and drought experiments. Confusion matrices reveal that model based on F v / F m and Dark Green performed better (accuracy = 1, precision =1) than that based on Pn, g s , transpiration, and stem water potential. This study supports the potential use of F v / F m and Dark Green for early and non-destructive stress type identification.
Abstract Correlative imaging is a powerful tool for revealing information on cell-type structures and their biochemistry, with the potential to inform healthier food choices and improved dietary recommendations. Determination of plant structures and their structural biochemistry advances our understanding of specific structures designed to store different biomolecules within cells and tissues. Compared to the classical biochemical separation techniques, the key advantage of sequential correlative imaging techniques is in relating spatial plant (micro)structures to their biochemistry in a nondestructive manner. Sequential imaging reported here comprises six methodologies on a single sample, a cross-section of a Tartary buckwheat (Fagopyrum tataricum) grain, namely, bright-field and autofluorescence microscopy, fluorescence microspectroscopy, MeV-secondary ion mass spectrometry, micro-particle-induced X-ray emission, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry. Results confirm that the stepwise addition of the desired information across several classes of biomolecules and several spatial scales informs the quality and safety of plant-based produce across scales. Therefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity. The advantages and disadvantages of the selected methodologies were critically evaluated.
Reproduction assets foundThe paper explicitly points to a public Zenodo deposit containing the correlative imaging data (SEM, micro-PIXE, MeV-SIMS maps) used in its analyses, with instructions for reproducing image fusion in Wolfram Mathematica or ImageJ.Dataset · publicsed to reveal the allocation of K
to cotyledons (Supplementary Fused Image 1). Similarly, on
the same SEM image, MeV-SIMS distribution maps under the
selected peak were overlaid (Supplementary Fused Image 2).
Custom combinations can be done in the Wolfram
Mathematica program or in ImageJ (Merge Channels) using
data available at https://doi.org/10.5281/zenodo.14628251, fol
lowing the instructions in the Materials and Methods.
Conclusions
The low emission properties of fluorescence biomolecules,
when excited with 405 nm light, inherently limit the informa
tion acquired using fluorescence imaging. At this excitation
wavelength, catechin may be the primary fluorophore in
Tartary buckwheat cotOpen asset ↗zenodo · 10.5281/zenodo.14628251pdf-raw-page:13 lines:1-89Plant phenotyping relevance matchCrossref · Europe PMC · checked 13 Sept 2026
Ensuring global food security requires noninvasive techniques for optimizing resource use and monitoring crop health. Hyperspectral imaging (HSI) enables the precise analysis of plant physiology by capturing spectral data across narrow bands. This review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning. These advancements allow real-time monitoring of photosynthesis, chlorophyll fluorescence, and carbon assimilation, linking spectral data to plant health and agronomic decisions. Key indicators such as solar-induced fluorescence and vegetation indices enhance crop stress detection. This work compares HSI-derived metrics in differentiating nutrient deficiencies, drought, and disease. Despite its potential, challenges remain in data standardization and spectral interpretation. This review discusses solutions such as molecular phenotyping and predictive modeling, for AI-driven precision agriculture. Addressing these gaps, HSI is poised to revolutionize farming, improve climate resilience, and ensure food security.
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
Reproduction assets foundThe paper's data availability statement explicitly provides all code and raw data (hyperspectral reflectance and trait measurements) for reproducibility via the authors' public GitHub repository.Code · publicidge, Cambridge, UK
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These authors contributed equally.
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Corresponding authors.
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Email address:
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rudan.xu@uni-potsdam.de
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jfergu@essex.ac.uk
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jk417@cam.ac.uk
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nikoloski@mpimp-golm.mpg.de
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Data availability statement
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All code and raw data to ensure reproducibility of the results can be accessed at:
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https://github.com/Rudan-X/HyperspectralML
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Funding statement:
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J.F. was supported by the European Union’s Horizon 2020 research and innovation program
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grant 862201 (to J.K. and Z.N.). R.X. was supported by the International Max Planck Research
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School "Molecular Plant Science" between the Max Planck Institute of Molecular Plant
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Physiology and the UniveOpen asset ↗Rudan-X/HyperspectralMLpdf-raw-page:1 lines:1-71Plant phenotyping relevance matchOpenAlex · checked 6 Sept 2026
The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R2 > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R2 of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data.
Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R² = 0.75) and leaf chlorophyll content (LCC, R² = 0.91), and shows a general high retrieval performance for light absorption (fAPARCₕₗ, R² = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.
Verticillium wilt is a major threat to eggplant production, and there is an urgent need for the rapid and accurate screening of resistant varieties to enhance breeding efficiency. Owing to the long incubation period of Verticillium wilt before visible symptoms appear, early disease detection remains challenging. In this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration. The results showed that the two-leaf-one-heart stage, combined with a spore concentration of 1 × 10⁷ spores/mL, provided optimal conditions for inoculation. Chlorophyll fluorescence (Chl-F) was used to monitor the early physiological changes in plants under pathogen stress. After pathogen inoculation, changes in Chl-F parameters, such as the effective quantum yield of photosystem II (ΦPSII) and the relative electron transport rate (rETR), were negatively correlated with the genotype’s resistance. These parameters served as early indicators for resistance screening and grading. Furthermore, our findings confirmed the high correlation (R = 0.92, P < 0.01) between the leaf injection and root-dipping inoculation methods in a resistance-segregating population, validating the reliability of the leaf injection method for resistance screening. This study demonstrated the effectiveness of combining the leaf injection method with Chl-F analysis for precise and early disease detection and offered valuable insights into enhancing eggplant breeding strategies for Verticillium wilt resistance.
Photosynthesis plays a pivotal role in vegetable growth. However, its intricate interplay with plant physiology and environmental factors complicates precise prediction of photosynthetic rates (Pn). Current predictive models primarily focus on environmental influences on photosynthesis, limiting their applicability to leaves exhibiting different physiological traits. To address the challenge, we introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies. Eggplant leaves were used as experimental samples. We collected 5280 Pn data of leaves with different ChlF parameters under controlled changes in temperature, [CO₂], and light intensity. The Fₒ (initial fluorescence) and Fᵥ/Fₘ (Maximum light energy conversion efficiency of PSII system) were selected as key ChlF indicators using the entropy method. Fₒ and Fᵥ/Fₘ, along with temperature, [CO₂], and light intensity, are key features, while Pn serves as a label, forming a robust modeling dataset. Then, we proposed a Convolutional Neural Network Regression model with Input Encoding and Genetic Algorithm optimization (CNNR-IEGA) to train these environment and fluorescence data and develop the predictive model for eggplant Pn.The results indicate that the model exhibits excellent performance in predicting Pn. On unknown datasets, the root mean square error of the model is only 0.97 μmol·m⁻²·s⁻¹, with a high coefficient of determination reaching 0.99. Compared to models established by other algorithms (including multiple nonlinear regression, support vector regression, and back propagation neural network), the proposed model demonstrates superior performance across training, testing, and validation sets. Furthermore, compared to models without ChlF parameters and those with single ChlF parameters, the proposed model has the highest accuracy. This demonstrates the validity of using fluorescence to characterize crop photosynthetic performance. CNNR-IEGA can serve as a basis for crop growth environment assessment, greenhouse control, and production warning, offering new theories and opportunities for the development of precision agriculture.
ABSTRACT Current efforts to detect and evaluate crop resistance to insect pests are limited by traditional phenotyping methods, which are time‐consuming and highly variable. Sugarcane aphid (SCA; Melanaphis sacchari ) is a major pest of sorghum in North America that has emerged over the last decade and negatively impacts plant growth and development. The spectral reflectance data in visible, near infrared and shortwave infrared range (VIS–NIR–SWIR; 400–2500 nm) have been used to measure plant traits related to stress responses, nutrient dynamics, and physiological status. We examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA. We used eight sorghum lines that displayed varied levels of resistance to SCA and collected data from control and aphid‐infested plants. Spectral feature data were collected using a leaf spectrometer, while plant physiological and chlorophyll fluorescence parameters were measured with LICOR and MultispeQ devices. The random forest classifier model differentiated the control and aphid‐infested plants with a high accuracy of 87.4% with important spectral features in the VIS–NIR spectral range, particularly from 508 to 573 nm and 715 to 728 nm. The spectral indices exhibit significant difference in Greenness Index and Plant Senescence Reflectance Index in aphid‐infested susceptible lines (BTx623, SC1345) compared with control plants. In addition, plant physiological parameters, such as stomatal conductance and chlorophyll fluorescence, showed significantly higher value for aphid‐infested resistant line (Tx2783) compared with susceptible line (BTx623) in both treatments. Further, a partial least square regression model demonstrated medium predictive capability for plant physiological parameters related to fluorescence. In summary, spectral features at VIS–NIR range demonstrated promising results in differentiating aphid‐infested sorghum plants. This is a proof‐of‐concept study on potential of spectral sensing to develop an effective monitoring and phenotyping plant resistance to aphids.
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
To achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures.
The linear shape of cereal leaves creates distinct longitudinal zones that coordinate tissue maturation and resource allocation. Under abiotic stress such as heat, these longitudinal zones may differentially activate protective pathways, revealing hidden heterogeneity in stress response that remains poorly understood. Barley (Hordeum vulgare), a cold-adapted crop particularly sensitive to elevated temperatures, can serve as an ideal model for studying region-specific heat responses in leaves. Using chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD, highlighting the added value of chlorophyll fluorescence-based assessments. NPQ kinetics traits displayed consistent spatial gradients from tip to base, with heat stress reducing NPQ induction across leaf gradients. Genome-wide association analysis of traits derived from chlorophyll fluorescence imaging across leaf gradients identified significant SNPs within multiple candidate genes, including HORVU.MOREX.r3.3HG0262630 that was consistently detected in over 90% resampling iterations under heat stress, suggesting its key role during heat responses. Transcriptomic profiling along the leaf axis revealed both conserved and region-specific heat responses between leaf regions. Conserved activations highlighted conserved heat response pathways, including the reactivation of the Arabidopsis thermomemory module FtsH6-HSP21. In contrast, the region-by-temperature interaction analysis identified 40 genes with spatial responses indicative of resource reallocation from growth to defense, including those involved in growth and transport. The integration between spatially resolved phenotyping and transcriptional profiling underscores region-specific variation in response to heat along the leaf axis, guiding targeted strategies to enhance heat resilience in barley and other cereal crops.
Yang S, Zhou BY, Chen Z, Li SY, Kim Y, Luo J, Lee K, Zhou Y, Zhu L, Wu FX, Kim JS, Yang WC.
Chlorophyll fluorescence
Enzymes are essential biocatalysts in living organisms, with their dysregulation linked to various human and plant diseases. Recent advancements in enzyme detection and imaging have primarily focused on fluorescent sensors due to their superior sensitivity. However, achieving a balance between high sensitivity, specificity, and excellent catalytic efficiency remains a major challenge in the design of enzyme-activated fluorescent sensors. Herein, we propose a novel cavity filling-based design strategy (CFRD) to optimize enzyme-activated fluorescent sensors. Using nitroreductase as an example, we computationally designed and experimentally validated six fluorescent sensors, with HC - SF exhibiting an impressive catalytic efficiency ( k cat / K m ) of 435.54 μM -1 ·min -1 , demonstrating a substantial improvement in catalytic performance. Fluorescence imaging of HepG2 cells and zebrafish confirmed the NTR detection capability of HC - SF in living organisms. Most importantly, it enabled real-time, noninvasive monitoring of environmental stresses in plants. This strategy holds great potential for the design of enzyme-activated fluorescent sensors and offers a promising pathway for bioimaging applications.
Fluorescence imaging has become a central tool in plant cell biology, enabling detailed analysis of cellular structures and dynamics. However, challenges such as phototoxicity, photobleaching, and the invasiveness of fluorescent labeling have driven the development of artificial intelligence (AI)-based alternatives. Among these, deep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy. Compared with traditional methods reliant on manual operations or simple thresholding algorithms, segmentation powered by AI-based image transformation offers enhanced accuracy and reproducibility in quantifying cellular features. Moreover, virtual staining transforms bright-field images into synthetic fluorescence images, enabling non-invasive, high-resolution analyses while bypassing the need for physical labeling. Together, these techniques expand the analytical capabilities of plant cell microscopy, facilitating efficient and precise imaging workflows. Despite their potential, these approaches face technical challenges. Virtual staining relies heavily on high-quality bright-field images and is currently constrained when applied to three-dimensional analyses of complex plant tissues. Future efforts must focus on developing diverse training datasets and advancing AI technologies to overcome these limitations. By offering automated segmentation and virtual staining, AI is transforming plant cell microscopy into a more versatile and powerful tool, paving the way for groundbreaking discoveries and broader applications in plant cell biology.
Shalini Krishnamoorthi · Sally Shuxian Koh · Mervin Chun‐Yi Ang · Mark Ju Teng Teo · Randall Ang Jie · U. S. Dinish · Michael S. Strano · Daisuke Urano
Abstract Recent advancements in plant sensing technologies have significantly improved agricultural productivity while reducing resource inputs, resulting in higher yields by enabling early disease detection, precise diagnostics, and optimized fertilizer and pesticide applications. Each adopted technology offers unique advantages suitable for various farm operations, breeding programs, and laboratory research. This review article first summarizes key target traits, endogenous structures, and metabolites that serve as focal points for plant diagnostic and sensing technologies. Next, conventional plant sensing technologies based on light reflectance and fluorescence, which rely on foliar phytopigments and fluorophores such as chlorophylls are discussed. These methods, along with advanced analytical strategies incorporating machine learning, enable accurate stress detection and classification beyond general assessments of plant health and stress status. Advanced optical techniques such as Fourier transform infrared spectroscopy (FT‐IR) and Raman spectroscopy, which allow specific measurements of various plant metabolites and structural components are then highlighted. Furthermore, the design and applications of nanotechnology chemical sensors capable of highly sensitive and selective detection of specific phytochemicals, including phytohormones and signaling second messengers, which regulate physiological and developmental processes at micro‐ to sub‐micromolar concentrations are introduced. By selecting appropriate sensing methodologies, agricultural production, and relevant research activities can be significantly improved.
Rice, a vital staple crop, provides sustenance for more than half of the global population, and its production needs to be increased to keep up with the growing global population, while plant diseases threaten the sustainability of rice production, causing significant crop losses and reducing harvest quality. The widespread use of chemical pesticides has raised concerns about environmental pollution, pesticide resistance, and economic burdens. Therefore, alternative, sustainable disease management strategies are urgently needed. One promising solution involves plant immunity inducers, which activate the plant's natural defense mechanisms to combat pathogens. However, the mechanisms underlying immune activation remain poorly understood, and effective tools for the real-time visualization of immune responses are lacking. In this study, we developed a water-soluble and biosafe D-A-D (donor-acceptor-donor)-type fluorescent probe for real-time visualization of immune activation in rice via near-infrared second-window (NIR-II) fluorescence imaging. The probe, featuring a unique electronic structure, with a diphenylaminoxanthene and a benzyl group being the electron donors, the benzo[ cd ]indolium being the electron acceptor, and a butylamino group for nitric oxide (NO) detection, allows the monitoring of NO production, a key signaling molecule in plant immunity. Our results show that the probe effectively detects NO generation in response to salicylic acid (SA), an immune inducer, and visualizes immune activation and consequent microbial resistance in rice via NIR-II fluorescent imaging. This approach could provide an effective means for obtaining valuable insights into plant immune dynamics and contribute to promoting sustainable agriculture and food security.
Carboxylesterases serve as crucial regulators of metabolic processes and environmental adaptation across species. While significant progress has been made in developing animal-compatible fluorescence probes, real-time monitoring of the catalytic activity of these enzymes in plants remains challenging due to the limited penetration of visible light in plant tissues. Herein, we designed and synthesized TCF-CEs, tricyanofuran-xanthene probes containing carboxylesterase-cleavable ester bonds that triggers near-infrared fluorescence activation within 20 min. The probe demonstrates excellent specificity, low detection limit of 1.18 × 10 -4 U, and negligible cytotoxicity, which enables effective sensing of endogenous carboxylesterase activities in live mammalian cells. Furthermore, salt-stress experiments with Arabidopsis established direct correlations between carboxylesterase levels and plant stress adaptation. As an effective near-infrared probe capable of tracking carboxylesterase activities in both mammalian cells and plant systems, TCF-CEs provides a powerful tool for investigating metabolic responses between organisms and environmental challenges.
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Plant phenotyping relevance matchCrossref · Europe PMC · checked 6 Sept 2026
The widespread applications of fluorescence imaging in plant science still suffer from challenges including strong auto-fluorescence (chlorophyll) and tissue light scattering, resulting in low signal-to-background ratio (SBR) for in vivo bioimaging. Moreover, the relationship between the transport efficacy of fluorescence probes in plants and their sizes has been rarely investigated. To address these bottlenecks, we developed an ingenious PEG-engineering strategy on the second near-infrared (NIR-II) donor-acceptor-donor (D-A-D) emissive dye (CCNU1020) to adjust the self-assembly nanosizes of NIR-II fluorescence probes, resulting in three variants: SYH1 (170 nm), SYH2 (80 nm), and SYH3 (60 nm). As the polyethylene glycol (PEG) chain length increased, the probes' nanosize decreased from 170 to 60 nm. Among them, SYH3 exhibited the fastest entry velocity into Epipremnum Aureum leaf and spread over the leaf veins evenly than the other two probes, of which SYH1 even could hardly entry into the leaf. Meanwhile, SYH3 demonstrated high-contrast imaging of leaf vein with an exceptional signal to background ratio (SBR, ~ 18.6) superior to that of classical NIR-I indocyanine green (ICG) (~ 3.0) and SYH2. This promising imaging ability of leaf veins achieved by size optimization laid the foundation for the early diagnosis of viral infections. In vivo experiments further confirmed that SYH3 effectively accumulated and monitored in the lesion of Tobacco mosaic virus (TMV)-infected Arabidopsis thaliana, which matched well with the green fluorescent protein (GFP)-labeled results. This work represents a significant step forward in plant bioimaging in the cutting-edge NIR-II region.
Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape ( Brassica napus ), and analyzed using MATLAB 2021b to quantify NDI, ExG, F d /F m , and plant leaf temperature. NDI, ExG and F d /F m decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in F d /F m , while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 h enabled the diagnosis of herbicide MOAs with 89.6 % accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 %. The indices acquired at 6 h, and F d /F m and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 %, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.
Background Rice blast, one of the major diseases causing significant rice yield loss, downregulates the photosynthetic activity and induces aggressive spread of cell death causing food security concerns. Hence, earlier quantification of rice blast is imperative for improved management of the disease. Instantaneous chlorophyll fluorescence (e.g., sun-induced chlorophyll fluorescence under sunlight), which is mechanistically linked with photosynthesis at the photosystem scale, has shown the potential for quantifying the impact of abiotic stresses on plant physiology but remains yet to be tested for biotic stresses. Here, we assessed the potential of chlorophyll fluorescence (CF) for quantifying rice blast impact on plant physiology. In particular, we further retrieved the quantum yield of chlorophyll fluorescence (Φ F ) by normalizing the influence of the magnitude of incident radiation. Results Φ F sensitively responded to rice blast within 24 and 96 hours post-inoculation for susceptible and resistant cultivars, respectively. We confirmed that the Φ F showed strong sensitivity in response to different doses of inoculation and to cultivar difference. In addition to Φ F results, we further investigated the role of red to far-red CF ratio (CF R:FR ) in rice blast detection. CF R:FR , which was previously reported to be tightly coupled with chlorophyll contents, captured the impact of rice blast inoculation to some extent while green chlorophyll vegetation index did not show any difference across all inoculated groups. Conclusions We confirmed that the Φ F sensitively responded to rice blast inoculation and differentiated two dose levels of inoculation and low- and high-resistance levels via the comparison of two cultivars. Furthermore, the full spectrum of chlorophyll fluorescence was used to obtain the red to far-red CF ratio and showed its capability for indicating the physiological impact of rice blast. Our findings highlight the unique role of chlorophyll fluorescence in sensitively quantifying rice blast impact. Our approach is highly scalable through sun-induced chlorophyll fluorescence observations and thus will contribute to improving the large-scale management of rice blast.
Background Drought stress, the most prevalent abiotic stress, has a significant effect on citrus production worldwide. The differential mechanisms to overcome the drought stress has been reported in citrus rootstock genotypes. This study evaluated nine citrus rootstock genotypes, including indigenous rough lemon variants, for drought tolerance. The genotypes were subjected to well-watered, drought stress, and re-watering conditions to assess morphological, physiological, and biochemical responses. High-throughput imaging techniques were employed to non-destructively assess chlorophyll fluorescence, digital leaf area, and plant tissue water content during drought stress. Results For rapid and accurate screening of rootstocks, phenomics and physio-biochemical tools were used to know morpho-physiological responses to drought. Citrus rootstock genotype X639 demonstrated superior performance under drought stress conditions. It maintained the highest growth in terms of relative shoot increment (8.09%), number of leaves (79.00), and specific leaf area (62.45 cm 2 g -1 ). X639 also excelled in root morphological parameters, including root length, projected area, diameter, surface area, volume, and number of tips, forks, and crossings. Trifoliate hybrids X639 and Troyer citrange exhibited larger stomata (54.73 and 43.82 µm 2 ) compared to mono-foliate species, with minimal impact of drought on stomatal pore area. X639 maintained the highest relative water content, membrane and chlorophyll stability indices, leaf gas exchange parameters, and antioxidant enzyme activity. RLC-1 and RLC-4 genotypes showed pronounced accumulation of leaf proline and antioxidant enzymes during drought, contributing to better recovery after re-watering. Conclusion In this study, Cleopatra mandarin, Grambhiri, and RLC-2 were identified as drought-susceptible rootstocks based on their responses. Rootstock genotypes X639 and RLC-4 proven a superior drought-tolerant genotypes. Their robust root system enables efficient water uptake and the maintenance of water relations during drought stress. The drought tolerance of X639 was evidenced by its ability to maintain plant tissue moisture, membrane and chlorophyll stability, and higher photosystem II efficiency. High-throughput imaging techniques have proven effective in rapidly assessing and differentiating drought-tolerant and drought-susceptible citrus rootstocks based on their photosystem- II efficiency, leaf area, and tissue water content during induced drought stress. These findings will contribute to the selection and development of drought-tolerant citrus rootstocks to improve citrus production under water-limited conditions.
Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.
Real-time monitoring of plant stress signaling molecules is crucial for early disease diagnosis and prevention. However, existing methods are often invasive and lack sensitivity, rendering them inadequate for continuous monitoring of subtle plant stress responses. In this study, we develop a non-destructive near-infrared-II (NIR-II) fluorescent nanosensor for real-time detection of stress-related H 2 O 2 signaling in living plants. This nanosensor effectively avoids interference from plant autofluorescence and specifically responds to trace amounts of endogenous H 2 O 2 , thereby providing a reliable means to real-time report stress information. We validate that it is a species-independent nanosensor by effectively monitoring the stress responses of different plant species. Additionally, with the aid of a machine learning model, we demonstrate that the nanosensor can accurately differentiate between four types of stress with an accuracy of more than 96.67%. Our study enhances the understanding of plant stress signaling mechanisms and offers reliable optical tools for precision agriculture.
High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses. PhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights. This system allows for comprehensive assessment of plant health and development. PhenoGazer features automated moveable upper and lower racks for continuous measurements. The lower rack, equipped with four blue LED lights and spectrometer fiber optics, captures blue light-induced chlorophyll fluorescence at night. The upper rack, carrying four spectrometer fiber optics and cameras, captures hyperspectral reflectance and RGB images during the day. This dual capability enables detailed evaluation of plant phenology, stress responses, and growth dynamics throughout the entire crop growth cycle. Fully automated and managed by a Raspberry Pi running Python scripts, PhenoGazer ensures precise control and data acquisition with minimal human intervention. Additionally, it includes continuous measurements through a datalogger to acquire photosynthetically active radiation (PAR), soil moisture and temperature, and features expansion capability for additional analog or digital sensors as desired by end users. To test the system, soybean plants representing three conditions, healthy well watered, healthy droughted, and diseased, were monitored to evaluate growth and stress responses. PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber. By combining nighttime blue light induced chlorophyll fluorescence, hyperspectral reflectance-based vegetation indices, and RGB imagery, PhenoGazer represented a significant advancement in plant phenotyping technology, enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.
Solar-induced chlorophyll fluorescence (SIF) from hyperspectral imaging is strongly associated with agricultural indices, particularly leaf chlorophyll content (LCC), in crop phenotyping. However, confounding factors such as spectral resolution (SR), canopy structure, and illumination reduce SIF sensitivity and complicate its association with these indices. This study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy. This study employed theoretical simulations, experimental validation, and two field experiments comparing devices with varying SRs to extract SIF at 687 nm and 761 nm (SIF Red and SIF NIR ). Initially, SCOPE model was used to simulate radiance trends across different SRs. Secondly, narrow-band imager with 7 nm full width at half maximum (FWHM) were then compared with radiance from sub-nanometer and ASD spectrometers (0.3 nm and 3 nm FWHM). Finally, to modify SIF measurements, canopy structure was quantified using fluorescence escape fraction (f esc ), while absorbed photosynthetically active radiation (APAR) and intensity component from hue-saturation-intensity color model (I HSI ) were used to account for illumination. And driver factor for SIF extraction was explored. Results showed a strong relationship (R 2 ≥ 0.95) between original simulated radiance from sub-nanometer spectrometers and radiance resampled to 3, 5, and 7 nm FWHM. SIF NIR from the narrow-band imager correlated with the other spectrometers (R ≥ 0.64). While lower SR reduced SIF sensitivity, the narrow-band imager still showed potential for estimating LCC. Modified SIF NIR by f esc , APAR, and I HSI , improved correlations with LCC (R = 0.77, 0.74, and 0.86) compared to unmodified SIF NIR (R = 0.65). Estimation model based on SIF modified by f esc , APAR and I HSI presented R P 2 of 0.73, 0.75, 0.84 and outperformed SIF (R P 2 = 0.52), and photosynthetically active radiation and APAR showed R of 0.52 and 0.72 with SIF NIR , which indicated that disentangling confounding factors could enhance the sensitivity of SIF and structural effect governed SIF strongly than illumination effect. This study demonstrated the feasibility of narrow-band imagers for capturing spatial–temporal SIF variability by addressing confounding factors and can provide guidance for narrow-band instrument development for SIF extraction.
High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses. PhenoGazer, a high-throughput phenotyping system, enhances crop monitoring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics, four Raspberry Pi cameras, and blue LED lights. This system allows for comprehensive assessment of plant health and development. PhenoGazer features automated moveable upper and lower racks for continuous measurements. The lower rack, equipped with four blue LED lights and spectrometer fiber optics, captures blue light-induced chlorophyll fluorescence at night. The upper rack, carrying four spectrometer fiber optics and cameras, captures hyperspectral reflectance and RGB images during the day. This dual capability enables detailed evaluation of plant phenology, stress responses, and growth dynamics throughout the entire crop growth cycle. Fully automated and managed by a Raspberry Pi running Python scripts, PhenoGazer ensures precise control and data acquisition with minimal human intervention. Additionally, it includes continuous measurements through a datalogger to acquire photosynthetically active radiation (PAR), soil moisture and temperature, and features expansion capability for additional analog or digital sensors as desired by end users. To test the system, soybean plants representing three conditions, healthy well watered, healthy droughted, and diseased, were monitored to evaluate growth and stress responses. PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber. By combining nighttime blue light induced chlorophyll fluorescence, hyperspectral reflectance-based vegetation indices, and RGB imagery, PhenoGazer represented a significant advancement in plant phenotyping technology, enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.
High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology. Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms, using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change. Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency. Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.
Introduction: Photosynthesis is fundamental to agricultural productivity, but its relatively low light-to-biomass conversion efficiency represents an opportunity for enhancement. High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. Methods: To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. Results: Night measurement significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II (Fv/Fm) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130, have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. Discussion: These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.
Reproduction assets foundThe paper's fluorescence phenotype datasets and analysis outputs (trait QC cutoffs, BLUP/heritability model tables, GWAS significant SNP tables) are stated to be available via the article's online Supplementary Material hosted by Frontiers. No standalone author code repository or named data repository accession appearsSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1595339/full#supplementary-material
References
Ali W. Grzybowski M. Torres-Rodríguez J. V. Li F. Shrestha N. Mathivanan R. K.
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Quantitative genetics of photosynthetic trait variation in maize
. bioRxiv , eraf198 . doi:
10.1101/2024.11.25.625283
PMC12448886
40365812
Alter P. Dreissen A. Luo F.-L. MatsubaOpen asset ↗lines:613-651Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Lihua Liu · Xiaolong Yang · Piotr Robakowski · Zipiao Ye · Fubiao Wang · Shuangxi Zhou
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
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 following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Plant phenotyping relevance matchOpenAlex · checked 14 Sept 2026
The plant’s phenotype changes under biotic and abiotic stress, reflecting its adaptations in gene expression and metabolism. For crop management, rapid detection of plant stress responses is crucial. To facilitate rapid detection of stress responses in crops, we explored the potential of UCPH’s PhenoLab for assessing barley disease resistance under both biotic and abiotic stress. We used this high-throughput macroscopic phenotyping platform to assess barley disease resistance and combined pathogen and abiotic stress response nondestructively by reflectance and fluorescence imaging over time and validate them spectroscopically in leaf extracts. At specific wavelengths, PhenoLab spectral signatures clearly distinguished cultivars with different levels of susceptibility to the obligate biotroph pathogen Blumeria graminis (powdery mildew). Microscope phenotyping at similar reflectance and fluorescence settings parallelled the PhenoLab-derived spectral signatures. However, a specific systemic resistance response emerged three days after inoculation, detectable only by microscopy when targeting infected and non-infected leaf areas. We hypothesized that combined stresses would work additively and used phenotyping to study the response of the resistant and susceptible barley cultivar to a combination of drought with powdery mildew infection. Surprisingly, drought made the resistant cultivar less resistant and the susceptible one less susceptible according to changes in reflectance and fluorescence at defined wavelengths. The spectroscopic absorbance assay confirmed this result biochemically. This proof-of-concept study showcases the potential of holistic functional phenomics, using non-invasive imaging to identify predictive spectral signatures for barley pathogen resistance.
Carbon monoxide (CO) is widely recognized as a significant environmental pollutant and is associated with numerous instances of accidental poisoning in humans. However, it also serves a pivotal role as a signaling molecule in plants, exhibiting functions analogous to those of other gaseous signaling molecules, including nitric oxide (NO) and hydrogen sulfide (H 2 S). In plant physiology, CO is synthesized as an integral component of the defense mechanism against oxidative damage, particularly under abiotic stress conditions such as drought, salinity, and exposure to heavy metals. Current research methodologies have demonstrated a lack of effective tools for monitoring CO dynamics in plants during stress conditions, particularly in relation to heavy metal accumulation across various developmental stages. Therefore, development of a sensor capable of detecting CO in living plant tissues is essential, as it would enable a deeper understanding of its biological functions, underlying mechanisms, and metabolic pathways. In response to this gap, the present study introduces a novel technique for monitoring CO production and activity in plants using nitrogen-doped carbon quantum dots (N-CQDs). These nanodots exhibited exceptional biocompatibility, low toxicity, and environmentally sustainable characteristics, rendering them an optimal tool for CO detection via fluorescence quenching mechanism, with a detection limit (LOD) of 0.102 μM. This innovative nanomarker facilitated the detection of trace quantities of CO within plant cells, providing new insights into plant stress responses to heavy metals such as Cu, Zn, Pb, Ru, Cr, Cd, and Hg, as well as the processes involved in seed germination. Additionally, confocal microscopy validated the interaction between CO and N-CQDs, yielding visual evidence of CO binding within plant cells, further enhancing the understanding of CO's role in plant biology.
In-season nitrogen (N) status diagnosis is an effective way to guide split N applications for improved profitability and minimized negative environmental impacts. Petiole nitrate-N concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) N status diagnosis but is limited because of destructive sampling and chemical processing needs. Leaf sensors can be used to predict PNNC and other N status indicators and overcome these challenges. The SPAD meter is a sensor commonly used to estimate leaf chlorophyll (Chl) based on transmittance, while Dualex is a newer leaf sensor that can also measure leaf flavanol (Flav) and anthocyanin (Anth) through Chl fluorescence. Limited research has been conducted to compare the two leaf sensors for potato N status assessment, despite their respective success in N status diagnosis for other crops. Therefore, the objectives of this study were to 1) compare the performance of the Dualex sensor relative to the SPAD meter for predicting potato N status indicators when only sensor data are used, 2) evaluate the potential of improving potato N status prediction using multi-source data fusion compared with only using leaf sensor data, and 3) develop practical strategies for leaf-sensor-based in-season potato N status diagnosis. The plot-scale experiments were conducted in Becker, Minnesota, USA in 2018, 2019, 2021, and 2023 involving different cultivars, N treatments, and irrigation treatments in a split plot design with three replications. Leaf sensor data and plant samples were simultaneously collected and processed multiple times at key growth stages each year. Daily weather data were also collected at the on-site weather station. Different in-season potato N status indicators including PNNC and N nutrition index (NNI) were derived from plant samples, while weather- and management-related parameters were calculated using the weather data and management records. Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical in-season potato N status diagnostic strategy was developed using linear support vector regression model with SPAD, cultivar information, accumulated growing degree days (GDDs), accumulated total moisture, and as-applied N rate to predict vine or whole plant NNI, achieving an R² of 0.80 - 0.82, accuracy of 0.75 - 0.77, and a Kappa statistic of 0.57 - 0.58 (near-substantial). Further research is also required to determine the critical N dilution curve and sufficiency ranges of NNI for potatoes based on different genetic, environmental, and management conditions to better support decision-making.
Abstract To safely dissipate excess excitation energy, photosynthetic organisms have evolved multiple photoprotection mechanisms. These mechanisms involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized non-photochemical quenching mutants of Nicotiana benthamiana , an allotetraploid vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching efficiencies of various xanthophylls and the contributions of six quenching pathways across different mutants. It also suggests that enhancing VDE, ZEP, and PsbS expression improves overall quenching efficiency, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO 2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize ( Zea mays , C4) and basil ( Ocimum basilicum , C3) under different photon densities and spectral compositions. Maize showed the highest CO 2 assimilation rate of 30.99 ± 1.54 µmol CO 2 /(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO 2 /(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO 2 assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.
Reproduction assets foundThe paper's data availability statement names a public GitHub repository (KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlation) hosting the study's datasets, which underpin the PAM fluorescence/gas-exchange measurements and the Random Forest/XGBoost analysis. The statement does not explicitly distinguish code vs. Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-CorrelationOpen asset ↗KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlationlines:486-500Plant phenotyping relevance matchOpenAlex · Europe PMC · checked 6 Sept 2026
Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally. However, it is known to be very sensitive to droughts, which can severely impact yield. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, reliable phenotyping of water stress responses remains a significant bottleneck in crop genetics and breeding programs. Overcoming this bottleneck requires high-throughput phenotyping platforms. In this study, we used an indoor image-based phenotyping facility, the National Plant Phenotyping Infrastructure at the University of Helsinki. The facility incorporates cutting-edge imaging technologies such as top- and side-view digital imaging for assessment of growth and development, as well as chlorophyll fluorometry for the detection of physiological responses. In this study, 44 faba bean accessions were subjected to early-stage water stress via weight-based water-holding capacity. The accessions presented a range of responses to water stress across the studied traits, including plant height, total canopy area, digital biomass, and water use efficiency. Our results also revealed a strong correlation between digital biomass and biological biomass. Here, we demonstrate the potential of a fully automated indoor phenotyping facility for screening a relatively large faba bean germplasm collection under well watered and water stressed conditions. Accessions that maintained growth and physiological performance under water stress conditions in this study may serve as valuable pre-breeding materials for the development of drought-adapted faba beans.
Photosynthesis plays an important role in the terrestrial carbon cycle and is often studied using terrestrial biosphere models (TBMs). The maximum carboxylation rate at 25 °C (Vcₘₐₓ₂₅) is a key parameter in TBMs, and yet the information on the spatiotemporal distribution of this parameter is uncertain. In this study, we retrieved the global distribution of Vcₘₐₓ₂₅ at 0.25° resolution based on TROPOMI-observed solar-induced chlorophyll fluorescence (SIF) and meteorological forcing data using a parameter optimization technique. This study improves global mapping of Vcₘₐₓ₂₅ using TROPOMI's SIF and MODIS photochemical reflectance index (PRI) for accurate GPP estimation by sunlit leaves in the following aspects: the previous method relied on an empirical estimation of the ratio of SIF per unit sunlit leaf area to that per unit shaded leaf area (β), while β here was derived from a look-up table (LUT) constructed using the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. Validated at two flux tower sites, the LUT method explained most of the variation in β with R² = 0.71 and 0.67, RMSE=0.19 and 0.15 and Slope=0.84 and 0.70 for two ground validation sites. We calculated the global ratio of SIF from sunlit to that from shaded leaves (SIF_ratio), and found that the SIF_ratio had a strong spatio-temporal variability with a global average of approximately 4.6, and that the contribution of SIF from shaded leaves to the canopy total was <20 %. The optimized Vcₘₐₓ₂₅ from TROPOMI was validated against Vcₘₐₓ₂₅ derived from concurrent flux data at 27 sites distributed globally using an independent method (R² = 0.39 - 0.65, RMSE = 6.47 - 21.74 μmol m⁻² s⁻¹ and rRMSE =0.14–0.36). Based on the improved global Vcₘₐₓ₂₅ map, we found that, spatially, Vcₘₐₓ₂₅ varies significantly with latitude and between- and within-plant function types (PFTs), and temporally, it has strong seasonal variation in all PFTs except evergreen broadleaf forests. The new global Vcₘₐₓ₂₅ dataset would be useful for improving terrestrial GPP modelling from the current state of the art of using constant Vcₘₐₓ₂₅ values by plant functional type.
Key message Chlorophyll fluorescence (CF) measurements have been demonstrated to be an efficient and non-invasive tool for identifying and developing PVY-resistant potato cultivars. The validity of CF measurements was confirmed through viral titer and yield-loss assays. In the quest to identify resistant sources for potato virus Y (PVY) within Indian potato germplasm, we developed a phenotyping approach leveraging plant physiological responses against PVY infection. The study evaluated 71 potato genotypes including cultivated and experimental clones, during the year 2021-2022 and 2022-23 through mechanical inoculation in experimental fields at the Punjab Agricultural University, Ludhiana. We employed a combination of serological and molecular screening, complemented with chlorophyll fluorescence (CF) measurements to classify resistant and susceptible genotypes. Out of 71 genotypes, 34 exhibited PVY resistance, with KP-16-19-14 being the highly resistant line with minimal yield loss (i.e., only 1.64% reduction) and undetectable viral titer. This genotype holds promise as a valuable resistance source for future breeding programmes. Our findings revealed that resistant genotypes maintained stable CF metrics and experienced minimal yield reductions (up to 5.15% only), with very low viral titer. In contrast, the photosynthetic efficiency was significantly declined in susceptible genotypes, which also experienced yield losses up to 58.84% with very high viral titer. Correlation coefficient and principal component analysis (PCA) revealed a strong association among the CF parameters, disease severity, viral titer, and yield losses. This emphasizes the utility of CF as a valuable tool for assessing resistance through physiological responses to PVY. Study demonstrates that photochemistry, heat dissipation, and fluorescence emission patterns of PS-II effectively differentiate resistant and susceptible genotypes. Moreover, this study highlights the potential of integrating physiological assessments with molecular diagnostics in large-scale preliminary screening to identify and develop PVY-resistant potato genotypes.
The invasive aquatic macrophyte Eichhornia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high and low light stress remains poorly resolved. This study integrates chlorophyll fluorescence and gas exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in E. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that E. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how E. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. Insights gained have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Rapid detection of plant infections is crucial for minimising crop loss and optimising management strategies, particularly in the context of climate change. While traditional diagnostic methods provide precise measurements of phytohormones such as salicylic acid (SA), a key regulator of plant defence responses, their reliance on bulky equipment and lengthy analysis times limits field applicability. This study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples. A tailored sample pre-treatment protocol was developed and validated with real strawberry samples using HPLC measurements. The assay demonstrated a detection limit ranging from 10 -9 to 10 -6 mg/mL, within the relevant range for early infection diagnosis. The integration of the microfluidic platform with the optimised pre-treatment protocol offers a portable, cost-effective solution for on-site phytohormone analysis, providing a valuable tool for early infection detection and improved crop management.
Khong DT, Vu KV, Sng BJR, Choi IKY, Porter TK, Cui J, Gong X, Wang S, Nguyen NH, Ang MC, Park M, Lew TTS, Loh SI, Ahsim R, Chin HJ, Singh GP, Chan-Park MB, Chua NH, Strano MS, Jang IC.
Auxin, particularly indole-3-acetic acid (IAA), is a phytohormone critical for plant growth, development, and response to environmental stresses like shade avoidance syndrome and thermomorphogenesis. Despite its importance, there is no existing method that allows for convenient and direct detection of IAA in various plant species. Here, we introduce a near-infrared fluorescent nanosensor that directly measures IAA in planta using corona phase molecular recognition with high selectivity, specificity, and spatiotemporal resolution. The IAA sensor can be conveniently functionalized to living plants and localized in various tissues, including leaf, cotyledon, and root tip, with the capability to visualize intrinsic IAA distribution. The IAA nanosensor was further tested in Arabidopsis thaliana leaf with tunable levels of endogenous IAA, in which the sensor measured dynamic and spatiotemporal changes of IAA. We also showed that the IAA sensor can be used for qualitative and quantitative mapping of IAA induction and spatial movement in various plant species undergoing environmental or stress response, such as shade avoidance syndrome, high temperature stress, and gravitropism. This highlights the potential application of IAA sensor for monitoring plant health in agriculture.
Abstract Botrytis cinerea is a filamentous fungus that infects over 200 species of crops causing grey mold disease with devastating losses to agriculture worldwide. The heavy reliance on synthetic fungicides in the strawberry industry has led to the emergence of fungicide resistance in B. cinerea . Therefore, understanding the fundamental biology of B. cinerea is the first step in the search for novel antifungals. Although B. cinerea is one of the most serious pathogens of strawberry ( Fragaria x ananassa ), few protocols have been specifically developed to study this pathosystem. Consequently, early development of pathogen penetration in strawberry is poorly understood. Here we developed assays using detached strawberry leaves, fruit and petals to study B. cinerea infection. These assays allow comparison of treatment effect on the same fruit, and facilitate the screening of fungicides or biocontrol agents. Through real-time PCR, chlorophyll fluorescence analysis, scanning electron and confocal microscopy, we quantified the lesion and fungal biomass of B. cinerea in the early stages of infection in fruit and petals, and demonstrated that B. cinerea penetrates through stomata of strawberry achenes, revealing a previously unrecognized infection route in this host. These data provide a deeper understanding of the B. cinerea -strawberry interaction and will serve as a foundation for future studies seeking novel antifungal treatments against B. cinerea .
Abiotic stress poses significant challenges to the ecological environment and global food security. Early and accurate diagnosis of abiotic stress is essential for modern agriculture. Recently, fluorescence sensing technology has emerged as a valuable tool for monitoring abiotic stress due to its ease of use and capability for spatiotemporal visualization. These probes specifically bind to abiotic stress biomarkers, facilitating the detection of stress responses and advancing related biological research. However, there is a lack of comprehensive reviews on fluorescence probe for abiotic stress, which limits progress in this area. This review outlines the biological markers of abiotic stress, discusses the types and design principles of fluorescence probe, and reviews research on detecting plant responses to such stress. Its goal is to inspire the rational design of fluorescence probe for plant bioimaging, promote early diagnosis of abiotic stress, and enhance the understanding of plant defense mechanisms at the molecular level, ultimately providing a scientific basis for stress management in agriculture.
High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology . Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms , using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change . Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency . Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.
Crop models are essential for evaluating the effects of climate change on crop yields, optimizing agronomic practices, and guiding policy decisions to enhance food security. However, using traditional crop models, including both process-based and statistical models, for regional applications presents significant challenges. Process-based crop models often require extensive, locally-sensed inputs to drive the models, which are generally lacking at the regional level. Meanwhile, statistical crop models depend heavily on training data, but it is often difficult, or even impossible, to find high-quality training data on a large scale. Solar-induced chlorophyll fluorescence (SIF), a more physiologically based proxy for gross primary production (GPP), has shown good potential for estimating GPP and crop yield. We developed a practical SIF-based crop model driven by satellite SIF observations and three readily available datasets: air temperature, vapor pressure deficit, and soil moisture content. The key improvement of our research is to parameterize the fraction of open PSII reaction centers (qL) for crops, and incorporate variations in qL into the SIF-based estimation of crop GPP and yield. Using a leaf-level measurement system, we provided parameters for qL in corn and soybean. We showed that the simulated qL closely matches the measured qL, with R² > 0.95 and RMSE <0.05, even under conditions of high light and/or high temperature, whereas the performance of SIF alone significantly decreased under stress. By using SIF and qL within the mechanistic light response model, one can accurately estimate crop GPP without the need to parameterize various plant physiological processes or nutrient dynamics and management practices. This improvement substantially simplifies the model, reduces the need for driving variables and calibration data, and minimizes associated uncertainties. We applied the model to estimate corn and soybean yields in the U.S. Midwest for the period 2018–2023. A comparison with eddy covariance-based GPP measurements reveals that the simulated GPP accounts for 85 % of the variability in daily observed GPP for corn and 81 % for soybean. The model's performance at the regional scale was assessed by comparing it against county-level crop yield statistics. On average, the model captures 78 % of the county-level yield variability across more than 700 counties during the study period, achieving 76 % for corn and 81 % for soybean, with RMSE values of 14.47 Bu/Acre, and 4.09 Bu/Acre, respectively. The practical, yet mechanistic, SIF-based model introduced in this study represents a significant advance in regional and national crop yield estimation.
Viscosity and hypochlorite (ClO - ) are two crucial microenvironmental species that play significant roles in biological activities. Their abnormal levels are closely associated with numerous common diseases. Therefore, accurate and real-time detection of hypochlorite and viscosity related to inflammatory microenvironment conduces to elucidate the pathogenesis and further diagnose the disease. In this work, based on the strategy of the phenothiazine (PTZ)-dicyanoisophorone (DCO) dyad system, a new dual-response fluorescent sensor (PBI) was successfully constructed for the simultaneous detection and visualization of viscosity and hypochlorite (ClO - ) both in vitro and in vivo. The free sensor emits weak fluorescence in aqueous solution thanks to twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET). However, in a high-viscosity system, the fluorescence emission of the sensor at 459 nm was significantly enhanced. Upon introduction of ClO - in aqueous buffer solution, the PBI exhibited apparent fluorescence enhancement at 577 nm, and showed large Stokes shift (177 nm). The fluorescence responsive mechanism was confirmed using HRMS, 1 H NMR and DFT calculation analysis. Onion and lotus root cells imaging of PBI towards ClO - was implemented. Furthermore, PBI has been successfully applied to the fluorescence imaging of viscosity and exogenous/endogenous hypochlorite in zebrafish.
Methylglyoxal (MG) can be produced via various pathways in plants. MG is toxic for plant cells at high levels, however it acts as a signaling molecule at low levels, just as H 2 O 2 in plants. Therefore, MG detection is very important for investigating its roles in plant cells, especially in plants under environmental stresses. The near-infrared fluorescent probe SWJT-2 is a novel probe with high sensitivity for the rapid detection of MG in human HeLa cells, but at present it is not clear whether the probe can be used to determine MG levels in plant tissues. In this present research, we tried to apply the probe in plant research. Our results showed that 40 min treatment of SWJT-2 (80 μM) can be applied to the detection and imaging of MG levels in tobacco (Nicotiana benthamiana) tissues.
Sensing rice drought stress is crucial for agriculture, and chlorophyll a fluorescence (ChlF) is often used. However, existing techniques usually rely on defined feature points on the OJIP induction curve, which ignores the rich physiological information in the entire curve. Independent Component Analysis (ICA) can effectively preserve independent features, making it suitable for capturing drought-induced physiological changes. This study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve. The results show that the 20-dimensional ChlF features obtained by ICA provide superior classification performance, with Accuracy , Precision , Recall , F1 - score , and Kappa coefficient improving by 18.15%, 0.18, 0.17, 0.17, and 0.22, respectively, compared to the entire curve. This work provides a rice drought stress levels determination method and highlights the importance of applying dimension reduction methods for ChlF analysis. This work is expected to enhance stress detection using ChlF.
Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.
The soil-plant-atmosphere continuum (SPAC) is the interconnected water pathway between soil, plants, and atmosphere, and plays a pivotal role in distribution of water and nutrients in terrestrial ecosystems. In order to understand and predict the dynamics between its components, especially in the context of advancing climate change, it is essential to investigate both the above- and below-ground part of the SPAC with high temporal resolution. However, while methods to observe the above-ground part of the plant are frequently employed, due to its inaccessibility, in-situ measurements of root system activity are still scarce.In this study, we employed a novel combination of sensors at the plot scale to obtain a more complete picture of the dynamics between root water uptake, plant photosynthesis and transpiration, and atmospheric conditions. During the growth season of 2023, we studied the rhizosphere beneath maize plots using spectral electrical impedance tomography, a method which has been shown to be sensitive to soil water content dynamics and root structure and activity. Water transport through the plant stem was monitored via sap flow sensors, while photosynthetic activity and atmospheric conditions were measured continuously using a sun-induced fluorescence sensor and a weather station, respectively. Time series data were analyzed across multiple time windows, focusing on environmental events such as precipitation, prolonged dry periods, and variations in cloud cover.Our results demonstrate we achieved consistently high-quality electrical impedance data throughout the monitoring period. The electrical imaging results exhibit spatially and temporally well resolved diurnal variations in the subsurface polarization behaviour, suggesting a sensitivity to root ion uptake processes. In particular, variability in polarization signatures was more pronounced near the surface early in the season, and shifted to deeper layers later in the season. We attribute this behaviour to the seasonal shift in water availability towards deeper layers, causing a deeper active root water uptake zone. Additionally, rain events promote polarization variability in shallow soil layers. Above-ground data showed cyclical variations both for sap flow and fluorescence measurements and revealed a clear connection to meteorological conditions such as cloud cover or precipitation, confirming the coupling of above-ground plant activity to the atmosphere. Together, the below- and above-ground observations provide a holistic view of the processes within the SPAC, and allow analysis of the complex relations between transpiration, photosynthesis, and root water uptake. To conclude, this study contributes to a deeper understanding of water uptake and plant activity dynamics in crop systems and may inform the breeding of adapted plant varieties, the optimization of agricultural management practices, and the calibration of physiological models describing the SPAC.
We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the code and datasets for reproducing the SfM 3D plant imaging results in the 4TU repository, with a DOI matching an allowed URL.Code · publicThe code and data sets for reproducing the results are available in 4TU repository at https://doi.org/10.4121/e6db8707-10ee-4553-9a98-753f1b4c526a .Open asset ↗4TU repository · 10.4121/e6db8707-10ee-4553-9a98-753f1b4c526alines:52-127Plant phenotyping relevance matchEurope PMC · bioRxiv · checked 15 Sept 2026
Maximizing the nitrogen fixation occurring in rhizobia-legume associations represents an opportunity to sustainably reduce nitrogen fertilizer inputs in agriculture. High-throughput measurement of symbiotic traits has the potential to accelerate the identification of elite rhizobium/legume associations and enable novel research approaches. Plasmid-ID technology, recently deployed in Rhizobium leguminosarum , facilitates the concurrent assessment of rhizobium nitrogen-fixing effectiveness and competitiveness for root nodulation. This study adapts Plasmid-ID technology to function in Sinorhizobium species that are central models for studying rhizobium-legume associations and form economically important symbioses with alfalfa. New Sino-Plasmid-IDs were developed and tested for stability and their ability to measure competitiveness for root nodulation and nitrogen-fixing effectiveness. Rhizobial competitiveness is measured by identifying strain-specific nucleotide barcodes using Next-Generation Sequencing while effectiveness is measured by GFP fluorescence driven by the synthetic nifH promoter. Sino-Plasmid-IDs allow researchers to efficiently study competitiveness and effectiveness in a multitude of Sinorhizobium strains simultaneously.
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e., SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM (R = 0.87, R² = 0.76 before and R = −0.82, R² = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands.
Murphy KM, Johnson BS, Harmon C, Gutierrez J, Sheng H, Kenney S, Gutierrez-Ortega K, Wickramanayake J, Fischer A, Brown A, Czymmek KJ, Bates PD, Allen DK, Gehan MA.
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Phosphorus (P) is an essential macronutrient for cotton (Gossypium hirsutum L.) growth, and plays a crucial role in yield formation. In this context, P deficiency reduces yield due to the limited leaf photosynthesis caused by the disruption of photosynthetic apparatus, and thus can be detected early via photosynthesis-related chlorophyll a fluorescence before visible leaf changes. In addition, the leaf subtending to cotton boll (LSCB) is the primary source of photosynthates, contributing to the boll biomass accumulation. Therefore, it is necessary to develop methods for early assessment of P status in the LSCB, facilitating rapid intervention in cotton production. To satisfy above demand, this study conducted a field experiment to explore the impact of different P application levels [0 (Deficient P), 100 (Critical P), and 200 (Excess P) kg P₂O₅ ha⁻¹] on cotton yield, boll weight accumulation and LSCB photosynthesis. Results showed that the increase of boll weight under P application is a significant factor contributing to yield improvement, and 15-25 days post anthesis is the key development period of cotton boll regulated by P. During this key period, P deficiency decreases the I and P steps of chlorophyll a fluorescence transients (indicating the damage to oxygen-evolving complex), and thus lead to the impaired photosystem II (PSII) and the reduction of electron transfer capacity. Then, the relationship between the leaf phosphorus concentration (LPC) and JIP-test parameters was fitted by Partial Least Squares Regression (PLSR) and showed good accuracy (R²=0.61 in calibration; RMSE=0.05 %, RRMSE=14.00 % in validation). Among the JIP-test parameters, the six ones (i.e. RC/CSₘ, FV/FO, ETO/CSₘ, FV/FM, DIO/RC and PIABS) show the highest correlation to LPC. This study demonstrated that the PLSR model generated using JIP-test parameters has the potential to detect P status in cotton during the key development period, and provided a new insight for optimizing P nutrient management in cotton production.
Abstract Global climate change intensifies extreme weather-induced crop losses, necessitating drought-resilient crops. Qingke (Hordeum vulgare var. nudum), the staple barley of Tibet's climate-vulnerable plateau, offers genetic insights into stress adaptation. We established a seedling-stage drought evaluation system identifying four biomarkers: fresh weight, chlorophyll fluorescence (Fv/Fm, NPQ, RFD), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) accumulation. Systematic screening of the physiological traits revealed these parameters as optimal predictors of drought tolerance, enabling rapid germplasm classification. Application to three uncharacterized cultivars (ZY673, ZY1403, KL14) demonstrated weak drought resistance across all lines, with ZY1403 showing extreme sensitivity. This standardized protocol for hulless barley integrates photosynthetic efficiency and oxidative stress metrics, providing breeders with actionable thresholds for climate-resilient crop development in montane agroecosystems.
Perri, M. · Khan, M. S. · Wallabregue, A. L. D. · Voloboeva, V. · Ridgway, A. M. · Smith, E. N. · Bolland, H. · Hammond, E. M. · Conway, S. J. · Weits, D. A. · Flashman, E.
O_LILow oxygen signalling in plants is important in development and stress responses. Measurement of oxygen levels in plant cells and tissues is hampered by a lack of chemical tools with which to reliably detect and quantify endogenous oxygen availability. We have exploited hypoxia-activated fluorescent probes to visualise low oxygen (hypoxia) in plant cells and tissues. C_LIO_LIWe applied 4-nitrobenzyl (4NB-) resorufin and methyl-indolequinone (MeIQ-) resorufin to Arabidopsis thaliana whole cells and seedlings exposed to hypoxia (1% O2) and normoxia (21% O2). Confocal microscopy and fluorescence intensity measurements were used to visualise regions of resorufin fluorescence. C_LIO_LIBoth probes enter A.thaliana whole cells and are activated to fluoresce selectively in hypoxic conditions. Similarly, incubation with A.thaliana seedlings resulted in hypoxia-dependent activation of both probes and observation of fluorescence in hypoxic roots and leaf tissue. MeIQ-Resorufin was used to visualise endogenous hypoxia in lateral root primordia of normoxic A.thaliana seedlings. C_LIO_LIOxygen measurement in plants until now has relied on invasive probes or genetic manipulation. Use of these chemical probes to detect applied and endogenous hypoxia has the potential to facilitate a greater understanding of oxygen dynamics in plant cells and tissues, allowing correlation of oxygen concentrations with adaptive and developmental responses to hypoxia. C_LI
Hydrogen sulfide (H 2 S) plays a vital role in plant physiology and stress adaptation, but the detection of endogenous H 2 S remains a challenge. In this work, a near-infrared fluorescent probe (NIR-BOD-HS) was synthesized using boron-dipyrromethene (BODIPY) as the raw material, which showed a good linear relationship in the concentration range of 0.1-70 μM and a detection limit of 56 nM. The long-wavelength emission (712 nm) reduced the interference of plant autofluorescence and improved the imaging quality. The probe combined with fluorescence imaging technology nondestructively realized the spatiotemporal distribution signal of H 2 S in the deep tissues of plants. In addition, the dynamic changes of H 2 S content during seed germination and seedling growth under abiotic stress were also demonstrated through the changes in fluorescence signals. This study helps to understand the physiological response mechanism of plants under abiotic stress and provides a scientific basis for further research on plant imaging and agricultural production.
Systemic fluorescence tracers introduced into crop plants provide an active signal for crop–weed differentiation that can be exploited for precision weed management. Rhodamine B (RB), a widely used tracer for seeds and seedlings, possesses desirable properties; however, its application as a seed treatment has been limited due to potential phytotoxic effects on seedling growth. Therefore, investigating mitigation strategies or alternative systemic tracers is necessary to fully leverage active signaling for crop–weed differentiation. This study aimed to identify and address the phytotoxicity concerns associated with Rhodamine B and evaluate Rhodamine WT and Sulforhodamine B as potential alternatives. A custom 2D fluorescence imaging system, along with analytical methods, was developed to optimize fluorescence imaging quality and facilitate quantitative characterization of fluorescence intensity and patterns in plant seedlings, individual leaves, and leaf disc samples. Rhodamine compounds were applied as seed treatments or in-furrow (soil application). Rhodamine B phytotoxicity was mitigated by growing in a sand and perlite media due to the adsorption of RB to perlite. Additionally, in-furrow and seed treatment methods were tested for Rhodamine WT and Sulforhodamine B to evaluate their efficacy as non-phytotoxic alternatives. Experimental results demonstrated that Rhodamine B applied via seed pelleting and Rhodamine WT used as a direct seed treatment were the most effective approaches. A case study was conducted to assess fluorescence signal intensity for crop–weed differentiation at a crop–weed seed distance of 2.5 cm (1 inch). Results indicated that fluorescence from both Rhodamine B via seed pelleting and Rhodamine WT as seed treatment was clearly detected in plant tissues and was ~10× higher than that from neighboring weed plant tissues. These findings suggest that RB ap-plied via seed pelleting effectively differentiates plant seedlings from weeds with reduced phytotoxicity, while Rhodamine WT as seed treatment offers a viable, non-phytotoxic alternative. In conclusion, the combination of the developed fluorescence imaging system and RB seed pelleting presents a promising technology for crop–weed differentiation and precision weed management. Additionally, Rhodamine WT, when used as a seed treatment, provides satisfactory efficacy as a non-phytotoxic alternative, further expanding the options for fluorescence-based crop–weed differentiation in weed management.
Several home pesticides are organophosphorus compounds. These compounds inhibit the enzyme acetylcholinesterase, causing harmful effects on the health of biota. Through this research, the usefulness of Glycine max (soybean) and Cichorium intybus (chicory) plants as sentinels of organophosphorus compounds in the environment was successfully tested. Different concentrations of the insecticide chlorpyrifos were tried out. Damage to plants at the photosynthetic apparatus level was evaluated by measuring the high temporal resolution variable chlorophyll fluorescence (OJIP test). Several parameters derived from this test indicated a high level of damage in both species even at the mean dose recommended for use in the field. However, a few parameters did not consistently reflect damage in leaves. A drop in the values of the maximum fluorescence (F M ), the quantum yield of electron transport flux, transport between quinones A and B (ET 0 /ABS) and the maximal quantum yield of PSII (TR 0 /ABS) could alert us about the presence of organophosphates in the environment. An increase in the dissipated energy flux per reaction center (DI 0 /RC) values was also observed. The species showed different sensitivities, with soybean plants being the most sensitive. The OJIP transient thus becomes a valuable rapid, non-destructive tool for biomonitoring this class of pesticides in the environment.
Abiotic stress severely hinders plant growth and development, resulting in a considerable reduction in crop yields. Salicylic acid (SA) serves as a central signal mediating abiotic stress responses in plants. Real-time fluorescence tracking using specific probes can enhance our understanding of the SA-triggered modulation underlying these events. However, in complicated living plant microenvironments, selective recognition and bioimaging of SA is a great challenge for scientists due to the severe background interference and SA analogues. Herein, an efficient fluorescence probing technology employing a highly selective rhodamine probe-phoxrodam was developed, which realizes the precise bioimaging of SA in salt-stressed plant seedlings. Experimental findings reveal that phoxrodam demonstrates exceptional selectivity (fluorescence intensity: I Phoxrodam+SA /I Phoxrodam+SA analogues > 4.29-fold), high sensitivity (limit of detection = 6.42 nM, fluorescence quantum yield: Φ Phoxrodam+SA = 0.36) and good anti-interference properties. Furthermore, we confirmed that phoxrodam accurately detects SA in the roots of salt-stressed wheat seedlings, the low-temperature resistance of Nicotiana benthamiana and the heavy metal resistance of pea seeds, using in vivo confocal imaging. This study provides a feasible strategy for efficiently tracking plant signalling molecules and promotes the in-depth research of SA-mediated physiological mechanisms, laying a key foundation for the future development of new immune activation inducers.
Durum wheat production is concentrated in Mediterranean climate regions, making it essential to develop cultivars that adapt to its changing conditions, including water and heat stress. In this regard, photosynthetic capacity estimates may help improve the selection of the most adapted cultivars. However, the cost and inherent low throughput of the usual methodological approaches makes, in many cases, phenotyping unfeasible, particularly under field conditions. This study uses leaf photosynthetic measurements taken with a low-cost handheld chlorophyll sensor (MultispeQ Photosynq) and a biomass sensitive sensor (GreenSeeker) measuring the normalized difference vegetation index (NDVI) to assess the performance of six modern durum wheat cultivars. The sensors were employed at anthesis and grain filling under two different types of management (rainfed and support irrigation) for two growing seasons. Compared to irrigated plants, rainfed trials had significantly lower photosynthetic performance during the two phenological stages evaluated. Significant genotype differences in steady-state fluorescence yield (Fs) and maximum fluorescence yield (Fm′) across treatments and crop seasons were found. This study shows that leaf chlorophyll fluorescence parameters can be used to select modern wheat cultivars with an open-source, low-cost, handheld sensor (Photosynq).
ABSTRACT Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R² > 0.58), while the poro-fluorometer was efficient for predicting net CO₂ assimilation (R² > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R² > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.
Remotely sensed top-of-the-canopy (TOC) SIF is highly impacted by non-physiological structural and environmental factors that are confounding the photosystems' emitted SIF signal. Our proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach, capable of accounting physically for the main confounding factors, i.e. , SIF scattering and reabsorption within a leaf, by canopy structures, and by the soil beneath. Here, we propose a novel SIF downscaling method that separates the structural component from the functional physiological component of TOC SIF signal by using the 3D Discrete Anisotropic Radiative Transfer (DART) model coupled with the leaf-level fluorescence model Fluspect-CX, and estimates the Fluorescence Quantum Efficiency (FQE) at photosystem level. The method was first applied on in-situ diurnal measurements acquired at the top of the canopy of an alfalfa crop with a near-distance point-measuring FloX system. The retrieved photosystem-level FQE diurnal courses correlated significantly with photosynthetic yield of PSII measured by an active leaf florescence instrument MiniPAM ( R = 0.87, R 2 = 0.76 before and R = −0.82, R 2 = 0.67 after 2.00 pm local time). Diurnal FQE trends of both photosystems jointly were descending from late morning 9.00 am till afternoon 4.00 pm. A slight late-afternoon increase, observed for three days between 4.00 and 7.00 pm, could be attributed to an increase in FQE of PSI that was retrieved separately from PSII. The method was subsequently extended and applied to airborne SIF images acquired with the HyPlant imaging spectrometer over the same alfalfa field. While the input canopy SIF radiance computed by two different methods, i) a spectral fitting method (SFM) and ii) a spectral fitting method neural network (SFMNN), produce broad and irregularly shaped (skewed) histograms (spatial coefficients of variation: CV = 29–35 % and 14–20 %, respectively), the retrieved HyPlant per-pixel FQE estimates formed significantly narrower and regularly bell-shaped near-Gaussian histograms (CV = 27–34 % and 14–17 %, respectively). The achieved spatial homogeneity of resulting FQE maps confirms successful removal of the TOC SIF radiance confounding impacts. Since our method is based on direct matching of measured and physically modelled canopy SIF radiance, simulated by 3D radiative transfer, it is versatile and transferable to other canopy architectures, including structurally complex canopies such as forest stands. • A novel solar-induced fluorescence (SIF) downscaling method based on DART modeling. • Method removes confounding structural impacts from top-of-canopy SIF observations. • Applied to in-situ SIF measurements, it produced FQE diurnal courses of alfalfa crop. • Adapted to airborne SIF images, it mapped FQE spatial variation.
Low-cost, minimally invasive microscopy for tracking cellular dynamics in living plants within their natural ecosystems is crucial for addressing fundamental questions in plant ecology and biology. However, existing solutions are constrained by coarse resolution, limited field-of-view (FoV), and poor deployability in natural settings. Here, we utilize a compact, portable microscope ("miniscope") for label-free (autofluorescence) imaging in living poplar wood. We systematically implement and evaluate multiple computational methods to enhance resolution and FoV. Our optimal computational pipeline, comprising maximal intensity projection, deconvolution, and flat-field correction, increases resolution by up to 39% on-axis and up to 49% at the field edges, resolving features of 2.87 μm, averaged over a FoV of ∼1 mm (diameter), compared with a 4.34 μm baseline. We demonstrate microscopy within the tissue of a living poplar plant in our greenhouse, observing the embolism of vessel elements, wound response, and tissue deformation from moisture evaporation.
Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging, plant phenotyping enables precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures.
User-friendly handheld plant phenotyping devices, such as the MultispeQ, provide quick and easy measurements that effectively capture the dynamic nature of photosynthesis. This study demonstrates the added value of integrating measurements of such devices with both process-based and empirical modeling approaches for estimating the maximum leaf photosynthetic capacity ( A m a x ) and biomass production (DMP) of potato crops. Utilizing leaf fluorescence measurements, such as the efficiency of photosystem II ( ϕ 2 ) and the electron transport rate, gathered from two fields in the Netherlands from May to September 2019, we determined the A m a x to be 34 kg C O 2 ha −1 hr −1 with a standard deviation of 6.6 kg C O 2 ha −1 hr −1 . By incorporating dynamic photosynthetic parameters, leaf area index (LAI) retrieval, and crop modeling techniques to scale assimilation from the leaf to the canopy level, we successfully reduced the discrepancy between simulated and measured dry matter production in 16 out of 18 cases, offering significant advantages over fixed, literature-based photosynthetic parameter values.
Studying cell-to-cell heterogeneity is essential to understand how unicellular organisms respond to stresses. We introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits, particularly their interactions within individual cells that have identical genotypes, cellular contexts and histories. Our approach combines single-cell imaging of chlorophyll a fluorescence with machine learning and we study light stress responses in Chlamydomonas reinhardtii as a proof-of- concept. This framework allows us to score the extent of high-light responses such as state transitions (qT) and high-energy quenching (qE), to reveal significant cell-to-cell heterogeneity and to reveal a strong correlation between qT and qE, undetectable in bulk measurements. This study highlights the value of single-cell phenotypic analysis for for investigating light stress responses in unicellular organisms. We detail the key aspects that come into play to generalize the method to other complex stress responses involving multiple traits.
This study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels, using ordinary off-the-shelf cameras and a UV light. A mobile phone camera is used to detect the fluorescence emission in the blue wavelength region of rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red-to-far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission at each pixel point from the acquired image data. The spectral distributions can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions by the proposed method are compared to the direct measurements obtained with a spectroradiometer and the estimates obtained using the minimum norm estimation method. The estimated images of fluorescence emissions are presented for rice grains and green leaves. The reliability of the proposed estimation method is demonstrated.
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R 2 > 0.80, nRMSE 2 > 0.90, nRMSE 2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R 2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's chlorophyll measurement and trait data in a public Zenodo repository, which is an allowed URL. No separate author analysis code URL is given (analyses were in Matlab/R), so the qualifying asset is the deposited dataset.Dataset · publicData are available in Zenodo repository found by https://zenodo.org/records/14615430.Open asset ↗Zenodo · 14615430pdf-page:14 lines:1-62Plant phenotyping relevance matchEurope PMC · checked 14 Sept 2026
ABSTRACT Background The growing demand for rare-earth elements (REEs), particularly dysprosium (Dy), in part driven by clean energy technologies, underscores the need for sustainable extraction methods. Recovery of Dy, particularly from geographically distributed waste sources is challenging. This gap positions phytomining—a technique using plants to accumulate metals— as a promising alternative. However, plant species differ in their ability to accumulate metals in high concentrations, necessitating efficient screening methods. In this study, we developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues. Results Our Dy detection method exploits Dy’s unique spectroscopic properties for sensitive and efficient analysis, enabling detection of concentrations as low as 0.3 µM. By incorporating sodium tungstate (Na WO) as a fluorescence enhancer, we achieved robust emissions at 480 and 580 nm, facilitating Dy quantification in complex plant matrices. Additionally, time-resolved fluorescence techniques reduced background autofluorescence from plant tissues, enhancing signal specificity. Validation against Inductively Coupled Plasma Mass Spectrometry (ICP-MS) demonstrated strong correlation. Greenhouse trials confirmed the method’s utility for screening Dy accumulation in living plants and highlight the potential for rapid standoff detection. Conclusions This fluorescence-based approach offers a scalable, efficient tool for identifying Dy-accumulating plants, advancing phytomining as a sustainable strategy for REE recovery.
The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation of canopy SIF models is limited by the lack of methods that combine direct, independent, and complementary measurements of the full fluorescence spectrum at the leaf and canopy levels. Here, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra. The approach is demonstrated with measurements in a rice crop at two contrasting stages of canopy development. We measured leaf reflectance, transmittance, and fluorescence spectra in situ, and subsequently inverted leaf structural and biochemical parameters and determined the leaf fluorescence quantum efficiency (FQE) using the Fluspect-Cx model. Two FQE inversion methods (Inversion-IIA and Inversion-IIB) were tested for the forward simulation of leaf fluorescence spectra. Leaf fluorescence spectra were then scaled up to the canopy level using 1D, 2D, and 3D radiative transfer schemes (SCOPE, mSCOPE, and DART), and compared with the direct canopy fluorescence spectral observations measured under red, green, blue, and white illumination. The validation results demonstrate that accounting for 3D canopy structure, as in the DART model, is critical to successfully scale the fluorescence spectrum from the leaf to the canopy level, whereas 1D SCOPE or even 2D mSCOPE were unable to fully reproduce the canopy fluorescence spectra. The results also demonstrate that the Inversion-IIB method matches relatively well the measurements with mean relative absolute errors (MRAE) of 20 %, 37 %, and 43 % versus Inversion-IIA with mean relative absolute errors (MRAE) of 62 %, 100 %, and 108 % for DART, mSCOPE, and SCOPE, respectively. We suggest that our validation approach is transferable to other plant species and canopy geometries, providing a means to standardize and evaluate the performance of canopy SIF models and improve our understanding of canopy SIF observations.
The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline-alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline-alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the QA⁻reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs.
Understanding the diurnal and seasonal regulation of photosynthesis is an essential step to quantify and model the impact of the environment on plant function. Although the dynamics of photosynthesis have been widely investigated in terms of CO2 exchange measurements, a more comprehensive view can be obtained when combining gas-exchange and chlorophyll fluorescence (ChlF). Until now, integrated measurements of gas-exchange and ChlF have been restricted to short-term analysis using portable infrared gas analyzer systems that include a fluorometer module. In this communication we provide a first-time demonstration of long-term, in situ and combined measurements of photosynthetic gas-exchange and ChlF. We do so by integrating a new miniature pulse amplitude modulated-fluorometer into an existing system of automated chambers to track photosynthetic gas-exchange of leaves and shoots in situ. The setup is used to track the dynamics of the light and carbon reactions of photosynthesis at a 20-min resolution in leaves of silver birch (Betula pendula Roth) during summertime. The potential of the method is illustrated using the ratio between electron transport and net assimilation (ETR/ANET), which reflects the internal electron use efficiency of photosynthesis. The setup successfully captured the diurnal patterns in the ETR/ANET during summertime, including a large increase in noon ETR/ANET in response to a period of high temperatures and relatively low soil moisture, pointing to a drastic decrease in electron-use efficiency. The observations emphasize the value of combined and long-term in situ measurements of ChlF and gas-exchange, opening new opportunities to investigate, model and quantify the regulation of photosynthesis in situ and the connection between ChlF and photosynthetic gas-exchange. The next steps, potential and limitations of the approach are discussed.
Leaf chlorophyll content (LCC) is an important indicator of photosynthetic capacity. Sun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC. The far-red to red ratio of chlorophyll fluorescence (Fᵣₐₜᵢₒ) has been used to empirically estimate LCC in some previous studies. While these studies support the use of the Fᵣₐₜᵢₒ for LCC estimation, its theoretical underpinning remains less well-defined and its effectiveness across a wider range of scenarios remains unclear. In this study, we established the relationship between the Fᵣₐₜᵢₒ and LCC using the light use efficiency (LUE)-based SIF model and spectral invariant radiative transfer theory. Firstly, the LUE-based SIF model demonstrates that the change in the leaf Fᵣₐₜᵢₒ is controlled by the ratio of the fluorescence escape fraction (i.e., fₑₛc from the photosystem to the leaf surface) at the corresponding bands. Secondly, a fₑₛc modeling approach is presented using the spectral invariant theory and thus the fₑₛc ratio is linked to LCC. Theoretical analysis shows that the Fᵣₐₜᵢₒ has a strong correlation with LCC, which explains over 90 % of the variation in Fᵣₐₜᵢₒ. Both experimental measurements and model simulations from a radiative transfer model Fluspect were used to validate the relationship between LCC and three Fᵣₐₜᵢₒ (i.e., Fratio↑, Fratio↓ and Fratiotot), which were derived from the upward and downward SIF of leaves, as well as the total SIF observed from both sides. The Fluspect simulations were used to assess the sensitivity of the Fᵣₐₜᵢₒ-LCC relationship to the leaf structure. Two types of experimental measurements, including the field measurements of three crops and the laboratory measurements of 20 tundra plants, were employed to examine the species dependence of the Fᵣₐₜᵢₒ-LCC relationship. The performance of Fᵣₐₜᵢₒ for LCC estimation was evaluated and compared with spectral indices and the PROSPECT model using the experimental measurements and leave-one-out cross-validation (LOOCV) approach. Both the Fluspect simulations and the experimental measurements indicate that the Fᵣₐₜᵢₒ is strongly correlated with LCC for a wide range of leaf scenarios. The Fᵣₐₜᵢₒ-LCC relationship remains relatively stable across different leaf structures and plant species, since the relationship is almost consistent. The LOOCV of experimental measurements shows that the Fᵣₐₜᵢₒ provides promising and robust LCC estimates, with the Fratiotot performing the best. The Fratiotot outperforms spectral indices, reducing the RMSE for LCC estimation by 19.5 %-93.9 %. Furthermore, compared to the PROSPECT model, the Fᵣₐₜᵢₒ achieves a reduction in RMSE by 30.4 %-77.8 %. These results demonstrate that the Fᵣₐₜᵢₒ is effective for estimating LCC of diverse plant species. This study advances our understanding of the relationship between the Fᵣₐₜᵢₒ and LCC, supporting the use of SIF signals for remote sensing of LCC.
Citrus Huanglongbing (HLB) poses a significant threat to citrus orchards. Timely HLB screening of citrus trees is essential for citrus orchard management. This study developed a multi-excitation fluorescence imaging system based on the correlation between HLB stress and flavonoid fluorescence characteristics. The feasibility of using fluorescent images to classify healthy, macular (nutrient-deficient, not relate to HLB) and HLB-infected citrus fruits was explored. Initially, three-dimensional fluorescence spectra of citrus peels at maturity were scanned and the obtained Excitation-Emission Matrices (EEMs) were analyzed to screen four fluorescence characteristic regions (FCR1-FCR4) that were sensitive to HLB-infected citrus. Subsequently, four fluorescence imaging conditions (G1: EX = 365 ± 20 nm, EM = 525 ± 20 nm, G2: EX = 415 ± 20 nm, EM = 525 ± 20 nm, B1: EX = 308 ± 20 nm, EM = 450 ± 20 nm, and B2: EX = 365 ± 20 nm, EM = 450 ± 20 nm) were designed based on characteristic fluorescence bands. The imaging system primarily utilizes standard CMOS cameras and optical filters for image acquisition, offering significant advantages in terms of operational simplicity and cost-effectiveness. An HLB classification model was constructed using the Random Forest (RF) algorithm based on color feature parameters of fluorescence images, with a classification accuracy of up to 87.5 %. When the top 10 image feature parameters with the highest contribution rate were selected to construct the classification model considering the equipment cost, the accuracy is 83.33 %. This study demonstrated that fluorescence imaging utilizing flavonoid fluorescence characteristics enables non-destructive and rapid detection of citrus HLB. This approach provides valuable data and technical support for decision-making on spring orchard cleanup and control of HLB.
Powdery mildew disease threatens wheat production worldwide, and early detection is of great significance for disease control and maximizing yield and quality. To improve early remote sensing detection of wheat powdery mildew, solar-induced chlorophyll fluorescence (SIF) parameters were extracted using three-band Fraunhofer line discrimination (3FLD) and reflectance index approaches, and vegetation index (VI) was calculated by hyperspectral reflectance. All features and feature subsets of different data sources were used as inputs to multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms to construct a wheat powdery mildew monitoring model. SVM includes linear kernel function (LK), polynomial kernel function (PK), and Gaussian radial basis function (RBF). Under wheat powdery mildew stress, wheat canopy reflectance showed a blue shift, and fluorescence weakened. The correlation between SIF−A intensity and disease index (DI) in the O²−A band extracted using the 3FLD method was the highest at −0.781, showing that the SIF parameter was useful for monitoring powdery mildew. Whether based on all features or feature subsets, the RBF model achieved the highest model accuracy, followed by the RF and the MLR. In the feature subset, the accuracy ranges of RBF, LK, and PK models are 0.740−0.871, 0.724−0.850, and 0.716−0.841 respectively. The SIF+VI in the RBF model is more useful for early and stable disease monitoring of wheat powdery mildew. This innovative technical solution is expected to support the early diagnosis of wheat powdery mildew, significantly improving disease prevention and control efficiency and effectiveness.
ABSTRACT Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost‐effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.
The results of the application of the chlorophyll fluorescence and proline content (PC) methods for assessing the drought tolerance of oilseed radish varieties are presented. The interval of Relative humidity of soil (RHS) 60.8% - 35.6% with raising air temperature (0.8°C day-1) and decrease leaf RWC (6.7% day-1) was investigated. An increase F0 and Fst of 1.11 and 0.42 relative fluorescence units (RFU) per 1% decrease in RHS, 2.33 and 0.88 RFU per 1°C increase in air temperature, and 0.35 and 0.13 RFU per 1% decrease in leaf RWC was showed. A decrease Fpl, Fm at 6.57 and 50.1 RFU per 1% decrease RHS, 13.8 and 105.2 RFU per 1°C increase temperature and 2.1 and 15.7 RFU per 1% decrease leaf RWC was determined. An increase PC of 0.20 and 0.07 nmol gDW-1 per RFU increase F0 and Fst and 1.14 and 8.73 nmol gDW-1 per RFU decrease Fpl and Fm was noted. An increase PC of 5.74 nmol gDW-1 per 1% decrease in RHS and 12.05 nmol gDW-1 per 1°C increase in air temperature was proved.
The widely recognized phytohormone, salicylic acid (SA), serves not only as an exogenous additive for fruits and vegetables but, more crucially, as an in vivo regulator of the entire plant growth process. Consequently, it is essential to achieve both in vitro detection and in vivo imaging analysis of the plant hormone SA. In this study, a biocompatible supramolecular probe was crafted using a "label-free" SA aptamer as the host for an aggregation-induced emission (AIE) organic small molecule. Upon recognizing the target SA, the aptamer is not readily digested by the exonuclease (Exo.I), which permits the aptamer sequence to be largely preserved. Under these conditions, the aptamer can be further integrated with the AIE organic small molecule, thereby enabling it to produce fluorescence emission due to restricted intramolecular motion. The probe can efficiently accomplish in vitro detection of SA within a linear range of 0.3-70 μM, with a detection limit reaching 0.09 μM. Most importantly, building on the achievement of fluorescence imaging monitoring of SA in plants, this work has developed a fluorescence image processing program utilizing the Java algorithm. This program facilitates the one-click conversion of monochrome images to pseudo-color in conventional fluorescence imaging, thereby enhancing the ability of the naked eye to discern image details.
Introduction In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF's estimation capabilities using various datasets. Results and discussion The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R ² =0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management.
Photosynthesis drives crop growth and production, and strongly affects grain yields; therefore, it is an ideal trait for wheat drought resistance breeding. However, studies of the negative effects of drought stress on wheat photosynthesis rates have lacked accurate evaluation methods, as well as high-throughput techniques. We investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data. We analyzed various morpho-physiological traits involved in wheat drought tolerance, including tiller number, leaf relative water content, and malondialdehyde content, to determine the relationships between drought resistance and hyperspectral and ChlF data. The results showed that the spectral first derivative ratio (FDR) between drought stress and control conditions in the 680-760 nm region was closely related to photosynthetic capacity and drought tolerance and that hyperspectral imaging can be used to monitor ChlF parameters, with bands sensitive to ChlF identified in two spectral regions (539-764 nm and 832-989 nm). The spectral first derivative at 989 nm had the strongest linear relationship with the minimal fluorescence (R 2 = 0.49). An uninformative variable elimination algorithm indicated that FDRs in the green (504-609 nm), red (724-751 nm), and near-infrared (944-946 nm) light regions had great potential as indices of drought resistance. A support vector machine model based on the FDRs of these characteristic bands identified wheat drought resistance with 97.33% accuracy. These findings provide insight into the application of high-throughput technologies in studying drought resistance and photosynthesis in wheat.
Key message A novel fluorescent i-motif DNA silver nanoclusters system has been developed for visualization of reactive oxygen species in plants, enabling the detection of intracellular signaling in plant cells. Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo. However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli, such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.
Daily water stress reflects the water stress status of crops on a specific day, which is crucial for studying drought progression and guiding precision irrigation. However, accurately monitoring the daily water stress remains challenging, particularly when eliminating the impact of historical stress and normal growth. Recent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively. Remote sensing observations, such as the solar-induced chlorophyll fluorescence (SIF) and reflectance, offer information on the crop canopy structure, physiology or their combination. However, the sensitivity of different structural, physiological or combined remote sensing variables to the daily water stress remains unclear. We investigated this issue via continuous measurements of active fluorescence, leaf rolling, and canopy spectra of maize under different irrigation conditions. The results indicated that with increasing water stress, vegetation exhibited significant coordinated diurnal variations in both structure and physiology. The influence of water stress was minimal in the morning but peaked at noon. The morning-to-noon ratio (NMR) of the apparent SIF yield (SIFy), in which only the effect of the photosynthetically active radiation (PAR) is eliminated and in which both structural and physiological information is incorporated, exhibited the highest sensitivity to water stress variations. This NMR of the SIFy was followed by the NMR of the normalized difference vegetation index (NDVI) and the NMR of the canopy fluorescence emission efficiency (ΦFcanopy) obtained via the fluorescence correction vegetation index (FCVI) method, which primarily reflect structural and physiological information, respectively. This study highlights the advantages of utilizing diurnal vegetation structural and physiological variations for monitoring daily water stress levels.
Plant developmental biology necessitates precise three-dimensional (3D) tracking of dynamic processes in live plants, and the 3D imaging technique in developmental bioimaging requires suitable fluorophores to achieve single-cell resolution imaging. Herein, we have designed a series of plasma membrane fluorescent dyes with a number of excellent properties and established a single-cell resolution imaging tool based on these dyes for three-dimensional imaging of various tissues and organs in living plants. The designed plasma membrane fluorescent dyes not only have the advantages of rapid wash-free staining, highly specific targeting, high brightness and high contrast imaging, ultralong imaging time and low biotoxicity, but also effectively avoid the autofluorescence interference of chlorophyll in cells, allowing for the development of a three-dimensional imaging approach of living plant organs with single-cell resolution. The three-dimensional histological structures of various organs of adult Arabidopsis thaliana, including roots, leaves, flowers, and fruits, were successfully reconstructed with single-cell resolution using this model plant. Furthermore, the 3D imaging method was employed to track the dynamic changes in tissue and organ morphology at the single-cell level during key plant developmental processes, including seed germination, root development, leaf growth, and anther development.
Macroscopic phenotypic changes in plants are frequently employed as a means of evaluating the biological response of plants to external environmental stresses. However, the lack of effective observational tools at the microscopic cellular level hinders the ability to fully comprehend the intricacies of this response. Herein, we developed a plasma membrane fluorescent dye with target-activated green emission complemented with conventional FM dyes, and established a four-dimensional (4D) imaging approach based on this dye for spatio-temporal monitoring of plasma membrane dynamics during cellular responses to external environmental stress. A green fluorescent dye, designated FMG-DBO, was constructed by modifying the bridged unit between the aniline donor and the pyridinium acceptor. Its green emission can be combined with that of conventional FM dyes, enabling high-resolution imaging of plant leaf cells containing chlorophyll. The anchoring ability of the dyes was enhanced by incorporating a rigid diaza[2.2.2]octane unit as an anti-permeability group. The long retention time of the FMG-DBO dye in the plasma membrane enables the tracking of three-dimensional dynamics of the plasma membrane of plant cells. Consequently, an FMG-DBO-based four-dimensional imaging approach was established to monitor dynamic changes of plant cells under external environmental stress at the cellular level. The biological responses of two different drought-tolerant rice root cells to drought stress were examined by this four-dimensional imaging approach. It was observed that the two types of rice root cells exhibited disparate responses to the drought environmen. This approach offers alternative cell-level visualization tools for evaluating the biological responses of plant cells under environmental stress.
This study proposes a method for estimating fluorescence emission spectra from images of plant grain and leaves without using a spectrometer. We construct two types of multiband imaging systems with six channels using ordinary off-the-shelf cameras and UV light. A mobile phone camera is used to detect fluorescence emission in the blue wavelength region for rice grains. For plant leaves, a small monochrome camera is used with additional optical filters to detect chlorophyll fluorescence in the red to far-red wavelength region. A ridge regression approach is used to obtain a reliable estimate of the spectral distribution of the fluorescence emission from the acquired image data. The spectral distribution can be estimated by optimally selecting the ridge parameter without statistically analyzing the fluorescence spectra. An algorithm for optimal parameter selection is developed using a cross-validation technique. In the experiments using real rice grains and green leaves, the estimated fluorescence emission spectral distributions are compared to direct measurement by a spectroradiometer and estimation by the minimum norm estimation method. The reliability of the proposed estimation method is demonstrated.
Sun-induced chlorophyll fluorescence (SIF) has recently emerged as a proxy for canopy photosynthesis of vegetation and offers a promising approach for scalable remote crop monitoring. Effective application of SIF for crop monitoring requires better understanding of the processes that cause SIF-photosynthesis decoupling at leaf and canopy scales. To answer this challenge, we developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox). First, we evaluated the performance of OctoFlox and found high stability and cross-channel comparability. Second, we performed an evaluation of different SIF retrieval methods to identify the best suited retrieval method for our system configuration for both red (SIFʀᴇᴅ) and far-red SIF (SIFꜰʀ). We then deployed OctoFlox within Soil-Plant Atmosphere Research (SPAR) controlled-environment chambers that enable measurement of canopy-scale SIF and photosynthesis with matching footprints. We analyzed the effect of the SPAR chamber tops on the light environment and found minimal impact on the spectral response. Lastly, we examined the response of SIF and canopy photosynthesis using the SPAR chambers. Soybean plants were evaluated at pre-drought, drought (irrigated at 100 % field capacity vs. 33 % field capacity for 2 weeks) and after 1 week recovery from drought. During early growing season, SIFꜰʀ and SIFʀᴇᴅ exhibited similar responses. At peak growing season (R2 growth stage), SIFꜰʀ increased during afternoon depression of photosynthesis, but SIFʀᴇᴅ decreased. We demonstrate that pairing SIF instrumentation with SPAR chambers can accelerate understanding SIF-photosynthesis relationships from diurnal to seasonal scales in relation to crop physiological responses to abiotic stress. We provide user recommendations for future applications using OctoFlox and SPAR chambers for co-measuring SIF and GPP.
Agricultural production models predict crop yield by accounting for a variety of species, cultivar, farming management, and environmental impacts on crop photosynthesis. Without suitable constraints, however, large uncertainties may exist in simulations of crop photosynthesis. Recent advances in retrieving solar-induced chlorophyll fluorescence (SIF) at the top-of-canopy (TOC) have provided a promising measurement for crop photosynthesis. Within the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFₜₒc) which can be measured by remote sensing platforms. The new model (APSIM-SIF) first estimates the leaf-level chlorophyll fluorescence emitted over the full SIF spectrum (SIFₜₒₜ_fᵤₗₗ) according to CO₂ assimilation in crops. The model then mechanistically decomposes the conversion from SIFₜₒₜ_fᵤₗₗ to SIFₜₒc into two factors: the SIF band conversion factor (ɛ) and the fluorescence escape ratio (fₑₛc) that represent the impact of leaf physiological status and plant structure properties, respectively. ɛ can be estimated using leaf structural and biochemical parameters as inputs; fₑₛc for near-infrared SIF can be expressed as a function of directional reflectance in the near-infrared region (RNIR), Normalized Difference Vegetation Index (NDVI), and the fraction of PAR absorbed by crops (fAPAR). The APSIM-SIF model determined more than 90% of the variation in gross primary productivity (GPP), aboveground biomass and leaf area index (LAI) measurements for maize (Zea mays L.) at two AmeriFlux sites in the U.S. Midwest and it also captured the seasonality of SIF (R² = 0.84) and GPP (R² = 0.81) well at an irrigated maize site in China. The APSIM-SIF model was also applied to the simulation of TOC SIF emission of maize and soybean (Glycine max L.) in the U.S. Midwest during the 2018 growing season. The simulated SIFₜₒc accounted for more than 75% of the variability of daily satellite SIF observations for grid squares with more than 70% crop area. The main contribution of this study lies in two aspects: (1) a physically-based framework is proposed to incorporate the SIF module to the APSIM-DCaPST model, and (2) the two important factors used in this framework (ɛ and fₑₛc) remains largely constant during the peak growing season. These findings provide a theoretically robust and operational basis for linking SIF observations with crop growth.
Gallic acid (GA) is a secondary metabolite derived from plant phenolics. It is essential to maintain normal physiological activities in plants facing adversity. This, in turn, helps maintain crop integrity. Consequently, the surveillance of GA levels in plants is of significant importance. This study developed a near-infrared (NIR) red fluorescent Ag/Au nanocluster sensor, utilizing glutathione and bovine serum albumin as dual ligands. This modification shifts the emission wavelength to the red spectrum, mitigating the interference from the plant's inherent fluorescence. The fluorescence resonance energy transfer (FRET) mechanism is exploited. When the presence of Ag + induces the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to oxTMB, which in turn quenches the fluorescence at 650 nm. GA's abundant phenolic hydroxyl group will reduce oxTMB to TMB, so the material's fluorescence will be turned on to detect GA. The established platform can quantify a wide range of GA thanks to the above reasons. In particular, this platform has a recovery of 96.09%-104.7 % for detecting GA in tomato samples with an error of no more than 3 % and a detection limit as low as 38.29 nM. On the other hand, a combination of probes and the fluorescence platform was used to visualize GA levels in tomato leaves under drought and salt stress. This can assist in elucidating the physiological adaptations of plants to environmental changes. The groundbreaking GSH/BSA-Au/Ag nanosensor shows great potential for trace detection of GA in plants and will contribute to a deeper understanding of plant physiology.
Background Nitroxyl (HNO) is an emerging signaling molecule that plays a significant regulatory role in various aspects of plant biology, including stress responses and developmental processes. However, understanding the precise actions of HNO in plants has been challenging due to the absence of highly sensitive and real-time in situ monitoring tools. Consequently, it is crucial to develop effective and accurate detection methods for HNO. Establishing such methodologies will enable researchers to elucidate the functional roles of HNO in plant physiological processes, thereby advancing our knowledge of plant resilience and adaptation under environmental stressors. Result Herein, we successfully constructed a near-infrared fluorescent probe, DCIF-HNO, based on the dicyanoisophorone platform as fluorophore and 2-(diphenylphosphino)benzoate as HNO recognition site for identifying HNO in plants. Probe DCIF-HNO exhibited rapid response, excellent selectivity, and high sensitivity to HNO in vitro spectroscopic tests, while also demonstrating low toxicity and biocompatibility. A rapid and portable smartphone sensing platform for HNO in actual samples was successfully constructed based on probe DCIF-HNO and color recognition application. Moreover, probe DCIF-HNO was successfully applied to plant cells and tissues, enabling real-time visualization and detection of HNO and revealing the complex network of HNO interactions during H 2 S/NO crosstalk in plants. Furthermore, the increase in HNO levels in plants response to high salt and Cr stress was observed using probe DCIF-HNO. Transcriptome sequencing and differential metabolites analysis were employed to gain insight into the mechanism of HNO production under Cr stress. Significance Due to the optical properties and high-resolution imaging capabilities of DCIF-HNO, this study offers a novel framework for elucidating the signaling role of HNO in plant stress responses. The precise visualization of HNO dynamics enhances our understanding of the complex molecular pathways involved in plant adaptation to abiotic stressors. This research not only advances plant physiology but also has significant implications for developing strategies to enhance agricultural resilience in challenging environmental conditions.
As a common pollutant, cadmium (Cd) poses a serious threat to the growth and development of plants. Currently, there is no effective method to elucidate the protective mechanism of Cd 2+ in plant cells. For the first time, we designed a Cd 2+ fluorescent probe to observe the adsorption and sequestration of Cd 2+ in rice cell walls and vacuoles. Specifically, Cd 2+ is blocked by the Casparian strip and electrostatically attracted to hemicellulose, which is abundantly adsorbed and fixed to the cell walls of the endodermis. For Cd 2+ that successfully entered the endodermis, one part entered the cells and was compartmentalised and fixed in the vacuoles, while the other part entered the vascular bundles and precipitated in the cell walls of the sclerenchyma through the ion exchange effect. Furthermore, with prolonged exposure to Cd 2+ , compartmentalised bodies that were strongly labelled by fluorescence gradually appeared in the vacuoles, which were assumed to be a new heavy metal protective mechanism activated by plants in response to continuous Cd 2+ exposure. In conclusion, this study provides an innovative and effective method for the detection of adsorption, transportation, and accumulation of Cd 2+ in plant tissues, which can be employed for the rapid identification of crops with low Cd accumulation.
Herbicide safeners are considered key agents for plant protection that reduce the harmful impacts of herbicides on crops and the environment in general, but traditional evaluation methods for their effectiveness are time-consuming and labor-intensive. In this study, a rapid and non-destructive method was proposed using chlorophyll fluorescence and hyperspectral imaging that combined with machine learning models. Besides, chemometric analysis was utilized to reveal the action mechanism between the wheat crop (Triticum aestivum L.) understudy and the herbicide isoproturon (ISO) and safener gibberellin acid (GA 3 ). The results showed that ISO caused oxidative stress and disrupted the photosynthesis mechanism in wheat by hindering the electron transport pathway from primary acceptor quinone to secondary acceptor. Meanwhile, GA 3 stimulated wheat to synthesize more glutathione (GSH) that accelerated the herbicide action metabolism. It's worth noting that excessive GA 3 has decreased significantly the GSH and photosynthetic pigment concentrations, while the malondialdehyde concentration was significantly (p 3 . In conclusion, the novelty of the current study came from the accurate real-time tracking method for GA 3 action mechanism and its effectiveness on ISO toxicity. Where, that model holds great value for reducing the traditional methods' limitations in safener developments.
Abstract Reactive oxygen species (ROS) are crucial in plant growth, defense, and stress responses, making them vital for improving crop resilience. Various ROS sensing methods for plants have been developed to detect ROS in vitro and in vivo . However, each method comes its own advantages and disadvantages, leading to an increasing demand for a simple and effective sensory system for ROS detection in plants. Here, we introduce novel DNA silver nanoclusters (DNA/AgNCs) sensors for visualizing ROS in plants. Two sensors, C 20 /AgNCs and FAM-C 20 /AgNCs-Cy5, detect intracellular ROS signaling in response to stimuli such as abscisic acid, salicylic acid, ethylene, and bacterial peptide elicitor flg22. Notably, FAM-C 20 /AgNCs-Cy5 exceeds the sensing capabilities of HyPer7, a widely recognized ROS sensor. Taken together, we suggest that fluorescent i-motif DNA/AgNCs system is an effective tool for visualizing ROS signals in plant cells. This advancement is important to advancing our understanding of ROS-mediated processes in plant biology.
The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. This approach was validated using a previously published dataset. A subgroup of the identified “anomalies” was clearly linked to stress-induced reductions in photosynthesis. Furthermore, the percentage of these “anomalies” showed a meaningful correlation with both the progression and severity of stress. The results highlight the still largely unexploited potential of Machine Learning in OJIP analysis.
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 · publicSupplementary Materials: The following supporting information can be downloaded at https://
www.mdpi.com/article/10.3390/stresses4040051/s1. All OJIP data used in the study can be found in
Supplementary data (OJIP data).xlsx.Open asset ↗stresses4040051pdf-page:12 lines:1-58Plant phenotyping relevance matchOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background The early and specific detection of abiotic and biotic stresses, particularly their combinations, is a major challenge for maintaining and increasing plant productivity in sustainable agriculture under changing environmental conditions. Optical imaging techniques enable cost-efficient and non-destructive quantification of plant stress states. Monomodal detection of certain stressors is usually based on non-specific/indirect features and therefore is commonly limited in their cross-specificity to other stressors. The fusion of multi-domain sensor systems can provide more potentially discriminative features for machine learning models and potentially provide synergistic information to increase cross-specificity in plant disease detection when image data are fused at the pixel level. Results In this study, we demonstrate successful multi-modal image registration of RGB, hyperspectral (HSI) and chlorophyll fluorescence (ChlF) kinetics data at the pixel level for high-throughput phenotyping ofA. thalianagrown in Multi-well plates and an assay with detached leaf discs ofRosa × hybridainoculated with the black spot disease-inducing fungusDiplocarpon rosae. Here, we showcase the effects of (i) selection of reference image selection, (ii) different registrations methods and (iii) frame selection on the performance of image registration via affine transform. In addition, we developed a combined approach for registration methods through NCC-based selection for each file, resulting in a robust and accurate approach that sacrifices computational time. Since image data encompass multiple objects, the initial coarse image registration using a global transformation matrix exhibited heterogeneity across different image regions. By employing an additional fine registration on the object-separated image data, we achieved a high overlap ratio. Specifically, for theA. thalianatest set, the overlap ratios (ORConvex) were 98.0 ± 2.3% for RGB-to-ChlF and 96.6 ± 4.2% for HSI-to-ChlF. For theRosa × hybridatest set, the values were 98.9 ± 0.5% for RGB-to-ChlF and 98.3 ± 1.3% for HSI-to-ChlF. Conclusion The presented multi-modal imaging pipeline enables high-throughput, high-dimensional phenotyping of different plant species with respect to various biotic or abiotic stressors. This paves the way for in-depth studies investigating the correlative relationships of the multi-domain data or the performance enhancement of machine learning models via multi modal image fusion.
Photoprotection in plants includes processes collectively known as nonphotochemical quenching (NPQ), which quench excess excitation-energy in photosystem II. NPQ is triggered by acidification of the thylakoid lumen, which leads to PsbS-protein protonation and violaxanthin de-epoxidase activation, resulting in zeaxanthin accumulation. Despite extensive study, questions persist about the mechanisms of NPQ. We have set up a novel analytical pipeline to disentangle NPQ induction curves measured at many light intensities into a limited number of different kinetic components. To validate the method, we applied it to Chl-fluorescence measurements, which utilised the saturating-pulse methodology, on wild-type (wt) and zeaxanthin-lacking (npq1) Arabidopsis thaliana plants. NPQ induction curves in wt and npq1 can be explained by four components ( α , β , γ and δ ). The fastest two ( β and γ ) correlate with pH difference formed across the thylakoid membrane in wt and npq1. In wt, the slower component ( α ) appears to be due to the formation of zeaxanthin-related quenching whilst for npq1, this component is 'replaced' by a slower component ( δ ), which reflects a photoinhibition-like process that appears in the absence of zeaxanthin-induced quenching. Expanding this approach will allow the effects of mutations and other abiotic-stress factors to be directly probed by changes in these underlying components.
Pescador-Dionisio S, Cendrero-Mateo MP, Moncholí-Estornell A, Robles-Fort A, Arzac MI, Renau-Morata B, Fernández-Marín B, García-Plazaola JI, Molina RV, Rausell C, Moreno J, Nebauer SG, García-Robles I, Van Wittenberghe S.
Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the paper's raw and processed phenotyping/spectroscopy measurements open access on Zenodo (doi: 10.5281/zenodo.12800064). This is a paper-specific, public, actionable dataset. However, the Zenodo URL is not among the allowed_urls, so no asset URL is providedDataset · publicData Availability Statement
Raw and processed data are available open access through the Zenodo repository (doi: 10.5281/zenodo.12800064 ).Zenodo · 10.5281/zenodo.12800064lines:539-574Plant phenotyping relevance matchOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. This significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II ( F v /F m ) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130 , have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.
China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440-530 nm and 680-780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.
Phytopathogens are a significant challenge to agriculture and food security. In this regard, methods for the early diagnosis of plant diseases, including optical methods, are being actively developed. This review focuses on one of the optical diagnostic methods, chlorophyll fluorescence (ChlF) imaging. ChlF reflects the activity of photosynthetic processes and responds subtly to environmental factors, which makes it an excellent tool for the early detection of stressors, including the detection of pathogens at a pre-symptomatic stage of disease. In this review, we analyze the peculiarities of changes in ChlF parameters depending on the type of pathogen (viral, bacterial, or fungal infection), the terms of disease progression, and its severity. The main mechanisms responsible for the changes in ChlF parameters during the interaction between pathogen and host plant are also summarized. We discuss the advantages and limitations of ChlF imaging in pathogen detection compared to other optical methods and ways to improve the sensitivity of ChlF imaging in the early detection of pathogens.
Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.
Due to their sessile nature, plants are unable to escape environmental factors that negatively impact health, resulting in losses to agricultural productivity. Rapid, non-invasive tools to detect plant stress response are essential for optimizing resource efficiency and mitigating the effects of extreme environmental pressures. However, many existing methods are either invasive, incompatible with other measurement techniques, or have not been applied to a wide range of varying environmental factors. In this study, we assess the physiological responses of four week old camelina (Camelina sativa) and sorghum (Sorghum bicolor) to chitosan, cold, drought, and both acute and chronic salt stress. Several plant characteristics were measured in parallel during stress exposure, including fluorescence and gas exchange parameters (MultispeQ and LI-6800), tissue electrical impedance with wearable biosensors (Multi-PIP), and biochemical properties via Fourier-transform infrared (FTIR) spectroscopy. We compiled unique profiles for whole plant physiological changes in response to environmental stress, demonstrating that certain aspects of plant health and makeup underwent alterations on differing temporal scales. This finding emphasizes the need for a comprehensive multi-modal approach to rapidly and accurately perform remote sensing of plant health in the field. Physiological parameters such as leaf impedance were also observed to rapidly change in response to treatment and can be leveraged to detect very early signs of plant perturbation. This research establishes the utility of a holistic phenotyping approach to inform agricultural strategies aimed at enhancing crop resilience under changing environmental conditions.
Xu Y, Li X, Du H, Mao F, Zhou G, Huang Z, Fan W, Chen Q, Ni C, Guo K.
Field / plotChlorophyll fluorescenceMultispectral / hyperspectralWhole plant / canopy / plot / fieldGrowth / time-series analysisGrowth / development / phenology
Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R² values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.
Recent research has shown that optimizing photosynthetic and stomatal traits holds promise for improved crop performance. However, standard phenotyping tools such as gas exchange systems have limited throughput. In this work, a novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes. Using the dual-imaging methods and traditional approaches, we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance, and Fq'/Fm', highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combinations of preferable traits (i.e. inherently high gsmax, low Ki, and maintained leaf evaporative cooling) are present in wheat. This work highlights the effectiveness of thermal imaging in screening dynamic gs in a panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimization within future breeding programmes.
Reproduction assets foundThe paper's Data Availability statement points to a public Dryad repository containing the raw phenotyping data (photosynthesis and stomatal kinetics measurements) for this study, matching an allowed URL. Supplementary datasets S1–S2 are calculation spreadsheets but no standalone public URL is given for them beyond theDataset · publicRaw data can be accessed from the Dryad Digital Repository ( Faralli et al. , 2024 ) ( https://doi.org/10.5061/dryad.79cnp5j4d ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.79cnp5j4dlines:117-171Plant phenotyping relevance matchEurope PMC · checked 15 Sept 2026
Salt stress is an adverse environmental condition that harms plant growth and development. The development of salt stress probes is critical for tracking the growth dynamics of plants, molecular breeding or screening of growth regulators. The sodium chloride (NaCl)-responsive fluorescent probe Aza-CyBz is designed based on the tenet that NaCl induces formation of ordered aggregates, and the sensitive fluorescence response can enable the visualization of plant salt stress in root tip tissues and live plants. Herein, we describe a detailed three-step route for synthesis of Aza-CyBz and applications to monitoring salt stress in Arabidopsis thaliana. The procedures for operating fluorescence imaging under various stresses are also listed to eliminate interference from the oxidative mechanism of salt stress. Compared with conventional invasive approaches such as inductively coupled plasma emission spectrometry and flame photometer, our protocol can real-time monitor salt stress experienced by plants, which demands simple pretreatment procedure and staining technique. Due to near infrared fluorescence, this method provides direct visual observation of salt stress at both tissue and live plant levels, which is superior to conventional noninvasive approaches. The preparation of probe Aza-CyBz takes ~2 d, and the imaging experiments for assessing salt stress experienced by plants, including the preparation of stressed plant samples takes ~9-11 d for root tip tissues and ~23 d for live plants. Notably, acquisition and analysis visual images of salt stress in plants can be completed within 2 h and they require only a basic knowledge of spectroscopy and chemistry.
The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha -1 sufficed to meet crop N requirements. Leaf characteristics such as N leaf, total pigment content (TP), and leaf dry weight (DW leaf) changed significantly with increasing N application rates, influencing rapid chlorophyll fluorescence (OJIP) dynamics. Principal component analysis (PCA) reduced ChlF from 35 to 21, and four models were developed, among which, the model using ChlF and TP was more accurate than the model using DW alone. Key ChlF parameters for PLSR model performance included ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1-δ(Ro)). Although non-destructive N leaf detection using chlorophyll fluorescence technology proved feasible, additional leaf characteristics, such as TP, are necessary to improve model accuracy. Considering local field conditions is essential for the application of this technology at a larger scale. Precise evaluation of N status using chlorophyll fluorescence is beneficial for a more efficient N management and sustainable agriculture.
Maize (Zea mays L.) performs highly efficient C4 photosynthesis by dividing photosynthetic metabolism between mesophyll and bundle sheath cells. In vivo physiological measurements are indispensable for C4 photosynthesis research as any isolated cells or sectioned leaf often show interrupted and abnormal photosynthetic activities. Yet, direct in vivo observation regarding bundle sheath cells in the delicate anatomy of the C4 leaf is still challenging. In the current work, we used two-photon fluorescence-lifetime imaging microscopy (two-photon-FLIM) to access the photosynthetic properties of bundle sheath cells on intact maize leaves. The results provide spectroscopic evidence for the diminished total PSII activity in bundle sheath cells at its physiological level and show that the single PSIIs could undergo charge separation as causal. We also report an acetic acid-induced chlorophyll fluorescence quenching on intact maize leaves, which might be a physiological state related to the nonphotochemical quenching mechanism.
The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R 2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.
Abstract To explore the potential of using high-throughput plant phenomics in rice breeding programs, one hundred elite rice varieties from southern rice-growing areas in China were subjected to high-throughput phenomic analysis. A total of 88 parameters were measured and obtained using RGB imaging, fluorescence imaging, and hyperspectral imaging at four key rice growth stages: tillering, jointing, grain filling, and 20 days after grain filling. These 88 parameters, which include RGB color and morphological features, chlorophyll fluorescence characteristics, and rice surface reflectance spectra, were analyzed to characterize high yield and high grain quality in rice using subset selection regression and deep learning neural network models. A total of 39 significant linear regression models were obtained for predicting rice yield and grain quality, with R-squared values ranging from 0.86 to 0.15, and an average R-squared of 0.41. The data from the 100 rice varieties were split into training and test sets to evaluate the prediction accuracies of the models using mean absolute error between predicted and actual values. The results indicated that the deep learning neural network model can be used to refine the linear regression model, improving the prediction accuracy. These findings suggest that high-throughput plant phenomics can be effectively utilized in rice breeding programs to select for high-yielding, high-quality rice varieties.
The chlorophyll content of wheat was assessed using multispectral fluorescence imaging (MSFI). Ultraviolet (UV) light (365 nm)-induced fluorescence images at 440, 520, 690, and 740 nm, and visible light (460, and 610 nm)-induced fluorescence images at 690 and 740 nm were acquired while leaf chlorophyll content was measured using SPAD 520. The fluorescence images were processed after segmentation and channel extraction to calculate the parameters of each leaf based on fluorescence images (Fu440, Fu520, Fu690, and Fu740) obtained by UV excitation, and fluorescence images (Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, and Fr740) obtained by three excitations of 365 nm, 460 nm, and 610 nm light. 12 fluorescence ratio parameters under UV excitation and 26 fluorescence ratio parameters under three excitations were calculated. The correlation analysis revealed that the fluorescence parameters (Fr740, Fu440, Fu520, Fu690, Fu740, Fb690, Fb740, Fu440/Fu520, Fu520/Fu690, and Fu740/Fr740) showed a strong correlation with the chlorophyll content. These parameters have the potential to measure the chlorophyll content. Subsequently, stepwise regression analysis (SRA) was employed to screen 16 fluorescence parameters under UV excitation and 33 fluorescence parameters under three excitations, with the objective of identifying and eliminating redundant variables. Finally, four variables (Fu520, Fu690, Fu740, and Fu690/Fu520) under UV excitation and five variables (Fr740, Fu520, Fb740, Fu740/Fu690, and Fb740/Fb690) under three excitations were selected. The partial least squares regression (PLSR) model, constructed using three excitations, demonstrated enhanced performance with an Rc2 of 0.901, Rv2 of 0.904, root mean square error (RMSE) of calibration of 4.398, and RMSE of validation of 4.267. Multiexcitation fluorescence based on three excitations techniques has better performance for evaluating chlorophyll content.
Accurate assessing leaf nitrogen content (LNC) is crucial for actual production and fertilizer management. In this research, a portable device was designed to rapidly and non-destructively evaluate LNC with precision. Using hydroponically grown eggplants exposed to different nitrogen content nutrient solutions as experimental samples, we conducted various measurements, including chlorophyll fluorescence (ChlF) induction curves, hyperspectral images, and LNC values. Correlations between LNC and ChlF parameters were calculated, and the parameter qN obtained the highest correlation with LNC. False color images of qN were segmented using the K-Means algorithm to obtain three regions. The spectral data and the measured LNC of the corresponding region in the leaf were matched, and a LNC prediction model was developed using the partial least square regression (PLSR) algorithm with the processed spectral data as input and the measured LNC as output. The results showed that the model using standard normal variate-iteratively retains informative variables- successive projections algorithm (SNV-IRIV-SPA-PLSR) yielded the best performance, with a correlation coefficient of prediction (R²) of 0.9332, a root mean square error (RMSE) of 2.6890 mg/g, a residual prediction deviation (RPD) of 3.97 and a ratio of performance to interquartile distance (RPIQ) of 7.28. Based on the selected wavelengths from the SNV-IRIV-SPA-PLSR-VIP model, six narrow-band light emitting diodes (LEDs) were chosen as the light source for the designed device. Inexpensive modules were employed to assemble the device, and accuracy tests were conducted. The PLSR algorithm was employed to develop the device’s LNC evaluation model with the reflectance of the leaf under 6 LEDs as input (resulting in R², RMSE, RPD, and RPIQ values of 0.8075 6.6242 mg/g, 2.30 and 4.26, respectively). The model was then embedded in the core processor. To validate the device’s performance, an independent set was used, resulting in R² of 0.7559, RMSE of 7.4771 mg/g, RPD of 2.07, and RPIQ of 3.57, respectively. The proposed device could rapidly and accurately determine LNC in plants, surpassing other devices in terms of portability and cost. This research offers a potential solution for plant fertilizer management.
Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.
The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha⁻¹ sufficed to meet crop N requirements. Leaf characteristics such as N leaf, total pigment content (TP), and leaf dry weight (DW leaf) changed significantly with increasing N application rates, influencing rapid chlorophyll fluorescence (OJIP) dynamics. Principal component analysis (PCA) reduced ChlF from 35 to 21, and four models were developed, among which, the model using ChlF and TP was more accurate than the model using DW alone. Key ChlF parameters for PLSR model performance included ABS/RC, φ(Eo), ETo/CSm, and δ(Ro)/(1–δ(Ro)). Although non-destructive N leaf detection using chlorophyll fluorescence technology proved feasible, additional leaf characteristics, such as TP, are necessary to improve model accuracy. Considering local field conditions is essential for the application of this technology at a larger scale. Precise evaluation of N status using chlorophyll fluorescence is beneficial for a more efficient N management and sustainable agriculture.
Published1 Oct 2024Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
During the storage process, seeds deteriorate with aging, and their vigor gradually decreases, affecting the field emergence rate, uniformity, and crop yield. Therefore, it is critical to accurately and quickly identify seed vigor. Spectroscopic techniques are widely used in research to evaluate seed vigor rapidly. However, current research predominantly focuses on single varieties, with limited exploration of generalized detection methodologies for multi-variety seeds. In this paper, autofluorescence and reflectance spectroscopy techniques were used to detect several varieties of wheat seeds and analyze the changing pattern of their spectra after artificially accelerated aging treatment. The analysis compares the effectiveness of feature modeling of autofluorescence and reflectance spectra in cross-variety detection. By integrating these two spectral features through ensemble learning, a seed vigor detection model capable of cross-variety identification was developed and validated with an optimal accuracy of more than 87.5 %. Furthermore, for seed vigor group detection, the detection strategy was designed to discriminate small batches of seeds, and the final detection accuracy reached 93.9 % on average. In summary, this study shows that ensemble learning can combine the advantages of autofluorescence and reflectance spectroscopy, enhance the discrimination ability of combined datasets, and provide an effective reference for cross-variety seed vigor detection.
Waterlogging is expected to become a more prominent yield restricting stress for barley as rainfall frequency is increasing in many regions due to climate change. The duration of waterlogging events in the field is highly variable throughout the season, and this variation is also observed in experimental waterlogging studies. Such variety of protocols make intricate physiological responses challenging to assess and quantify. To assess barley waterlogging tolerance in controlled conditions, we present an optimal duration and setup of simulated waterlogging stress using image-based phenotyping. Six protocols durations, 5, 10, and 14 days of stress with and without seven days of recovery, were tested. To quantify the physiological effects of waterlogging on growth and greenness, we used top down and side view RGB (Red-Green-Blue) images. These images were taken daily throughout each of the protocols using the PSI PlantScreen™ imaging platform. Two genotypes of two-row spring barley, grown in glasshouse conditions, were subjected to each of the six protocols, with stress being imposed at the three-leaf stage. Shoot biomass and root imaging data were analysed to determine the optimal stress protocol duration, as well as to quantify the growth and morphometric changes of barley in response to waterlogging stress. Our time-series results show a significant growth reduction and alteration of greenness, allowing us to determine an optimal protocol duration of 14 days of stress and seven days of recovery for controlled conditions. Moreover, to confirm the reproducibility of this protocol, we conducted the same experiment in a different facility equipped with RGB and chlorophyll fluorescence imaging sensors. Our results demonstrate that the selected protocol enables the assessment of genotypic differences, which allow us to further determine tolerance responses in a glasshouse environment. Altogether, this work presents a new and reproducible image-based protocol to assess early stage waterlogging tolerance, empowering a precise quantification of waterlogging stress relevant markers such as greenness, Fv/Fm and growth rates.
High-throughput imaging enables rapid collection of large datasets and is used widely in many systems. However, this is not often used in plant-based systems due to issues related to the need to mount tissues and autofluorescence of plant metabolites. We therefore developed methodology enabling high-throughput imaging of Arabidopsis roots. In this system, growth media supplemented with India Ink (to block autofluorescence from cotyledons) is poured directly into multi-well coverglass-bottom plates and seedlings grown such that the roots grow down with the gravity vector and along the coverglass, effectively mounting themselves for imaging. This method enables high-throughput imaging of Arabidopsis roots.
As an important signaling molecule, carbon monoxide (CO) plays an important role in plant growth and development including affecting stomatal movement, stress response and root development. Thus, it is necessary to develop fluorescent probes that can be used to detect CO in live plant tissues and further enable a deep-understanding of its biological function, mechanism and metabolism. In this paper, a novel and sensitive fluorescent probe based on Cu 2+ modulated polydihydroxyphenylalanine nanoparticles (PDOAs) has been developed for the detection of CO. The fluorescence of PDOAs can be effectively quenched by Cu 2+ through the multi-coordination interaction. In the presence of CO, Cu 2+ can be effectively reduced to Cu + , which resulted in the release of free PDOAs and the Cu 2+ -quenched bright green fluorescence was restored obviously. Through this ingenious strategy, the abiotic CO can be accurately detected and identified with high selectivity, rapid response time within 5 min and an ultralow detection limit of 72.4 nM. Due to the admirable biocompatibility, the nano-material based probe has been successfully applied for in vivo imaging CO in the root tip and leave tissues of lettuce. To the best of our knowledge, this is the first example of a fluorescent probe-based methodology for the sensitive tracking of CO in plant tissues.
Published17 Sept 2024Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for PhotobiologyCited by 4 · OpenAlex ↗
The flesh oil content (OC) is a crucial commercial indicator of avocado maturity and directly correlates with its nutritional quality. To meet export standards and optimize edible characteristics, avocados must be harvested at the appropriate stage of physiological maturity. The significant variability in OC during maturation, without any external morphological indicators, poses a longstanding challenge. Currently, harvesting maturity is optimized through time-consuming, destructive laboratory methods like freeze-drying and chemical extraction, which use representative samples to estimate the maturity of entire orchards. In this study, for the first time, we employed fluorescence imaging of avocado skin using 365-nm UV polarized light excitation to estimate the OC in the 'Bacon' avocado cultivar. We developed a surface fluorescence index that strongly correlates with OC, achieving correlation coefficients up to - 0.91. Our non-destructive and rapid approach achieved a cross-validation accuracy with an R 2 value of 0.81, enabling the classification of avocados with low and high OC. This pioneering method shows considerable potential for further improvement and refinement. This study lays the groundwork for developing a portable, cost-effective, and real-time method for non-destructive in situ monitoring of avocado OC in the field and its integration into large-scale post-harvest grading systems.
Kelps are vital for marine ecosystems, yet the genetic diversity underlying their capacity to adapt to climate change remains unknown. In this study, we focused on the kelp Macrocystis pyrifera a species critical to coastal habitats. We developed a protocol to evaluate heat stress response in 204 Macrocystis pyrifera genotypes subjected to heat stress treatments ranging from 21 °C to 27 °C. Here we show that haploid gametophytes exhibiting a heat-stress tolerant (HST) phenotype also produced greater biomass as genetically similar diploid sporophytes in a warm-water ocean farm. HST was measured as chlorophyll autofluorescence per genotype, presented here as fluorescent intensity values. This correlation suggests a predictive relationship between the growth performance of the early microscopic gametophyte stage HST and the later macroscopic sporophyte stage, indicating the potential for selecting resilient kelp strains under warmer ocean temperatures. However, HST kelps showed reduced genetic variation, underscoring the importance of integrating heat tolerance genes into a broader genetic pool to maintain the adaptability of kelp populations in the face of climate change.
Reproduction assets foundThe authors publicly deposited both the analysis scripts and the numerical source data (including raw fluorescence intensity values underlying the heat-stress phenotyping) in a Zenodo repository, with explicit availability statements and URLs in the Data availability and Code availability sections.Code · publicAll scripts used in this study are available in a Zenodo repository at https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:168-242Dataset · publicNumerical source data for the graph presented in Figs. 1 – 3 , and Fig. 5 can be found in the Zenodo repository here: https://doi.org/10.5281/zenodo.13315681 .Open asset ↗Zenodo · 10.5281/zenodo.13315681lines:148-167Plant phenotyping relevance matchbioRxiv · checked 15 Sept 2026
Aoyama, T. · Nambo, M. · Yap, J. X. · Nakagawa, A. · Hayashi, M. · Ukai, Y. · Ohtsuka, M. · Hayashi, K.-i. · Sato, Y. · Tsuchiya, Y.
ArabidopsisChlorophyll fluorescenceCell / cellular structureRootVisualization / data management
Polar transport of the phytohormone auxin plays a crucial role in plant growth and response to environmental stimuli. Small-molecule tools that visualize auxin distribution in intact plants enable us to understand how plants dynamically regulate auxin transport to modulate growth. In this study, we developed a new fluorescent auxin probe, BODIPY-IAA2, which effectively visualizes auxin distribution in various plant tissues. We designed this probe to be transported by auxin transporters while lacking the ability to elicit auxin signaling. Using BODIPY as the fluorophore provides bright and stable fluorescence signals, making it suitable for live-imaging under standard fluorescent microscopy. We tested the probe with auxin reporter lines in Arabidopsis and performed yeast two-hybrid assays. The results showed that BODIPY-IAA2 did not activate auxin signaling through the auxin receptor TIR1. However, BODIPY-IAA2 did mildly compete with both exogenous and endogenous auxins for transport, indicating that the probe is transported by auxin transporters in vivo. The probe not only enables visualization of its tissue distribution but also allows sub-cellular staining, including the endoplasmic reticulum and tip regions in elongating cells in moss. We also observed unusual staining patterns in the main root of non-model parasitic plants where genetic transformation is not feasible. Our new fluorescent auxin probe demonstrates significant potential for detailed studies on auxin transport and distribution across diverse plant species.
Muhammad Faheem Jan · Ming Li · Waqas Liaqat · Muhammad Tanveer Altaf · Changzhuang Liu · Haseeb Ahmad · Ehtisham Hassan Khan · Zain Ali · Celaleddin Barutçular · Heba I. Mohamed
Reductive and oxidative signals transmitted from the photosynthetic electron chain to target proteins through the redox signaling network are key regulators of carbon assimilation and downstream metabolism. However, despite their crucial role in activating and inhibiting photosynthetic activity, their relation to photosynthetic efficiency is hardly quantified due to the methodological gap between traditional spectroscopic approaches for investigating photosynthesis and biochemical analyses used in the redox regulation field. Here, we simultaneously quantified redox signals and photosynthetic activity by exploring time and wavelength-resolved fluorescence spectra that capture biosensor and chlorophyll fluorescence signals. Using a set of potato plants expressing genetically encoded redox biosensors, we demonstrated how reductive and oxidative signals are amplified with elevated light intensities and revealed the tight connection between electron transport rate (ETR) and the generation of peroxiredoxin-related oxidative signals. These results demonstrate how full spectrum analysis can pave the way for the integration of genetically encoded biosensors in photosynthesis research and demonstrate light-dependent activation of inhibitory oxidative signals in major crop plants.
Fungal plant diseases are a major threat to plants and vegetation worldwide. Recent technological advancements in biotechnological tools and techniques have made it possible to identify and manage fungal plant diseases at an early stage. These techniques include direct methods, such as ELISA, immunofluorescence, PCR, flow cytometry, and in-situ hybridization, as well as indirect methods, such as fluorescence imaging, hyperspectral techniques, thermography, biosensors, nanotechnology, and nano-enthused biosensors. Early detection of fungal plant diseases can help to prevent major losses to plantations. This is because early detection allows for the implementation of control measures, such as the use of fungicides or resistant varieties. Early detection can also help to minimize the spread of the disease to other plants. The techniques discussed in this review provide a valuable resource for researchers and farmers who are working to prevent and manage fungal plant diseases. These techniques can help to ensure food security and protect our valuable plant resources.
Introduction Tomatoes are sensitive to low temperatures during their growth process, and low temperatures are one of the main environmental limitations affecting plant growth and development in Northeast China. Chlorophyll fluorescence imaging technology is a powerful tool for evaluating the efficiency of plant photosynthesis, which can detect and reflect the effects that plants are subjected to during the low temperature stress stage, including early chilling injury. Methods This article primarily utilizes the chlorophyll fluorescence image set of tomato seedlings, applying the dung beetle optimization (DBO) algorithm to enhance the deep learning bidirectional long short term memory (BiLSTM) model, thereby improving the accuracy of classification prediction for chilling injury in tomatoes. Firstly, the proportion of tomato chilling injury areas in chlorophyll fluorescence images was calculated using a threshold segmentation algorithm to classify tomato cold damage into four categories. Then, the features of each type of cold damage image were filtered using SRCC to extract the data with the highest correlation with cold damage. These data served as the training and testing sample set for the BiLSTM model. Finally, DBO algorithm was applied to enhance the deep learning BiLSTM model, and the DBO-BiLSTM model was proposed to improve the prediction performance of tomato seedling category labels. Results The results showed that the DBO-BiLSTM model optimized by DBO achieved an accuracy, precision, recall, and F1 score with an average of over 95%. Discussion Compared to the original BiLSTM model, these evaluation parameters improved by 9.09%, 7.02%, 9.16%, and 8.68%, respectively. When compared to the commonly used SVM classification model, the evaluation parameters showed an increase of 6.35%, 7.33%, 6.33%, and 6.5%, respectively. This study was expected to detect early chilling injury through chlorophyll fluorescence imaging, achieve automatic classification and labeling of cold damage data, and lay a research foundation for in-depth research on the cold damage resistance of plants themselves and exploring the application of deep learning classification methods in precision agriculture.
Accurate and non-destructive monitoring of wheat nitrogen nutrition is of great significance for field fertilizer management to ensure crop yield and quality, reduce environmental pollution, and improve economic benefits. Compared with spectral vegetation indices (which are sensitive to greenness and structural parameters), or active fluorescence (which is limited to small-scale studies), solar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics. However, there has been few studies comparing agronomic parameters, photosynthetic parameters, vegetation indices and SIF as an indicator of nitrogen status. In this paper, we therefore explore these measures as tools for monitoring nitrogen nutrition. During the 2016–2017 growing season, we conducted a field experiment in Rugao, Jiangsu Province, China, using winter wheat (Triticum aestivum L.) and different nitrogen treatments. The sensitivity of SIF indices, vegetation indices, photosynthetic parameters and agronomic parameters to crop nitrogen status were compared. Our results demonstrated that, compared with vegetation indices and agronomic parameters, the ratio of SIF emission peaks (FY₆₈₇/FY₇₆₁) responded to nitrogen status most rapidly at both the leaf and canopy scales, as soon as the fourth day after treatment (DAT4). A wheat nitrogen nutrition index (NNI), based on FY₆₈₇/FY₇₆₁, was used to construct a leaf dry matter (LDM-based NNI) diagnostic model, which will be beneficial for monitoring and diagnosing the nitrogen nutrition status of wheat leaves. Our results also illuminate the physiological mechanism that enables SIF to be used as a tool to monitor nitrogen nutrient status, primarily through changes in the proportion of light energy distribution. These findings provide theoretical and technical support for monitoring and diagnosing wheat nitrogen nutrition status based on SIF technology.
Predicting saccharine and bioenergy feedstocks in sugarcane enables growers and industries to determine the precise time and location for harvesting a better-quality product in the field. On one hand, Brix, Purity, and total recoverable sugars (TRS) can provide meaningful and reliable indicators of high-quality raw materials for first-generation (1 G) bioethanol. Conversely, Cellulose, Hemicellulose, and Lignin are the primary constituents of straw, directly contributing to second-generation (2G) bioethanol. However, analyzing these materials in the laboratory is a time-consuming and non-scalable task. Therefore, we propose an approach based on a multi-sensor framework, which includes multispectral unmanned aerial vehicle (UAV) imagery, thermal, photosynthetic active radiation (PAR), and chlorophyll fluorescence (ChlF) data, along with machine learning (ML) algorithms namely random forest (RF), multiple linear regression (MLR), decision tree (DT), and support vector machine (SVM), to develop a non-invasive and predictive framework for mapping sugarcane feedstocks. We collected samples of stalks and leaves/straw during the maturity stage while simultaneously collecting remote sensing data. The ML models played a crucial role in predicting 1 G (R² = 0.88–0.93) and 2 G (R² = 0.56–0.82) feedstocks. Notably, remote sensing data could serve as important features for the models, mainly through the spectral bands (Blue, Green, and RedEdge), DTemp and ChlF. Hence, the best features can be further implemented within a framework to predict sugarcane feedstocks. Our study marks a significant advancement in the industrial-scale prediction of sugarcane feedstocks, providing stakeholders with invaluable prescriptive harvesting strategies for both primary products and by-products.
Chlorophyll fluorescence measurement is a quick and efficient tool for plant stress-level detection. The use of Pulse amplitude modulation (PAM), allows the detection of the plant stress level under field conditions. Over the years, several parameters estimating different parts of the chlorophyll and photosystem response were developed to describe the plant stress level. Despite all fluorescence parameters being based on the same measurements, their relationship remains unclear, and their response to drought stress is significantly influenced by the incoming light intensity. In this study, we use six different annual plants from different families, both C3 and C4 photosynthesis types, to describe the plant response to drought through the fluorescence parameters response (NPQ, Y(NPQ), and qN). To describe the dynamic response to drought, we employed light-response curves, adapting and fitting an equation for each curve to compare the drought response for each fluorescence parameter. The results demonstrated that the non-photochemical quenching (NPQ) and the quantum yield of non-photochemical quenching [Y(NPQ)] maximal values decrease when the PSII functionality (F v /F m ) is lower than ~0.7. The basal fluorescence level ( F 0 $$ {F}_0 $$ and F s ) $$ {F}_s\Big) $$ remained unaffected by the stress level and stayed stable across the various plants and stress levels. Our results indicate that the response of different stress parameters follows a distinct order under continuous drought. Consequently, monitoring just one parameter during long-term stress assessments may result in biased analysis outcomes. Incorporating multiple chlorophyll fluorescence parameters offers a more accurate reflection of the plant's stress level.
Kulaporn Boonyaves · Mervin Chun-Yi Ang · Minkyung Park · Jianqiao Cui · Duc Thinh Khong · Gajendra Pratap Singh · Volodymyr B. Koman · Xun Gong · Thomas K. Porter · Nam-Hai Chua · Daisuke Urano · Michael S. Strano
Gibberellins (GAs) are a class of phytohormones, important for plant growth, and very difficult to distinguish because of their similarity in chemical structures. Herein, we develop the first nanosensors for GAs by designing and engineering polymer-wrapped single-walled carbon nanotubes (SWNTs) with unique corona phases that selectively bind to bioactive GAs, GA 3 and GA 4 , triggering near-infrared (NIR) fluorescence intensity changes. Using a new coupled Raman/NIR fluorimeter that enables self-referencing of nanosensor NIR fluorescence with its Raman G-band, we demonstrated detection of cellular GA in Arabidopsis, lettuce, and basil roots. The nanosensors reported increased endogenous GA levels in transgenic Arabidopsis mutants that overexpress GA and in emerging lateral roots. Our approach allows rapid spatiotemporal detection of GA across species. The reversible sensor captured the decreasing GA levels in salt-treated lettuce roots, which correlated remarkably with fresh weight changes. This work demonstrates the potential for nanosensors to solve longstanding problems in plant biotechnology.
The slightly sweet and acidic taste offered by Pontianak Siam oranges is influenced by the total soluble solids (TSS) and acidity in the fruit, in which, measuring these attributes is commonly performed using instruments that potentially damage the fruit's structure, thus, impractical for fresh fruit products. Moreover, the process of classifying the quality of fresh oranges has been based on physical appearance, leading to subjective results. Correspondingly, the objective of the study is to develop a prediction method for the physicochemical characteristics of Pontianak Siam oranges based on VIS-NIR-Fluorescence spectroscopy and an artificial neural network (ANN) model. The method is applicable to classify oranges based on physicochemical characteristics without damaging the fruit's structure. As a result, the best model for classifying the maturity level of Pontianak Siam oranges was obtained using a dataset with all feature combined spectra, attaining a training accuracy of 0.99 and testing accuracy of 1. The best model for predicting TSS was obtained using all feature combined spectra dataset, attaining R2 training = 0.89 and R2 testing = 0.91. The best model for predicting acidity was obtained using all feature reflectance spectra datasets, attaining R2 training = 0.96 and R2 testing = 0.97. The best model for predicting fruit firmness was obtained using all feature reflectance spectra dataset, attaining R2 training = 0.97, R2 testing = 0.89. Overall, the combination of Vis-NIR reflectance and fluorescence spectroscopy have the potential to be applied for non-destructive assessment of citrus quality in terms of visual classification and maturity parameters prediction.
Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.
Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.
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 following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24154784/s1 , Figure S1: HPLC measurements from the UW-Madison dataset ( n = 26).Open asset ↗lines:262-279Plant phenotyping relevance matchbioRxiv · checked 13 Sept 2026
O_LISynthetic biology has made progress in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. C_LIO_LITraditional methods for studying microbial and algal communities, such as microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on their size, complexity, and spectral fingerprints, offering more precise and comprehensive analysis than conventional flow cytometry. C_LIO_LIThis study demonstrates the use of full-spectrum cytometry for analyzing synthetic algal-microbial communities, facilitating rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, utilizing autofluorescence to distinguish them from noise, and subsequent creation of a spectral library for further analysis. The obtained library is used then to analyze mixtures of unicellular cyanobacteria and synthetic phytoplankton communities, revealing differences in spectral signatures. The synthetic consortium experiment monitored algal growth, comparing results from different instruments and highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. This approach demonstrated higher flexibility and accuracy in analyzing multi-component algal-microbial assemblages and tracking temporal changes in community composition. C_LIO_LIBy capturing the complete emission spectrum of each cell, this method enhances the understanding of algal-microbial community dynamics and responses to environmental stressors. With development of standardized spectral libraries, our work demonstrates an improved characterization of algal communities, advancing research in synthetic biology and phytoplankton ecology. C_LI
Background: The advancements achieved in artificial intelligence (AI) technology in recent decades have not yet been equaled by agricultural phenotyping approaches that are both rapid and precise. Efficient crop phenotyping technologies are necessary to enhance crop improvement endeavors in order to fulfill the projected demand for food in future. Methods: This work demonstrates a method for non-destructive physiological state phenotyping of plants using cutting-edge image processing methods in conjunction with chlorophyll fluorescence imaging. Key fluorescence metrics, such as fv/fm and NPQ, were extracted from images taken at different phases of development via processing. In addition, this research explores the transformative role of automated image analysis in high-throughput phenotyping of legume traits. A comprehensive examination of recent studies reveals the diverse applications of machine learning and deep learning algorithms in capturing morphological traits, assessing physiological parameters, detecting stress and diseases in various legume species. The comparative analysis underscores the superiority of automated systems over traditional methods, emphasizing scalability and efficiency. Challenges, including algorithm sensitivity and environmental variability, are identified, urging further refinement. Recommendations advocate for standardized metrics, interdisciplinary collaborations and user-friendly platforms to enhance accessibility. As the field evolves, the integration of automated image analysis holds promise for revolutionizing legume phenotyping, accelerating crop improvement and contributing to global food security in sustainable agriculture. Result: The findings demonstrate that the proposed method is effective in illuminating how plants respond to their environment, hence promoting advancements in plant phenotyping and agricultural research.
In the field of photosynthesis, only a limited number of approaches of super-resolution fluorescence microscopy can be used, as the functional architecture of the thylakoid membrane in chloroplasts is probed through the natural fluorescence of chlorophyll molecules. In this work, we have used a custom-built fluorescence microscopy method called Single Pixel Reconstruction Imaging (SPiRI) that yields a 1.4 gain in lateral and axial resolution relative to confocal fluorescence microscopy, to obtain 2D images and 3D-reconstucted volumes of isolated chloroplasts, obtained from pea (Pisum sativum), spinach (Spinacia oleracea) and Arabidopsis thaliana. In agreement with previous studies, SPiRI images exhibit larger thylakoid grana diameters when extracted from plants under low-light regimes. The three-dimensional thylakoid architecture, revealing the complete network of the thylakoid membrane in intact, non-chemically-fixed chloroplasts can be visualized from the volume reconstructions obtained at high resolution. From such reconstructions, the stromal connections between each granum can be determined and the fluorescence intensity in the stromal lamellae compared to those of neighboring grana.
Dopamine, alongside norepinephrine and epinephrine, belongs to the catecholamine group, widely distributed across both plant and animal kingdoms. In mammals, these compounds serve as neurotransmitters with roles in glycogen mobilization. In plants, their synthesis is modulated in response to stress conditions aiding plant survival by emitting these chemicals, especially dopamine that relieves their resilience against stress caused by both abiotic and biotic factors. In present studies, there is a lack of robust methods to monitor the operations of dopamine under stress conditions or any adverse situations across the plant's developmental stages from cell to cell. In our study, we have introduced a groundbreaking approach to track dopamine generation and activity in various metabolic pathways by using the simple nitrogen and sulfur co-doped carbon quantum dots (N, S-CQDs). These CQDs exhibit dominant biocompatibility, negligible toxicity, and environmentally friendly characteristics using a quenching process for fluorometric dopamine detection. This innovative nanomarker can detect even small amounts of dopamine within plant cells, providing insights into plant responses to strain and anxiety. Confocal microscopy has been used to corroborate this occurrence and to provide visual proof of the process of binding dopamine with these N, S-CQDs inside the cells.
Chlorophyll (Chl) plays a crucial role in photosynthesis, functioning as a photosensitizer. As an integral component of this process, energy absorbed by this pigment is partly emitted as red fluorescence. This signal can be readily imaged by fluorescence microscopy and provides a visualization of photosynthetic activity. However, due to limited resolution, signals cannot be assigned to specific subcellular/organellar membrane structures. By correlating fluorescence micrographs with transmission electron microscopy, researchers can identify sub-cellular compartments and membranes, enabling the monitoring of Chl distribution within thylakoid membrane substructures in cyanobacteria, algae, and higher plant single cells. Here, we describe a simple and effective protocol for correlative light-electron microscopy (CLEM) based on the autofluorescence of Chl and demonstrate its application to selected photosynthetic model organisms. Our findings illustrate the potential of this technique to identify areas of high Chl concentration and photochemical activity, such as grana regions in vascular plants, by mapping stacked thylakoids.
Monitoring vegetation is essential in Earth Observation (EO) due to its link with the global carbon cycle, playing a crucial role in ecosystem management. The fluorescence of chlorophyll (ChF) is a reliable indicator of plants' photosynthetic activity and growth, especially when they are experiencing unfavourable conditions, particularly in terrestrial wetlands. These wetlands are integral components of the landscape, contributing significantly to climate mitigation, adaptation, biodiversity, and the well-being of both the environment and humanity. We conducted a research study using the XGBoost machine learning algorithm to map the chlorophyll fluorescence parameter Fv/Fm in the Biebrza River Valley, which is known for its marshes, peatlands, and diverse flora and fauna. Our study highlights the benefits of using ensemble classifiers derived from EO Sentinel-2 satellite imagery for accurately mapping Fv/Fm across terrestrial landscapes under the Ramsar Convention at Narew River Valley (Poland) and Čepkeliai Marsh (Lithuania). The XGBoost algorithm provides an accurate estimate of ChF with a robust determination coefficient of 0.747 and minimal bias at 0.013, as validated using in situ data. The precision of Fv/Fm chlorophyll fluorescence parameter estimation from remote sensing sensors depends on the growth stage, emphasizing the importance of identifying the optimal overpass time for S-2 observations. Our study found that biophysical factors, as denoted by spectral indices related to greenness and leaf pigments, were highly impactful variables among the top classifiers. However, incorporating soil, vegetation and meteorological indicators from remote sensing data could further increase the accuracy of chlorophyll fluorescence mapping.
Solar-induced chlorophyll fluorescence (SIF) contains contributions from both photosystem I (PSI) and photosystem II (PSII). In theory, SIF emitted from PSII (SIFPSII) should be extracted from at-sensor SIF to quantify photosynthetic CO₂ assimilation, as PSI fluorescence yield is nearly insensitive to changes in photochemical yield. In many SIF-related studies, the fraction of chlorophyll-absorbed energy allocated to PSII (β₂), a key factor controlling the flux of excitation energy for SIFPSII, is simply assigned a fixed value. However, β₂ is regulated in response to variations in environmental conditions to avoid an energy imbalance between PSI and PSII. By quantifying the regulating effect of the cytochrome b₆f complex (Cyt b₆f) on the electron transport from PSII to PSI, and its interaction with energy dissipation in both photosystems, we develop a framework to estimate β₂ and PSII fluorescence yield (ΦF₂), two key determinants for SIFPSII, from SIF emission, PAR, air temperature, the maximum carboxylase activity of Rubisco (Vcₘₐₓ), and the maximum activity of Cyt b₆f (JCB₆F_ₘₐₓ). The framework is equipped with a two-leaf scheme, enabling us to estimate SIFPSII for sunlit and shaded leaves from top-of-canopy SIF observations (SIFTOC). Our simulation results showed that β₂ and ΦF₂ tend to change in opposite directions with varying PAR, making the contribution of PSII to SIFTOC relatively constant. We compare gross primary productivity (GPP) mechanistically estimated from SIFPSII obtained with fixed (GPPSIF_Fᵦ) and dynamic β₂ (GPPSIF_Dᵦ) against the eddy-tower-derived GPP (GPPEC) at a winter-wheat experiment site. At a half-hourly time scale, GPPSIF_Dᵦ is better than GPPSIF_Fᵦ, showing higher correlations with GPPEC (R² = 0.74 versus R² = 0.64 and RMSE = 6.61 μmol m⁻² s⁻¹ versus RMSE = 7.67 μmol m⁻² s⁻¹). The study provides a practical way to estimate the contribution of SIFPSII to SIFTOC, giving a better theoretical basis to SIF-based GPP estimation models.
The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce ( Lactuca sativa ) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests ( R 2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.
This research aimed to develop natural plant systems to serve as biological sentinels for the detection of organophosphate pesticides in the environment. The working hypothesis was that the presence of the pesticide in the environment caused changes in the content of pigments and in the photosynthetic functioning of the plant, which could be evaluated non-destructively through the analysis of reflected light and emitted fluorescence. The objective of the research was to furnish in vivo indicators derived from spectroscopic parameters, serving as early alert signals for the presence of organophosphates in the environment. In this context, the effects of two pesticides, Chlorpyrifos and Dimethoate, on the spectroscopic properties of aquatic plants (Vallisneria nana and Spathyfillum wallisii) were studied. Chlorophyll-a variable fluorescence allowed monitoring both pesticides' presence before any damage was observed at the naked eye, with the analysis of the fast transient (OJIP curve) proving more responsive than Kautsky kinetics, steady-state fluorescence, or reflectance measurements. Pesticides produced a decrease in the maximum quantum yield of PSII photochemistry, in the proportion of PSII photochemical deexcitation relative to PSII non photochemical decay and in the probability that trapped excitons moved electrons into the photosynthetic transport chain beyond Q A - . Additionally, an increase in the proportion of absorbed energy being dissipated as heat rather than being utilized in the photosynthetic process, was notorious. The pesticides induced a higher deactivation of chlorophyll excited states by photophysical pathways (including fluorescence) with a decrease in the quantum yields of photosystem II and heat dissipation by non-photochemical quenching. The investigated aquatic plants served as sentinels for the presence of pesticides in the environment, with the alert signal starting within the first milliseconds of electronic transport in the photosynthetic chain. Organophosphates damage animals' central nervous systems similarly to certain compounds found in chemical weapons, thus raising the possibility that sentinel plants could potentially signal the presence of such weapons.
Nitroxyl (HNO), a reactive nitrogen species (RNS), is essential for plant growth. However, the action of HNO in plants has been difficult to understand due to the lack of highly sensitive and real-time in-situ monitoring tools. Herein, we presented a near-infrared fluorescent probe, DCI-HNO, based on dicyanoisophorone fluorophore, for real-time mapping HNO in plants. The introduction of a phosphine moiety as a specific HNO recognition unit can inhibit the intramolecular charge transfer (ICT) of probe DCI-HNO. However, in the presence of HNO, the ICT process occurred, leading to the emission at 665 nm. Probe DCI-HNO exhibited high sensitivity (97 nM), rapid response time (8 min), large Stokes shift (135 nm) for detection of HNO in plants. The novel developed probe has successfully imaged endogenous HNO produced during NO/H 2 S cross-talk in plant tissues. Additionally, the up-regulated in HNO levels during tobacco aging and in response to stress has been confirmed. Therefore, probe DCI-HNO has provided a reliable method for monitoring the NO/H 2 S cross-talk and revealing the role of HNO in plants.
Heavy metals, including Hg 2+ , Cr 6+ and Cd 2+ , have always been a major issue in environmental pollution, leading to abnormal changes in the levels of biologically active molecules including Cys in plants, seriously affecting all aspects of the growth and development of plants. This makes it essential to develop a simple and practical method to study the potential impact of heavy metals on plants. In this paper, our research group has developed near-infrared fluorescent probe WRM-S, which has the advantages of fast response, sensitivity to Cys, and successfully applying it to cells and zebrafish. Moreover, it combined the close relationship between heavy metal stress on plants and Cys, using Cys as the detection target, monitoring the internal environment changes of two plants under Hg 2+ , Cr 6+ , and Cd 2+ stress in the environment, and then conducting 3D imaging. The results indicated that the probe has strong penetration ability in plant tissues, and revealed abnormal changes in plant Cys levels caused by heavy metal stress-induced cellular oxidative stress or cytotoxicity. Thus, the in-situ imaging detection of this probe provides a direction for the physiological dynamics research of plant environmental stress.
Plant bioengineering is a time-consuming and labor-intensive process, with no guarantee of achieving the desired trait. Here we report a fast, automated, scalable, high-throughput pipeline for plant bioengineering (FAST-PB). FAST-PB achieves gene cloning, genome editing, and product characterization by integrating automated biofoundry engineering of callus and protoplast cells with single cell matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). We first demonstrate that FAST-PB can streamline the Golden Gate cloning process, with the capacity to construct 96 vectors in parallel. To prove the concept, using FAST-PB, we first found that PEG2050 significantly increases transfection efficiency by over 45%. To validate the pipeline, we established a reporter-gene-free method for CRISPR editing via mutation of HCF136 , affecting cellular chlorophyll fluorescence. Next, we applied this pipeline for lipid production and found that diverse lipids were significantly enhanced up to sixfold through introducing multi-gene cassettes via CRISPR activation, and regenerated plant using this platform. Lastly, we harnessed FAST-PB to achieve high-throughput single-cell lipid profiling through the integration of MALDI-MS with the biofoundry, and differentiated engineered and unengineered cells using the single-cell lipidomics. These innovations massively increase the throughput of synthetic biology, genome editing, and metabolic engineering, and change what is possibly using single-cell metabolomics in plants.
Crassulacean acid metabolism (CAM) is found in a wide variety of vascular plant species, mainly those inhabiting water-limited environments. Identifying and characterizing diverse CAM species enhances our understanding of the physiological, ecological, and evolutionary significance of CAM photosynthesis. In this study, we examined the effect of CO2 elimination on chlorophyll fluorescence-based photosynthetic parameters in two constitutive CAM Kalanchoe species and six orchids. In CAM-performing Kalanchoe species, the effective quantum yield of photosystem II showed no change in response to CO2 elimination during the daytime but decreased with CO2 elimination at dusk. We applied this method to reveal the photosynthetic mode of epiphytic orchids and found that Gastrochilus japonicus, Oberonia japonica, and Bulbophyllum inconspicuum, but not Bulbophyllum drymoglossum, are constitutive CAM. Although B. drymoglossum had relatively high malate content in leaves, they did not depend on it to perform photosynthesis even under water deficient or high light conditions. Anatomical comparisons revealed a notable difference in the leaf structure between B. drymoglossum and B. inconspicuum; B. drymoglossum leaves possess the large water storage tissue internally, unlike B. inconspicuum leaves, which develop pseudobulbs. Our data propose a novel approach to identify and characterize CAM plants without labor-intensive experimental procedures. HighlightResponses of chlorophyll fluorescence-based photosynthetic parameters to CO2 elimination differ between Crassulacean acid metabolism (CAM) and C3 metabolism, proposing a novel approach to identify and characterize CAM plants.
High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F430,690 − F430,763)/(F430,690 + F430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm2, and the 3-D fluorescence reciprocal difference index (FRI (F650,704/F650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm2). The RMSE of the 3-D fluorescence ratio index (FRI (F500,748/F500,717)) for the carotenoid inversion is 3.77 μg/cm2 RMSE. Only FRI (F370,615/F370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm2). For Ants, although there was a 3-D FSI (FRDI (1/F540,679 − 1/F540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm2), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs.
Abstract The study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of time correlated single photon counting. In this approach, sub-nanosecond laser pulses with a repetition rate of 10 MHz are applied to excite the sample, followed by the analysis of arrival times of the emitted fluorescence photons. Photon statistics are generated by iteratively fitting the sum of two exponential functions. The tool was tested on both plastid and in vivo leaf samples of Savoy cabbage ( Brassica oleracea var. sabauda) with 3–4 subsequent leaves giving a complete sample coverage starting from the outermost. The Chl-a fluorescence lifetime exhibited a gradual increase in both the isolated plastid suspensions and the in vivo leaf samples towards the innermost leaf layers explained by an increase of natural absence of light (etiolation syndrome). Furthermore, cadmium stress and iron deficiency were investigated on treated sugar beet ( Beta vulgaris ) samples in vivo using TCSPS measurements. The reduced fluorescence quenching resulted in an increased fluorescence lifetime. Finally, a long-term (10 week) testing of the setup was carried out on Chl-retaining resurrection Haberlea rhodopensis plants protecting themselves by an elevated non-photochemical quenching yielding a decrease of fluorescence lifetime during their desiccation.
Yang S, Liu W, Shentu J, Chen X, Yang Y, Wang K, Qian J, Long L.
Field / plotChlorophyll fluorescenceLeafPhysiological trait estimationStress response / tolerance
Singlet oxygen ( 1 O 2 ) plays imperative roles in a variety of biotic or abiotic stresses in crops. The change of its concentration within a crop is closely related to the crop growth and development. Accordingly, there is an urgent need to develop an efficient analytical method for on-site quantitative detection of 1 O 2 in crops. Here, we judiciously constructed a novel ratiometric fluorescent probe, SX-2 , for the detection of 1 O 2 in crops. Upon treating with 1 O 2 , probe SX-2 displayed highly selective ratiometric fluorescence response, which is favorable for the quantitative detection of 1 O 2 . Concurrently, the fluorescence solution color of probe SX-2 was varied, obviously from blue to yellow, indicating that the probe is beneficial for on-site detection by the naked eye. Sensing reaction mechanism studies showed that the 2,3-diphenyl imidazole group in SX-2 could function as a new selective recognition group for 1 O 2 . Probe SX-2 was utilized for the detection of photoirradiation-induced 1 O 2 and endogenous 1 O 2 in living cells. The changes in the 1 O 2 level in zebrafish were also tracked by fluorescence imaging. In addition, the production of 1 O 2 in crop leaves under a light source of different wavelengths was studied. The results demonstrated more 1 O 2 were produced under a light source of 365 nm. Furthermore, to achieve on-site quantitative detection, a mobile fluorescence analysis device has been made. Probe SX-2 and mobile fluorescence analysis device were capable of on-site quantitative detecting of 1 O 2 in crops. The method developed herein will be convenient for the on-site quantitative measurement of 1 O 2 in distinct crops.
Oumeng Zhang · Haowen Zhou · Brandon Y. Feng · Elin M. Larsson · Reinaldo E. Alcalde · Siyuan Yin · Catherine Deng · Changhuei Yang
Field / plotChlorophyll fluorescenceRootWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometryRoot system architecture
Single-shot volumetric fluorescence (SVF) imaging offers a significant advantage over traditional imaging methods that require scanning across multiple axial planes as it can capture biological processes with high temporal resolution. The key challenges in SVF imaging include requiring sparsity constraints, eliminating depth ambiguity in the reconstruction, and maintaining high resolution across a large field of view. In this paper, we introduce the QuadraPol point spread function (PSF) combined with neural fields, a novel approach for SVF imaging. This method utilizes a custom polarizer at the back focal plane and a polarization camera to detect fluorescence, effectively encoding the 3D scene within a compact PSF without depth ambiguity. Additionally, we propose a reconstruction algorithm based on the neural fields technique that provides improved reconstruction quality compared to classical deconvolution methods. QuadraPol PSF, combined with neural fields, significantly reduces the acquisition time of a conventional fluorescence microscope by approximately 20 times and captures a 100 mm$^3$ cubic volume in one shot. We validate the effectiveness of both our hardware and algorithm through all-in-focus imaging of bacterial colonies on sand surfaces and visualization of plant root morphology. Our approach offers a powerful tool for advancing biological research and ecological studies.
β-Galactosidase (β-gal), an enzyme related to cell wall degradation, plays an important role in regulating cell wall metabolism and reconstruction. However, activatable fluorescence probes for the detection and imaging of β-gal fluctuations in plants have been less exploited. Herein, we report an activatable fluorescent probe based on intramolecular charge transfer (ICT), benzothiazole coumarin-bearing β-galactoside (BC-βgal), to achieve distinct in situ imaging of β-gal in plant cells. It exhibits high sensitivity and selectivity to β-gal with a fast response (8 min). BC-βgal can be used to efficiently detect the alternations of intracellular β-gal levels in cabbage root cells with considerable imaging integrity and imaging contrast. Significantly, BC-βgal can assess β-gal activity in cabbage roots under heavy metal stress (Cd 2+ , Cu 2+ , and Pb 2+ ), revealing that β-gal activity is negatively correlated with the severity of heavy metal stress. Our work thus facilitates the study of β-gal biological mechanisms.
Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.
Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of qL, the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of simulated and experimental data, a comprehensive understanding of qL responses to environmental and physiological variations has yet to emerge, and as a consequence, prediction of qL across leaf and canopy scales is still in an early stage. Based on a global sensitivity analysis of a recently developed mechanical model of photosynthesis, we find that the broadband total SIF emitted from PSII (SIFTOT_FULL_PSII) and leaf temperature (TLₑₐf) are the two major predictors of qL. A leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLₑₐf over a wide range of environmental conditions. From these measurements, we show that qL can be modelled as a hyperbolic function of SIFTOT_FULL_PSII with only one temperature-related parameter m which increases with temperature, but decreases rapidly as temperatures exceed the optimum temperature. It is suggested that m can be mathematically modelled by a peaked function. The results of the leaf-level experiments on winter wheat demonstrate that the proposed model predicts qL with high accuracy (R² ≥ 0.91, rRMSE ≤ 8.46%) under diverse light and temperature conditions. The essential steps necessary to apply it at canopy scale, including estimating the escape fraction, removing fluorescence emitted from Photosystem I, and reconstructing SIFTOT_FULL_PSII from top-of-canopy (TOC) narrowband SIF, are also presented. Our results confirm that estimated GPP using SIF-informed qL agrees well with measured GPP at a winter wheat site (R² = 0.81, rRMSE = 12.03%). The key benefit of SIF-informed approach is that SIFTOT_FULL_PSII provides critical information on the collective influence of the sub-canopy light environment on qL, avoiding the requirement to explicitly estimate qL at different canopy depths, potentially promoting the ability of SIF to mechanistically quantify photosynthetic CO₂ assimilation at large scales.
Monitoring changes in chlorophyll a (ChlFa) fluorescence during dehydration can provide insights into plant photosynthetic responses to climate change challenges, which are predicted to increase drought frequency. However, the limited knowledge of how ChlFa parameters respond to water deficit hinders the exploration of the photochemical mechanism of the photosynthetic process and the simulation of photosynthetic fluorescence models. Furthermore, how to track such responses of ChlFa parameters, especially at large scales, remains a challenge. In this study, we attempted to use spectral information reflected from leaves to follow the dynamic response patterns of ChlFa parameters of seven species under prolonged dehydration. The results showed that the investigated ChlFa parameters exhibited significant changes as dehydration progressed, with considerable variability among the different species as well as under different water conditions. This study also demonstrated that the integration of both spectral and water content information can provide an effective method for tracking ChlFa parameters during dehydration, explaining over 90% of the total variance in the measured ChlFa parameters. Collectively, these results should serve as a valuable reference for predicting the response of ChlFa parameters to dehydration and offer a potential method for estimating ChlFa parameters under drought conditions.
Monitoring plants' responses to water deficit using remote sensing still faces large uncertainty, mostly due to the inaccurate characterization of plants' physiological responses. Solar induced chlorophyll fluorescence (SIF) contains information on plants' physiological processes which regulates the energy partitioning after solar radiation is absorbed by chlorophyll, providing new opportunities to monitor plant response to drought stress. However, the drought-induced physiological, biochemical, and structural changes are strongly coupled, hindering the mechanistic understanding of drought impacts on plants. Here, using tower-based observations of SIF together with high spectral resolution reflectance measurements, we derived the time series of the fraction of absorbed photosynthetically active radiation by canopy, chlorophyll content, and fluorescence efficiency using two radiative transfer model-based decomposition methods, and evaluated their responses to two consecutive dry spells at a tall-grass prairie site in the USA (34°59′05.0″ N, 97°31′20.6″ W). We observed a robust signal of afternoon depression based on the fluorescence efficiency estimates during the second dry spell, which had much lower soil moisture than the first one. The strong decline in fluorescence efficiency in the afternoon was likely caused by the high temperature and atmospheric dryness when the soil was dry. Such a direct physiological response contributed to 14.4% to 36.0% of seasonal variation of afternoon SIF, depending on the decomposition method used. Sustained water stress also caused lagged responses. Despite the subsequent rainfall after the dry spell, we observed a continued decline of SIF due to the lagged decline of chlorophyll content and green canopy coverage. Our study demonstrates the use of continuous SIF measurements to understand the development of drought effects on plants, and highlights the importance of afternoon SIF measurements for physiological stress detection.