Abstract Purpose Agrivoltaic vineyards show strong spatio-temporal variability in canopy shading, but field methods to quantify panel-induced shading at canopy scale remain limited. Shading is a key factor because it affects plant physiological and morphological traits, with potential consequences for yield and production quality. This study developed a near-surface time-lapse RGB imaging approach to derive temporally explicit shading metrics in an agrivoltaic vineyard of Vitis vinifera cv. Falanghina in Southern Italy. Methods Two representative vine positions beneath the photovoltaic structure were monitored: Agrivoltaic Shade (AVS), with greater exposure to panel-induced shading, and Agrivoltaic Light (AVL), with lower exposure. Image-based canopy shading percentage was calculated through a dedicated processing workflow and integrated with radiometric and physiological measurements, including continuous photosynthetically active radiation (PAR), canopy-level spectral photon flux measurements, photosynthetic photon flux density (PPFD), band-specific photon flux densities, red:far-red ratio (R:FR), stomatal conductance (gₛ), and leaf temperature. PAR measurements beneath the panels were compared with a full-sun control area. Results AVS showed significantly higher shading than AVL (76.14% vs 39.45%, p Conclusion The proposed workflow offers a low-cost, non-destructive tool to quantify shading dynamics and support site-specific assessment of crop microenvironments in agrivoltaic systems. The approach provides crop-relevant information for precision monitoring and management of spatially heterogeneous light conditions across different crop species. Impact The data provided in this manuscript enable the quantification of in-season photovoltaic-induced canopy shading dynamics in an agrivoltaic vineyard using proximal RGB time-lapse imaging and crop-level radiometric measurements. These metrics reflect the spatial and temporal variability of light availability within the vineyard and support site-specific assessment of crop microenvironments and precision management of agrivoltaic systems.
Why it matches plant phenotyping methodsブドウ樹冠の遮光状態をRGBタイムラプス画像から定量化する手法を開発し、専用処理ワークフローと実測値で評価しており、植物フェノタイピング手法が中心である。
abstractThis study developed a near-surface time-lapse RGB imaging approach to derive temporally explicit shading metrics in an agrivoltaic vineyard of Vitis vinifera cv. Falanghina in Southern Italy.
Leaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax), but models trained in one species or measurement context often lose accuracy in another. This transfer problem limits the use of spectral approaches in multi-species crop phenotyping and carbon-cycle applications. Here, we tested physiology-informed inputs for leaf-level Vcmax25 retrieval using paired gas-exchange and reflectance data from wheat (C₃; n = 198) and maize (C₄; n = 81) grown under contrasting nitrogen supply. The four input configurations were raw spectra (Mod1), spectra scaled by a PPFD–absorptance proxy (Mod2), scaled spectra augmented with radiative-transfer-derived traits (Mod3), and scaled spectra augmented with a spectral coordination proxy (Mod4). Within datasets, the best models reached R² = 0.82 in wheat, 0.41 in maize, and 0.76 in the combined dataset. In a matched comparison with a common random-forest learner, the spectral coordination proxy Mod4 improved accuracy only slightly over Mod2 in wheat (RMSE −0.51%; p = 0.0058) and maize (RMSE −1.26%; p = 0.0011) but not in the combined dataset (RMSE −0.15%; p = 0.074), and the trait-based Mod3 showed no consistent benefit. When wheat models were tested on measurement dates not used in training, accuracy remained moderate (R² = 0.563; RMSE = 15.07 µmol m⁻² s⁻¹). Despite this within-dataset performance, models applied to the other species without calibration failed in both directions (negative R²), and adding source-species data did not improve prediction even when a few samples of the new species were used for calibration. These results show that physiology-informed input design provides at most small within-dataset gains, and that reliable prediction across C₃ and C₄ crops requires calibration data from the target crop.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から光合成能力Vcmaxを推定する手法を開発・比較・検証しており、植物形質取得が研究の中心です。
abstractLeaf hyperspectral reflectance can provide a scalable way to estimate photosynthetic capacity (Vcmax)
Urban air pollution may alter plant metabolism long before visible damage becomes apparent. Raman spectroscopy was evaluated as a rapid, non-destructive approach to resolve these biochemical adjustments. Mature Quercus ilex L. trees were sampled along a well-defined pollution gradient in Tuscany (Italy), spanning high, intermediate, and low levels of NO₂ and PM₁₀. Leaf Raman spectra revealed coordinated modulation of primary and secondary metabolism. Pigment-related bands (chlorophylls and carotenoids) increased toward the most polluted site, while inducible flavonoid signals showed site-dependent variation consistent with oxidative pressure in superficial tissues. These patterns were consistent with destructive biochemical analyses and chlorophyll fluorescence measurements, which indicated acclimation rather than photoinhibition damage. A composite Raman index showed a close site-level association with NO₂ exposure, suggesting that nitrogen-related urban pollution was the main exposure component linked to the observed metabolic response. Overall, Raman spectroscopy captures the chronic metabolic imprint of urban air pollution in Q. ilex, resolving coordinated pigment reinforcement and defensive activation without sample destruction. This approach provides a rapid and scalable framework for linking atmospheric chemistry to plant functional status in biomonitoring applications.
Why it matches plant phenotyping methods植物の代謝・機能状態を非破壊的に推定するRaman分光法を評価し、スペクトル指標と生化学・蛍光測定の整合性を検証しているため、手法が中心的です。
abstractRaman spectroscopy was evaluated as a rapid, non-destructive approach to resolve these biochemical adjustments.
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.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
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 match · UnverifiedCrossref · 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.
Why it matches plant phenotyping methodsRGB画像から蛍光由来の植物光化学状態を推定する手法を構築し、種間交差検証で性能評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying.
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.
Why it matches plant phenotyping methods植物の光合成状態を示すSIFを取得するタワー型分光観測システムとDOAS補正アルゴリズムを開発し、シミュレーションおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractWe present a DOAS-based SIF retrieval algorithm
Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.
Why it matches plant phenotyping methods3D LiDARによる植物構造計測と生理計測を統合したデータセットで、モデルの較正・ベンチマーク・評価を主目的としており、植物フェノタイピング手法と再利用可能なワークフローが中心である。
abstractthree-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds
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.
Why it matches plant phenotyping methods圃場光合成データから時間分解特徴量や遺伝的シグナルを抽出する計算手法を中心に扱っており、植物生理形質の実質的なフェノタイピング手法応用に該当する。
abstractmechanism-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.
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.
Why it matches plant phenotyping methods植物の光合成形質を対象に、HTP技術やセンサー手法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding
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.
Why it matches plant phenotyping methods植物の光合成状態（NPQ）を測定するための蛍光寿命・蛍光収率に基づく2つの方法を開発し、比較検証しているため、方法開発が中心である。
abstractHere 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.
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 match · UnverifiedEurope PMC · checked 14 Sept 2026
Accurate estimation of the photorespiratory CO 2 compensation point (Γ*) is essential for describing the balance between Rubisco carboxylation and oxygenation and for parameterising biochemical models of photosynthesis. Γ* and the rate of CO 2 release in the light (D L ) are commonly estimated using the Laisk method, based on measurements of net CO 2 assimilation rate (A net ) at low chloroplastic CO 2 concentrations (c c ), under several sub-saturating irradiance levels. However, many widely used temperature dependence relationships for Γ* (Γ*(T)) were derived using conventional linear implementations of the Laisk method, despite the intrinsically nonlinear behaviour of the A net -c c response predicted by the photosynthetic theory. Here, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels. Gas exchange of sunflower leaves was measured across a wide temperature range from 3.9°C to 42.0°C. The conventional linear implementation generated highly dispersed pairwise intersections and unstable estimates of both Γ* and D L , including some physiologically unrealistic negative D L values at low temperatures. In contrast, the mechanistically constrained Laisk-FvCB framework produced physiologically meaningful temperature responses and substantially reduced methodological artefacts associated with linear extrapolation. Using this framework, we derived a revised in vivo Γ*(T) relationship described by an Arrhenius-type function with Γ*(25) = 43.4 μmol mol -1 and an apparent activation energy of 27.7 kJ mol -1 , such that Γ*(T) = 43.4 exp[11.176 ((T - 25)/(T + 273.15))], where T is leaf temperature in °C. Comparison with other widely used Γ*(T) formulations showed substantial divergence at temperature extremes, often exceeding the variability expected from realistic interspecific differences in Rubisco specificity among C 3 species.
Why it matches plant phenotyping methods植物葉のガス交換からΓ*と光呼吸CO₂放出速度を推定する改良Laisk-FvCB手法を提示し、従来法との比較で推定の安定性と方法論的アーティファクトを検証しているため、植物表現型測定法が中心である。
abstractHere, we revisited the temperature dependence of Γ* and D L using the improved Laisk-FvCB framework that simultaneously constrains the nonlinear A net -c c response across multiple irradiance levels.
Phosphorus (P) deficiency severely limits soybean ( Glycine max L.) productivity. This study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes. In Experiment I, percent tolerance to phosphorus deficiency (PTPD) was calculated for ten growth parameters across 98 genotypes under P-deficient and control conditions. Principal component analysis and comprehensive evaluation identified six key indicators in Experiment I, which were subsequently refined to five indicators through further analysis: SPAD at V3 and R1, photosynthetic rate at R1, shoot dry weight at R8, and seed number per plant at R8. Experiment II re-evaluated these traits using 12 contrasting genotypes under three P levels, identifying CN 15 as the most P-efficient and SN 22 as the most P-inefficient. Experiment III further revealed that CN 15 maintained superior PSII performance and exhibited a 26.2% increase in grain P-utilization efficiency under 0 µM KH 2 PO 4 treatment. This integrated framework offers a preliminary reference for screening P-efficient soybean genotypes under controlled conditions, pending field evaluation.
Why it matches plant phenotyping methodsリン欠乏耐性を評価するPTPDと三段階の形質選抜フレームワーク自体を提案・検証しており、単なる生物学的処理試験ではなく、植物形質に基づく遺伝子型スクリーニング手法が中心である。
abstractThis study proposed a three-stage screening framework to identify reliable traits and P-efficient genotypes.
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.
Why it matches plant phenotyping methods衛星リモートセンシングによるSIF等の植物生理指標を用いてイネのオゾンストレスを地域スケールで推定する評価フレームワークが研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractThis 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
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
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.
Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。
abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.
Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。
abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis
Optimizing environmental inputs for indoor crop production by conducting a traditional endpoint growth analysis requires significant time and resources. The most common scientific approach to assessing crop response involves the accumulation of dry mass at the end of a cropping cycle. A growth dynamics analysis also results in the accurate estimation of the crop response to the growth environment through periodic destructive sampling. Measuring crop gas exchange in the same environment in which it is grown offers a powerful alternative to accelerating the environmental optimization process, especially for vegetative crops. This work introduces Minitron III, a third-generation technology advancement capable of continuous gas-exchange monitoring from seed to harvest for small specialty crop stands. For proof of concept, 24 ‘Rouxai’ red oakleaf lettuce plants were grown from seed to harvest over a 25-day cropping cycle. Instantaneous differences in the carbon dioxide (CO 2 ) and water vapor (H 2 O V ) mole fraction between sample/reference lines flowing through/around cuvette/growth space were measured using a differential infrared gas analyzer, allowing determination of net photosynthesis based on a 0.41-m 2 cropping area. Crop stand net photosynthesis was detectable 7 days after sowing seeds, increasing gradually from 0.13 to 0.60 µmol·m −2 ·s −1 over the following week. The crop net photosynthesis rate increased robustly on a daily basis from 15 days after sowing seeds. While the net photosynthesis rate at the beginning of the photoperiod was 0.68 µmol·m −2 ·s −1 on day 15, it increased to 7.7 µmol·m −2 ·s −1 by day 25 after sowing seeds. Crop dark respiration was detectable from 17 days after sowing seeds and ranged from −0.3 to −0.9 µmol·m −2 ·s −1 . Minitron III has potential for rapid optimization of multiple environmental inputs for indoor production of specialty crops based on the near-real-time crop response to environmental inputs.
Why it matches plant phenotyping methods作物のガス交換を連続測定して光合成・暗呼吸を推定するシステム自体の開発と概念実証が中心であり、植物生理状態のフェノタイピング手法に該当する。
titleDevelopment and Validation of Minitron III: A System for Continuous Monitoring of Crop Gas Exchange in Controlled Environments
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
【Objective】Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress. Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management. This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.【Method】The experiment was conducted in pots and the measurements were taken during the booting stage of rice. Three water treatments were set, including continuous flooding irrigation (CK), mild drought (MS) and severe drought (HS). Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform, from which we analysed the responses of chlorophyll fluorescence traits to soil water change. Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression (PLSR) and backpropagation neural network (BPNN), based on characteristic spectral bands.【Result】①The chlorophyll fluorescence traits Fv/Fm, Y(II), qL and Y(NPQ) varied with water stress, with significant changes observed 3-4 days after cessation of irrigation, and detectable variation identified up to day 6 after terminating irrigation. On day 6 after irrigation cessation, the HS treatment reduced Fv/Fm, Y(II) and qL by 41.3%, 46.9% and 53.1%, respectively, whereas increased Y(NPQ) by 117.5% compared with CK. ②Savitzky-Golay smoothing and multiplicative scatter correction (MSC) preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation. The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue (400-500 nm), red and near-infrared regions. ③Compared with PLSR, the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits. The BPNN was most accurate for estimating Y(NPQ) and qL, with the associated R2 values being 0.867 and 0.845, respectively, and less accurate for estimating Fv/Fm.【Conclusion】Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits. This approach provides a rapid, cost-effective, and non-destructive method for monitoring crop physiological responses to water stress.
Why it matches plant phenotyping methodsイネのクロロフィル蛍光という生理形質を、キャノピー分光反射から推定するセンサー計測・予測モデルを開発し、精度評価しており、フェノタイピング手法が中心である。
abstractCanopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform
Quantifying the canopy growth dynamics and light interception capacity under different management practices laid the physiological foundation for potato yield formation. However, the traditional manual measurement methods are labour-intensive, time-consuming, and incapable of capturing time-series dynamics. To address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model. Furthermore, how Nitrogen(N)-Potassium(K) interaction affects the temporal canopy growth dynamics, light interception, and tuber yield was determined. The results indicated that: (1) Among the 11 secondary indices extracted from the canopy growth dynamic curves, the interaction of N and K had the greatest effect on the maximum canopy duration and the total canopy growth curve integral. The direct path coefficients of N and K inputs on these two parameters were 0.847 and 0.805, and 0.234 and 0.148, respectively. (2) There was a strong linear relationship between the integral area under the curve (S∫) and the total plant dry weight, with R² at 0.90 in 2023-2024. A simplified net photosynthetically active radiation utilisation assessment framework that achieved high accuracy with minimal parameter was built. (3) Prolonging the maximum canopy continuous coverage time is the main way to improve potato yield. The overall effect of N input on yield was significantly higher than that of K fertiliser, with a total effect value of 1.428. Optimising the N-K interaction improves nutrient precision and light interception. The integration of UAV remote sensing and the crop physiological-ecological model enables the tracking of potato canopy dynamics, which is helpful for optimising management practices to improve potato yield.
Why it matches plant phenotyping methodsUAV RGB画像と生理モデルを統合した高スループット手法を開発し、ジャガイモのキャノピー成長動態と光 interception を時系列で推定することが中心である。
abstractTo address this, we proposed a novel high-throughput strategy that integrates UAV-based RGB imaging with a piecewise physiological model.
Enhancing photosynthesis is an important approach to improve crop yields. Photosynthesis, as a key factor determining crop yield, is an important approach to increasing crop production and addressing global food security issues. Improving its efficiency is crucial in this regard. However, traditional photosynthetic phenotyping has long been a bottleneck in crop breeding due to time-consuming data collection. In this study, we simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data. By applying this model, we evaluated Pn in 219 soybean materials. A multi-environment genome-wide association study (GWAS) based on multi-environmental prediction Pn was carried out using the 3VmrMLM method, and 24 significant quantitative trait loci (QTLs) and four suggestive QTLs were identified. Among them, 24 QTLs overlapped with multiple previously reported QTL related to photosynthesis, chlorophyll content, quality, etc., or with genes related to key agronomic traits such as yield. Additionally, four new QTLs were discovered, and four candidate genes potentially associated with Pn were identified. Further, haplotype analysis identified their optimal haplotypes. This study presents a robust and nondestructive hyperspectral model for estimating the photosynthetic rate in soybeans, which is successfully applied to genetic analysis, yielding stable and biologically meaningful results. The approach offers an effective means to explore the genetic basis of photosynthesis and provides a solid theoretical foundation for large-scale, monitoring of soybean photosynthetic physiology.
Why it matches plant phenotyping methods大豆葉のハイパースペクトルデータから光合成速度を推定するモデルを開発し、検証・大規模適用しており、植物フェノタイピング手法が研究の中心である。
abstractwe simultaneously measured the spectral reflectance and the net photosynthetic rate (Pn) of soybean leaves to develop a high-precision model for estimating Pn based on hyperspectral data.
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.
Why it matches plant phenotyping methods植物フェノタイピングの低コストセンサー、IoT、AI解析、校正・標準化などを中心に扱うレビューであり、単なる農業応用ではなく手法・プラットフォームの評価が主題である。
abstractThis 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.
Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas-exchange data. Despite methodological advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation performance for the considered models. We showed that while key parameters of C3 photosynthesis, such as maximum rate of RuBP-saturated carboxylation and of electron transport through photosystem II, can be reliably estimated from a single A-Ci curve, other parameters, such as leaf mitochondrial respiration and CO2 compensation point, require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Usage of barley gas-exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies and choosing parameterization approaches for steady-state C3 photosynthesis.
Why it matches plant phenotyping methodsガス交換応答曲線から光合成パラメータを推定するモデルとベイズ推論を体系的に比較・評価し、フェノタイピング戦略の設計を扱うため、植物表現型取得・推定法が中心である。
abstractHere, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements.
Early detection of heavy metal stress in plants is essential for effective environmental monitoring, particularly in contaminated urban areas. This study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species. Five species were selected: Trifolium pratense, Rumex acetosa, Alcea rosea, Amaranthus retroflexus, and Plantago lanceolata. Visible plant injuries were assessed using Evans Blue staining and image-based anatomical analysis, which enabled distinguishing between living, partially damaged, and dead cells. Multispectral observations using a MicaSense RedEdge-M camera allowed calculation of the Normalized Difference Vegetation Index (NDVI) to detect stress-related changes in photosynthetic apparatus. The studied species differed in their capacity to accumulate and translocate Cr and Ni. Metal bioaccumulation was low in all species (bioconcentration factor < 1), with the highest Ni accumulation observed in Plantago lanceolata. Translocation of both metals was the greatest in Trifolium pratense and Amaranthus retroflexus. Hydrogen peroxide levels increased in roots and leaves of all species, particularly in Alcea rosea. Despite the absence of visible injuries, microscopic anatomical changes were detected in T. pratense and R. acetosa, while NDVI values differed between sites. In summary, this study indicates that no simple relationship was found between physiological stress parameter values and NDVI. It is important to emphasize the need for continued research under controlled conditions with specific doses of PTEs salts. This should clearly demonstrate the relationship between plant physiological responses to stress and the results of multispectral observations.
Why it matches plant phenotyping methodsリモートセンシング、画像ベースの解剖診断、NDVIを用いた植物ストレス検出法の評価が研究目的として明示されており、植物状態の取得・推定が中心的です。
abstractThis study evaluated whether remote sensing combined with simplified anatomical diagnostics can provide a rapid and reliable method for detecting chromium (Cr) and nickel (Ni) stress in common urban weed species.
Fusarium head blight (FHB) is a major mycotoxigenic disease of wheat, causing yield and quality losses and deoxynivalenol contamination. Rapid, non-destructive tools are needed to detect FHB, monitor wheat physiological responses, and evaluate sustainable management strategies, including biological control agents. Although vegetation spectroscopy is widely used for high-throughput phenotyping, most spectral studies focus on binary disease detection, while the capacity of hyperspectral data to capture concurrent host–pathogen–biocontrol responses across leaf and canopy scales remains underexplored. Here, we tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085. Two cultivars, Bingo and Rebelde, with higher and lower FHB susceptibility, respectively, were treated with a chemical fungicide (Chem) or T. gamsii T6085 (Bioc) under FHB pressure. Leaf- and canopy-level spectra were acquired at 2, 5, and 14 days post-inoculation, alongside gas exchange, water status, and chlorophyll measurements. Permutational multivariate analysis of variance (PERMANOVA) tested whole-spectrum effects, partial least squares discriminant analysis (PLS-DA) explored class separability, and partial least squares regression (PLSR) estimated physiological traits. PERMANOVA detected genotype × inoculation × treatment interactions from 5 days post-inoculation at leaf and canopy levels. PLS-DA revealed treatment- and cultivar-dependent spectral fingerprints, but overall low-to-fair validation performance indicates that these class-specific patterns should be interpreted as exploratory and not as evidence of operational treatment discrimination. PLSR provided high accuracy for chlorophyll content and osmotic potential, moderate accuracy for CO 2 -assimilation traits, and poor accuracy for transpiration and leaf water potential. While the workflow is scalable as an experimental and analytical framework, its operational deployment will require broader validation across sites, seasons, cultivars, disease-pressure conditions, and sensing platforms.
Why it matches plant phenotyping methods小麦のFHB・生物防除応答を対象に、葉・群落ハイパースペクトル取得、分類、検証、形質推定を統合したフェノタイピング枠組みが中心である。
abstractwe tested a full-range (400–2400 nm) hyperspectral phenotyping framework to track early interactions among winter wheat, FHB, and Trichoderma gamsii T6085.
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.
Why it matches plant phenotyping methods植物の光合成生理状態（ΦPSII）を予測・計測するモデルを開発し、蛍光センサーによるフィードバック照明制御へ統合して検証しており、植物フェノタイピング手法が中心です。
abstractThis 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.
Field / plotLeafWhole plant / canopy / plot / fieldPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
• Rapid methods show reduced robustness in hot summer Mediterranean field conditions. • Method‑dependent differences in Vc max estimates strongly affect A - g s model outputs. • Steady‑state A / C i provides the most accurate gs simulations. • RACiR provides Vc max estimates closest to A / C i and represents a suitable option for high‑throughput phenotyping. The maximum rate of carboxylation of ribulose-1,5-bisphosphate ( Vc max ) represents a key biochemical trait and a fundamental parameter in C3 models of photosynthesis, as it enables an accurate representation of leaf carbon assimilation and gas exchange. Accurate estimation of this parameter is essential for process‑based modelling across scales, as uncertainties in Vc max may influence model behaviour when scaled from leaves to larger spatial domains. Traditionally, Vc max is derived from the response of photosynthesis ( A ) to intercellular CO 2 concentration ( C i ), known as the A / C i curve, a reliable but time-consuming and labour-intensive procedure that limits its application in high-throughput phenotyping. To address this limitation, rapid approaches such as the Rapid A/Ci Response (RACiR) and the one-point (OP) methods have been developed. However, their accuracy, reliability, and reproducibility must be carefully validated, as discrepancies arising from the use of heterogeneous data sources for model parameterization may introduce significant uncertainty. In this study, the RACiR and the OP methods were evaluated against the conventional A / C i curve in a two-year field experiment on four Cannabis sativa varieties grown under different irrigation regimes. Photosynthetic traits derived from each method were compared and integrated into a coupled A -stomatal conductance ( g s ) model to assess how method-driven differences affect model outputs. Overall, photosynthetic traits estimated from A / C i curves provided the most accurate simulations of g s , with R 2 values ranging from 0.55 to 0.84 and the lowest RMSE. In contrast, traits derived from RACiR and OP methods resulted in g s overestimations of 26.7% and 50.7%, respectively. Field application of RACiR was hindered by high failure rates under high summer temperatures, while OP estimates showed substantial variability. These results indicate that, despite the appeal of faster alternatives, the A / C i curve remains the most reliable method for estimating Vc max under Mediterranean field conditions, particularly when high accuracy is required for model-based applications.
Why it matches plant phenotyping methods植物の光合成形質（Vcmax等）を高スループットに測定する手法を比較・検証し、精度、再現性、失敗率、モデル性能を評価しており、フェノタイピング手法が中心です。
titleQuantification of leaf photosynthetic traits in field conditions: Towards an efficient and reliable method for plant phenotyping and modelling ecophysiological processes
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.
Why it matches plant phenotyping methods植冠クロロフィル蛍光の脱出率を推定する物理モデルとアルゴリズムを開発し、実測放射輝度および合成データで頑健性を評価しており、植物生理状態の計測手法が中心である。
abstractwe 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.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsコムギうどんこ病の早期検出に向け、クロロフィル蛍光画像法を圃場で検証し、QYmaxという植物生理形質を指標化する研究であり、フェノタイピング手法が中心です。
titleField Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator
This study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization to improve tomato production while reducing the carbon impact of supplemental LED lighting. The CNN-ELM model was trained using key environmental variables, including photosynthetic photon flux density (PPFD), red-to-blue light ratio, canopy temperature, CO 2 concentration, and relative humidity. Within the experimental conditions, the model achieved high predictive accuracy, with an R² of 0.976 and an RMSE of 0.712 µmol m -2 s -1 . Using these predictions, the MOEA/D algorithm generated Pareto-optimal lighting strategies, which were ranked through entropy-weighted TOPSIS and implemented via cloud-based control connected to a LoRa wireless sensor network and pulse-width-modulated LED drivers. The system was evaluated during a 110-day tomato cultivation trial and compared with single-parameter control and ambient-condition treatments. Results showed a 38.4% reduction in LED-related carbon emissions, a 22.6% increase in net photosynthetic rate, and a 31.7% improvement in harvestable yield relative to ambient conditions. Physiological analyses further indicated enhanced photosynthetic performance, radiation-use efficiency, and light utilization. Overall, the findings demonstrate that data-driven, closed-loop lighting management can simultaneously enhance productivity and reduce greenhouse gas emissions in controlled-environment agriculture when applied within the validated operational domain.
Why it matches plant phenotyping methods光合成という植物生理形質を予測するCNN-ELMモデルを中核に、センサーネットワークと閉ループ制御を統合・評価しており、単なる栽培試験ではなく形質推定手法の応用が主要内容である。
abstractThis study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization
Background Seed maturation is a critical developmental phase during which seeds acquire traits essential for nutritional value, desiccation tolerance, and long-term survival. Abscisic acid (ABA) signalling is a key regulator of this process, coordinating gene expression programs underlying the acquisition of seed quality traits. However, the molecular regulation of many of these traits remains poorly understood. To address this, we performed a comprehensive analysis of seed maturation in Arabidopsis thaliana, combining physiological and transcriptomic approaches across wild-type plants and mutants affected in ABA biosynthesis, signalling, and catabolism. Results We generated a high-resolution transcriptome dataset covering seed development from 12 days after pollination to the dry seed stage in wild-type and ten mutant lines. In parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance. Integration of these datasets using weighted gene co-expression network analysis (WGCNA) identified gene modules associated with specific trait acquisition patterns. This approach enabled the identification of coordinated transcriptional programs linked to distinct seed quality traits, extending beyond individual gene-level analyses. Notably, modules associated with desiccation tolerance and longevity were enriched for genes involved in stress responses and ABA-regulated pathways, highlighting the complex and multifactorial regulation of these traits. Conclusions This study provides a comprehensive physiological and transcriptomic framework for understanding seed maturation and the acquisition of key seed quality traits in Arabidopsis thaliana. By linking gene expression dynamics to trait development, our work offers new insights into the regulatory networks underlying seed resilience and storage capacity. The dataset is made accessible through SeedMatExplorer (https://www.bioinformatics.nl/SeedMatExplorer), an open-access web platform that enables interactive exploration and supports hypothesis generation. Together, this resource represents a valuable tool for advancing research on seed biology and improving seed performance in agricultural contexts.
Why it matches plant phenotyping methods種子成熟に伴う複数の植物形質を体系的に取得し、トランスクリプトームと統合した再利用可能なデータセットおよび探索プラットフォームを提供しており、単なる生物学的実験の routine 測定を超える。
abstractIn parallel, we characterized the temporal acquisition of multiple seed traits, including germination capacity, dormancy, chlorophyll fluorescence, longevity and desiccation tolerance.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
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.
Why it matches plant phenotyping methods小麦の窒素状態を非破壊・時系列に測定するリモートセンシングと高スループット表現型解析を、技術的課題や統合手法とともにレビューしており、表現型取得法が実質的に扱われている。
abstractWe then explore transformative advances in remote sensing technologies, highlighting the paradigm shift from traditional vegetation indices to physiological sensing approaches.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO2 assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research.
Why it matches plant phenotyping methods植物葉の光合成CO2同化速度を測定する携帯型センサーを開発し、市販ガス分析計との比較検証まで行っており、植物表現型取得法が研究の中心である。
abstractHere, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors
Predicting crop photosynthetic traits from UAV imagery requires frameworks that connect canopy-level spectral observations to leaf-level physiological processes. Existing approaches rely on empirical vegetation indices (VIs) and standard machine learning models, lacking physical interpretability and appropriate deep learning architectures for image data. We developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings. The framework was evaluated for predicting CO 2 assimilation rate ( A ), stomatal conductance ( g sw), Photosystem II efficiency ( F v’/ F m’), aboveground biomass (AGB), and grain yield in a subset of seven European winter wheat varieties selected from a larger 18-variety field experiment across two growing seasons (2022–2024). Model performance was evaluated using random hold-out tests and leave-one-variety-out (LOVO) validation with bootstrap confidence intervals. For grain yield, the best tabular models achieved R 2 = 0.92–0.96, and the ViT on image patches achieved a competitive R 2 = 0.92. ViT delivered the best performance in predicting g sw. BorutaSHAP selected PROSAIL-derived features alongside empirical VIs, confirming that physics-informed features provide complementary information. The hybrid ViT+PROSAIL model matched or outperformed ViT-only for most traits under LOVO validation, with the clearest gain observed for grain yield, indicating that physics-based features can help regularize spatial representations for improved cultivar-level transferability. This study demonstrates that integrating radiative transfer model physics with spatial deep learning advances UAV-based high-throughput phenotyping of photosynthetic traits in breeding programs.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から光合成形質、バイオマス、収量を推定する物理情報機械学習・画像解析フレームワークを開発し、複数の検証法で性能評価しており、植物表現型取得・推定法が中心である。
abstractWe developed a physics-informed multi-output machine learning framework that combines PROSAIL radiative transfer model inversion-derived biophysical parameters with spectral VIs and texture features (TFs), applies two spatial deep learning architectures, a Vision Transformer (ViT) and a 2D convolutional neural network (CNN), to multispectral image patches, and introduces a hybrid architecture that fuses PROSAIL-derived features with ViT spatial embeddings.
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.
Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性用データを提供しており、フェノタイピング手法が中心である。
abstractFirst stable release of the RGB and NPQ pixel-wise phenotyping pipeline
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.
Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性資料が中心であり、植物フェノタイピング手法に該当する。
abstractPython-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data.
Abstract Early detection of soil-borne fungal diseases is essential for sustaining chickpea ( Cicer arietinum L.) productivity. This study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes. Spectral data were collected at regular intervals from 1 to 76 days after sowing (DAS) and used to derive vegetation indices including NDVI, NDWI, PRI, and DSWI. Visual symptoms appeared at 46 DAS (DRR), 42 DAS (wilt), and 43 DAS (combined stress), whereas spectral indices indicated stress-related changes earlier, typically between 36 and 40 DAS. NDVI reflected early reductions in canopy vigor, PRI captured changes in photosynthetic activity, and NDWI and DSWI indicated alterations in plant water status, with DSWI showing comparatively consistent early sensitivity. Resistant genotypes maintained relatively stable NIR reflectance and water-sensitive spectral responses, while susceptible genotypes exhibited reduced NIR reflectance and increased SWIR absorption. Significant differences (p
Why it matches plant phenotyping methodsハイパースペクトル反射測定とスペクトル指標を用いて、植物体の病害ストレスを症状発現前に推定する方法を評価しており、表現型取得・抽出が研究の中心である。
abstractThis study evaluated hyperspectral canopy reflectance (350–2500 nm) for early detection of dry root rot (DRR; Macrophomina phaseolina ), Fusarium wilt ( Fusarium oxysporum f. sp. ciceri ), and their combined stress under controlled conditions using resistant and susceptible genotypes.
The spatial organization of essential, nonessential, and toxic metal(loid) elements (MEs) within plant cells underpins physiological function. Yet, comprehensive subcellular imaging of the full ME spectrum remains challenging due to trade-offs among spatial resolution, elemental coverage, and structural correlation. Here, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution. Applying this high-fidelity workflow to Arabidopsis , soybean, and wheat, we constructed single-cell metallome maps revealing an evolutionarily conserved subcellular architecture: chloroplasts enrich essential MEs (e.g., magnesium, iron, copper), whereas vacuoles compartmentalize nonessential [e.g., lanthanum (La)] and toxic MEs [e.g., cadmium (Cd), lead, arsenic]. We demonstrate that while this architecture remains stable under homeostasis, it undergoes dynamic, stimulus-specific, and dose-dependent remodeling under stress. Low-dose La(III) enhances pairwise and higher-order colocalizations of essential MEs within chloroplasts, correlating with improved photosynthetic efficiency and growth. High-dose La(III) induces nonphysiological La-ME associations and, critically, drives aberrant Cd(II) accumulation in chloroplasts-revealing a cross-toxicity mechanism wherein La(III) disrupts native sequestration barriers. In contrast, although high-dose Cd(II) is largely excluded from chloroplasts, it triggers a widespread redistribution of essential MEs, progressively eroding spatial organization. Thus, while both ions inhibit growth, they perturb metallomic networks via distinct mechanisms: La(III)-mediated disruption of sequestration vs. Cd(II)-induced systemic compartmental collapse. Our findings establish that subcellular ME networks are dynamically regulated and orchestrate physiological outcomes.
Why it matches plant phenotyping methods植物細胞内の金属元素分布と超微細構造を取得する統合イメージング基盤とワークフローの開発が中心であり、植物の生理状態・ストレス応答に結び付けて実証している。
abstractHere, we present an integrated scanning electron microscopy-focused ion beam-time-of-flight-secondary ion mass spectrometry platform that overcomes these limitations by achieving nanoscale coregistration of ultrastructure with ME distribution.
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.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
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:216-267Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published5 Jun 2026Proceedings of the National Academy of Sciences of the United States of AmericaCited by 0 · OpenAlex ↗
To improve crop yield and resilience, it is essential to identify the steps limiting [Formula: see text] assimilation rate in plant leaves. The combined effect of multiple traits can be resolved by mechanistic models of the underlying diffusion, biochemistry, and geometry. Yet the widely used simple serial resistance models overlook tissue geometry, and detailed anatomical models are computationally heavy and rely on parameters that are difficult to measure. Here, we develop a framework for systematic species and model comparison, and find that the necessary level of model resolution is species-specific. We apply a minimal reaction-diffusion model and reduce [Formula: see text] fixation in leaves to two key parameters. These parameters comprise a compact phase space in which three rate-limiting regimes emerge naturally: stomatal uptake, intercellular diffusion, and intracellular processes. Mapping diverse plant species into this phase space reveals: 1) dominant colimitations by stomatal and intracellular processes, 2) an equal partition between species that require spatially resolved leaf-scale models and species where intracellular models suffice. Taken together, we present a scalable path for interpreting complex trait data and bridging between models.
Why it matches plant phenotyping methods葉のCO2固定を機構モデルで2パラメータに縮約し、複数種の生理的制限状態と複雑な形質データを解釈・比較する計算フレームワークが中心であるため、植物生理形質の推定・解析手法として含める。
abstractHere, we develop a framework for systematic species and model comparison
Reproduction assets foundThe paper deposits its analysis code/scripts publicly on Zenodo (DOI 10.5281/zenodo.19087524) and GitHub (andreas-stillits/CarbonFixationModel), and uses the publicly deposited Knauer et al. leaf-trait/mesophyll-conductance dataset on Figshare (10.6084/m9.figshare.19681410) to map species into (τ, γ) space. All three, Code · publicCode and Scripts. All code is readily available at our github and at a public
repository (DOI: 10.5281/zenodo.19087524).Open asset ↗Zenodo · 10.5281/zenodo.19087524pdf-raw-page:8 lines:1-60Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
The fraction of absorbed photosynthetically active radiation (FAPAR) is critical for characterizing crop photosynthetic capacity and growth status. Remote sensing technology based on unmanned aerial vehicles (UAVs) enables efficient estimation of FAPAR, but multiple scattering and transmission in the complex and dynamically changing crop canopy and background limit the accuracy of vegetation index (VI)-based methods. This study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios, comprising three modules: (1) Variable Endmember Extraction , building a spectral library of foreground (crop) and background endmembers, by extracting pure pixels on the R-NIR feature space and reducing redundancy using k-means and iterative endmember selection algorithm; (2) Iterative Unmixing , iterating over foreground-background endmember combinations as input of the multilinear mixing model (MLM) pixel by pixel; (3) Optimal Selection , selecting the optimal combination according to RMSE and outputting corresponding canopy abundance A f . Taking sorghum and rice as study objects, this study collected UAV multispectral images and field-measured FAPAR at multiple periods to validate the advantages of VE-MLM. The results demonstrated that compared to fixed-endmembers and linear/bilinear mixing models, VE-MLM always achieved excellent unmixing performance, effectively quantifying canopy contributions. The derived A f mitigated the saturation and background interference that commonly existed in VI-based regression models and exhibited a higher correlation with FAPAR (sorghum: R 2 = 0.900, rRMSE = 7.753%; rice: R 2 = 0.807, rRMSE = 2.200%). In conclusion, VE-MLM has a great potential to address spectral variability, dynamic changes, and scene complexity in crop growth scenarios, providing a more accurate and generalizable approach for sorghum and rice FAPAR estimation in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物キャノピーのFAPARを推定するスペクトルアンミキシング手法を開発し、ソルガムとイネで実測値により検証しており、植物表現型取得が中心である。
abstractThis study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios
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.
Why it matches plant phenotyping methods小麦赤さび病の無症状期を対象に、ハイパースペクトル計測と2D-COS・機械学習による植物病害状態の抽出手法を開発・評価しており、表現型取得が中心である。
abstractIn this study, the reliability of hyperspectral data obtained from early asymptomatic leaves was first validated using quantitative real-time polymerase chain reaction (qPCR).
ABSTRACT Most land plants photosynthesize using the C 3 pathway, in which ribulose bisphosphate carboxylase/oxygenase (Rubisco) fixes CO 2 into 3-carbon acids. The C 4 pathway, a biochemical CO 2 -concentrating mechanism that operates in the context of specialized leaf anatomy to concentrate CO 2 around Rubisco, is more efficient. Introduction of the C 4 pathway into the C 3 crop rice could increase yield by 50%. Expression of five C 4 enzymes in transgenic rice previously led to flux through the first step. However, there was no evidence for flux later in the cycle. Here we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity: CO 2 fixation into C 4 acids by carboxylation of a C 3 compound, decarboxylation, refixation of CO 2 by Rubisco, and regeneration of the C 3 donor. We demonstrate that these four core C 4 reactions are operating in rice, establishing the in vivo flux framework needed to progress towards a functional carbon-concentrating mechanism.
Why it matches plant phenotyping methodsトランスジェニックイネのC4光合成フラックスを検出する新規プロトコルを開発し、植物内での生理状態を測定・実証しており、フェノタイピング手法が研究の中心である。
abstractHere we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity
Understanding how crop trait variability shapes genotype × environment × management (G × E × M) interactions remains a key uncertainty in predicting agricultural performance under a changing climate. Continental-scale crop models commonly rely on spatially uniform parameters, limiting their ability to represent adaptive variation in phenology, allocation, and yield formation. Here we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations. By constraining simulations with satellite-derived photosynthesis and county-level yield records from 2008 to 2022 across ~1000 winter wheat-producing counties, the inversion recovers coherent patterns of maturity group, reproductive capacity, harvest index, and root-shoot allocation. The optimized simulations reproduce observed carbon uptake and yield variability (gross primary productivity r = 0.76-0.88; phenology bias 90% of yields within ±20% of reports) and reveal distinct physiological profiles that align with the geographic distributions of major winter wheat market classes. The inferred controls explain class- and region-specific climate sensitivities: warmer winters reduce vernalization success in late-maturing cultivars, while elevated vapor pressure deficit causes strong yield losses in rainfed Hard Red Winter wheat. The results demonstrate that observation-constrained trait inversion within model-data fusion framework reveals biologically meaningful crop-class variation, thereby providing a scalable, physiologically grounded framework for diagnosing adaptive diversity and climate vulnerability across agroecosystems.
Why it matches plant phenotyping methods衛星由来の光合成観測と収量記録を用い、深層学習によるモデル・データ融合で冬コムギの生理・形態形質を空間的に推定する手法が研究の中心であるため。
abstractHere we integrate the mechanistic agroecosystem model Ecosys with a deep learning-enabled model-data fusion inversion to infer spatially explicit physiological controls (trait proxies) of U.S. winter wheat directly from observations.
Kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process and provide the means to identify factors limiting photosynthesis. However, the use of large-scale models is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型特異的な光合成動態パラメータを推定するニューラルネットワークであり、植物の生理形質の取得・推定手法の開発と検証が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper's Data Availability Statement provides a public GitHub repository with the authors' custom code for artificial dataset generation, neural network definition/training, and predicted maize genotype parameters. Zenodo datasets (gas exchange measurements and synthetic training data) are mentioned via DOIs but no Code · publicCustom code for the generation of the artificial dataset as well as code for neural model definition and training is available at https://github.com/pwendering/C4TUNE . This repository also contains the predicted parameters for the maize genotypes.Open asset ↗pwendering/C4TUNElines:223-270Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
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.
Why it matches plant phenotyping methods植物の光合成・機能を推定するリモートセンシング手法（SIF）を中心に、その機構とスケール間利用をレビューしており、植物フェノタイピング手法のレビューに該当する。
abstractThe emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Leaf hyperspectral reflectance (HSR) data have gained increasing attention due to their usage in predicting a range of leaf physiological, biochemical, structural, and photosynthetic traits using machine learning (ML) models. The PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion. However, a comprehensive evaluation of the accuracy and transferability of the PROSPECT model across a large set of species is hindered by the limited availability of ground truth data sets. Here, we employed a combination of inversion and forward simulation of the PROSPECT-D model across a broad range of species and identified four narrow wavebands linked to environmental effects. We also introduced a novel framework using partial least squares regression to enable the analysis of the transferability of the machine learning models trained base on the PROSPECT-D across species. This analysis revealed trait-specific patterns of transferability for the machine learning surrogate based on the PROSPECT-D forward model. We then extended this analysis to PROSPECT-D inversion using neural networks and developed a fast, accurate deep-learning-based surrogate inversion approach to estimate leaf traits from measured HSR data. Our data-driven framework paves the way for improving the accuracy of PROSPECT and similar mechanistic models.
Why it matches plant phenotyping methodsHSRから葉の生理・生化学・構造・光合成形質を推定する機械学習代理モデルとPROSPECT-D逆解析手法を開発・評価しており、植物形質取得法が研究の中心です。
abstractThe PROSPECT family of models offers a complementary, mechanistic means to estimate leaf traits from HSR data using model inversion.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
GrapevineRaman / spectroscopyLeafPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
Estimating crop trait data is critical for predicting crop responses to environmental change, enabling more informed diagnoses of crop performance and the development of on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping-specifically reflectance spectroscopy-has emerged as a key element of plant trait research, enabling rapid estimation of plant traits. However, little is known about whether reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits, especially variation that exists among different cultivars or genotypes of the same crop. Using wine grapes (V. vinifera subsp. vinifera) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy to quantify intraspecific variation in 12 leaf traits across 12 different cultivars from seven different varieties. We find significant variability in traits across and within cultivars, especially in gas-exchange and hydraulic traits, with cultivars varying along a resource-conservative-to-resource-acquisitive trait axis. Models based on spectral reflectance data were able to differentiate and predict this fine-scale trait variation among cultivars for seven plant traits, with a predictive power range of R2 = 0.12-0.57. Models predicting leaf chemical (i.e., carbon and nitrogen concentrations), physiological (i.e., maximum rate of light-saturated photosynthesis), and morphological traits (i.e., leaf dry matter content) were more accurate in their predictions, while models predicting leaf water status were less accurate. Our results indicate that reflectance spectroscopy can capture certain dimensions of the fine-scale trait variation that exists within genetically diverse agroecosystems, though spectroscopic estimates of intraspecific variation in leaf water status are less accurate.
Why it matches plant phenotyping methods反射分光法を用いてブドウ葉の複数形質を推定し、品種内変異に対する予測性能を評価しており、植物表現型取得・推定手法が研究の中心である。
abstracthigh-throughput phenotyping-specifically reflectance spectroscopy-has emerged as a key element of plant trait research, enabling rapid estimation of plant traits.
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.
Why it matches plant phenotyping methods非定常クロロフィル蛍光と葉温を用いて光合成・気孔コンダクタンス動態を推定する高速フェノタイピング手法を評価しており、相関検証が研究の中心である。
abstractfaster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale.
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 match · UnverifiedEurope 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.
Why it matches plant phenotyping methods低コストRGB撮像とU-Netによる自動セグメンテーションを中核とする、ジャガイモ表現型取得プラットフォームの開発・検証であり、形態・色・植生指数形質を自動抽出している。
abstractWe 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.
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.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
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 match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
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.
Why it matches plant phenotyping methods植物の光合成動態を定量化する周波数領域解析を中心的に提案し、蛍光・酸素発生・NPQなどの生理状態を抽出する方法論研究であるため。
abstractTogether, these results establish frequency-domain analysis as a general framework for probing, identifying, and testing the dynamic regulation of photosynthesis under fluctuating light.
Manual phenotyping of photosynthesis-related traits in rice is labor-intensive and limits the scale and temporal resolution of genetic analysis under field conditions. Here, we integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS) to dissect the diversity and genetic architecture of photosynthesis-related traits in a large indica rice diversity panel (>300 accessions) evaluated across three dry seasons. A total of 45 traits, including UAV-derived NDVI, canopy height, and canopy temperature, together with leaf gas-exchange, stomatal, anatomical, and agronomic traits, were quantified. UAV-derived traits captured temporal growth and senescence dynamics and showed strong and consistent correlations with leaf photosynthetic rate, stomatal conductance, flowering time, biomass, and grain yield. GWAS identified multiple QTLs for photosynthetic and HTP traits, including a cross-year stable transpiration-rate QTL (qTRMMOL-2-2) and a photosynthetic-rate QTL (qPHOTO-1-2). Haplotype analyses revealed that the wall-associated receptor-like kinase gene OsWAK6 and the potassium transporter gene OsHAK1 were strongly associated with variation in photosynthetic rate and transpiration, respectively. Several elite accessions with consistently high photosynthetic performance carried superior haplotypes at multiple qPHOTO loci, suggesting their potential value for breeding. Together, our results demonstrate that UAV-based HTP provides reliable field-scale proxies for physiological performance, and that integrating HTP with GWAS can enable the identification of genetic targets for improving photosynthesis, water use, and yield potential in rice. • Forty-five traits, including HTP, photosynthesis, and leaf morphology, were measured across three dry seasons in diverse Indica rice. • GWAS identified genes linked to photosynthesis and stomatal density, aiding in breeding resilient, high-yield rice. • UAV-based HTP data effectively tracked plant growth and senescence, correlating with photosynthetic rate. • GWAS co-localization revealed shared QTLs, suggesting multi-trait regulation by common genes.
Why it matches plant phenotyping methodsUAVベースのHTPによる植物形質取得と生理性能の推定が研究の中心であり、45形質を大規模・反復的に測定し、信頼性や他の生理形質との相関も評価している。
abstractwe integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS)
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
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.
Why it matches plant phenotyping methodsクロロフィル蛍光イメージングを用いた高スループット表現型解析と乾燥耐性スクリーニングが研究の中心であり、PSII効率などの植物生理形質を定量化している。
titleChlorophyll Fluorescence-Based High-Throughput Phenotyping Reveals Mechanisms and Enables Rapid Screening of Desiccation-Tolerant Wild Tomato Species.
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.
Why it matches plant phenotyping methods植物のPSII熱耐性をクロロフィル蛍光で定量する方法を開発・検証し、実装上の条件を評価した研究であり、方法が中心的です。
abstractThrough 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.
Waterlogging is an increasingly important constraint in orchard systems under climate extremes. Understanding cultivar-specific physiological responses and identifying reliable, non-invasive indicators of plant water status are essential for improving orchard management under excess soil moisture. In this study, we evaluated the physiological, hydraulic, and canopy thermal responses of two commercially important apple cultivars, ‘Fuji’ and ‘Gamhong' grafted onto M.9, under controlled waterlogging and recovery conditions. We quantified photosynthetic traits and plant hydraulic parameters—including sap flow (SF), leaf water potential ( Ψ Leaf ), and whole-plant hydraulic conductivity ( K s )—together with canopy thermal indicators, canopy temperature ( T c ), and a modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references. Waterlogging significantly reduced photosynthetic performance and hydraulic function in both cultivars, but responses differed in magnitude and recovery dynamics. ‘Fuji’ exhibited greater resilience, with smaller declines and faster recovery of gas exchange and water-relation traits, whereas ‘Gamhong’ showed earlier photosynthetic limitation and delayed recovery, indicating lower tolerance to saturated soil conditions. Leaf mass per area (LMA) increased under waterlogging, reflecting constraints on leaf expansion rather than enhanced photosynthetic activity. Among the thermal indicators, mCWSI showed the strongest correlations with Ψ Leaf , stomatal conductance ( g s ), and net photosynthetic rate ( P n ), outperforming T c as an indicator of plant water status. These findings demonstrate that canopy-based thermal metrics, particularly mCWSI when interpreted alongside physiological traits, provide a robust tool for detecting cultivar-specific responses to waterlogging stress. This multi-trait framework supports cultivar selection and precision water management in orchard systems exposed to episodic flooding.
Why it matches plant phenotyping methodsキャノピー熱画像から算出したmCWSIを生理・水分状態の指標として検証し、従来のキャノピー温度と比較しているため、表現型取得・評価法が研究の中心的要素である。
abstracta modified crop water stress index ( mCWSI ) calculated using empirically derived, day-specific canopy temperature references
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
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.
Why it matches plant phenotyping methods植物ストレス状態を遠隔測定するクロロフィル蛍光LiDARセンサーを開発し、測定性能とストレス検出能力を実証しており、植物フェノタイピング手法が中心である。
abstractwe 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.
Summary statement Cotton ( Gossypium hirsutum ) drought sensitivity depends strongly on flowering stage, but stage‐resolved, non‐destructive detection remains limited. Using controlled short‐term droughts imposed at early, mid, or late flowering, we integrated multispectral and hyperspectral canopy phenotyping with physiology and explainable machine learning to identify spectral predictors of metabolic status and recovery. Early and mid‐flowering drought responses were largely recoverable, whereas late‐flowering drought caused the most potent and least reversible losses in photosynthesis, canopy structure, and fiber quality. These results highlight late flowering as a critical vulnerability window and provide a mechanistically grounded framework for rapid phenotyping of stage‐specific drought resilience.
Why it matches plant phenotyping methodsマルチスペクトル・ハイパースペクトルによる非破壊キャノピー表現型計測と説明可能な機械学習を中核に、乾燥耐性を迅速推定する方法・枠組みを提示している。
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.
Why it matches plant phenotyping methods植物ストレス状態を測定する新規ChlF LiDARセンサーの設計・性能評価が研究の中心であり、植物での検証も行っているため。
abstractwe 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.
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.
Why it matches plant phenotyping methods植物ストレス状態を測定する遠隔クロロフィル蛍光センサーを設計・実証しており、表現型取得手法が研究の中心である。
abstractwe 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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
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
Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。
titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development
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.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-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.
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 match · UnverifiedEurope PMC · checked 15 Sept 2026
Advanced methods are necessary to improve the detection of flavescence dorée (FD), one of the most relevant grapevine ( Vitis vinifera ) diseases in Europe, caused by flavescence dorée phytoplasma (FDp). Detection is commonly carried out visually by agronomists/winegrowers, and is time-consuming and error-prone. The present study demonstrated that full-range hyperspectral data (i.e., 400 to 2,400 nm) collected at leaf level can be used as a tool to rapidly and nondestructively detect FD infection directly in the field. Focusing on a Sangiovese (red grape) vineyard of Tuscany (Central Italy), heavily affected by FD (incidence higher than 75%), we showed that the proposed hyperspectral approach is capable of (i) detecting FDp infection, even before the occurrence of leaf symptoms (accuracy >70%); (ii) discriminating FD-induced leaf symptoms, even between asymptomatic and lightly symptomatic leaves (accuracy 80%); and (iii) elucidating the complex physiological responses of grapevines to FDp infection, with changes in leaf parameters estimated from spectra suggesting that the disease not only impaired early-season photosynthetic efficiency but also accelerated leaf senescence, potentially impacting grapevine productivity and grape quality. Although the hyperspectral approach proposed here is not intended to replace traditional diagnostic methods (molecular analyses), it could serve as a valuable tool to support the monitoring of plants affected by FD and may represent a crucial advancement in FD management. Further and broader studies including vineyards less challenged by FD and with other grape varieties (e.g., white ones, showing leaf yellowing instead of reddening) are encouraged.
Why it matches plant phenotyping methods葉のハイパースペクトル計測により、ブドウの病徴・感染状態・生理状態を推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractfull-range hyperspectral data (i.e., 400 to 2,400 nm) collected at leaf level can be used as a tool to rapidly and nondestructively detect FD infection directly in the field.
Understanding CO2 plant exchange is essential for quantifying its role in the global carbon cycle, predicting ecosystem responses to environmental change, and evaluating long-term growth under varying environmental conditions across several types of photosynthesis[1,2]. Plants exchange carbon dioxide with the atmosphere through three primary physiological processes: photosynthesis, which assimilates CO₂ during daylight to produce glucose and release O2 as a byproduct; photorespiration, a light-dependent process that recycles harmful byproducts of photosynthesis while releasing excess energy and CO2; mitochondrial respiration, which releases CO₂ and consume O2 to produce energy, occurring both day and night. These CO₂ fluxes are coupled with transpiration that facilitates the loss of water vapor from leaves through stomata[1]. These processes can be accurately quantified using gas-exchange techniques, in which a gas analyzer measures the exchange of CO₂ and H₂O between leaves and the atmosphere. In this study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time. The sensor consists of a quantum cascade laser emitting at 4.234 μm as the light source and a photodetector to measure CO2 absorption along an open optical path of 10 cm. Measurements were performed using an amplitude modulation approach with first-harmonic detection at 10 kHz, employing a phase-sensitive lock-in amplifier. The optical sensor was placed inside a transparent plexiglass enclosure (525x375x300 mm3) containing a plant to monitor CO₂ exchange with the surrounding environment. A temperature and humidity sensor was also installed inside the enclosure, while a non-dispersive infrared CO₂ sensor (SEFRAM 9825) outside the enclosure was used to track ambient CO₂, temperature, and humidity. Continuous measurements were performed over approximately 20 days, covering both daytime and nighttime periods outside the laboratory, in a dedicated open area to minimize disturbances from nearby activity. Measured CO₂ concentrations inside the enclosure reflected both plant exchange and diffusive transport driven by the concentration gradient with the external environment. A differential equation model accounting for these processes was developed and applied to the experimental data to quantitatively determine the plant’s net CO₂ exchange rate.ReferencesNiu, Z., Ye, Z. W. Y., Huang, Q., Peng, C. & Kang, H. Accuracy of photorespiration and mitochondrial respiration in the light fitted by CO2 response model for photosynthesis. Front. Plant Sci. 16, 1455533 (2025).Busch, F. A., Ainsworth, E. A., Amtmann, A., Cavanagh, A. P., Driever, S. M., et al. A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls. Plant Cell Environ. 47, 3344–3364 (2024).
Why it matches plant phenotyping methods植物のCO₂交換率をリアルタイムに定量する光学センサーと解析モデルが研究の中心であり、植物の生理状態を測定するフェノタイピング手法に該当する。
abstractIn this study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time.
Introduction The optimal stomatal regulation theory provides an eco-evolutionary framework for interpreting the trade-off between CO 2 uptake and water loss. This theory postulates that the marginal water cost of carbon gain ( λ=∂E/∂A ) remains approximately constant over short timescales, thereby offering a mechanistic basis for predicting stomatal behavior and gas exchange. Methods In this study, leaf-level meteorological variables and gas exchange parameters of orchard citrus were measured throughout the entire phenological period during 2021-2022. We developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms: Rubisco-limited forms (OSCvc and OSCvcd), RuBP-regeneration-limited forms (OSCvj and OSCvjd), and combined forms that dynamically select the prevailing biochemical limitation (OSC and OSCd). Results The key parameter λ was estimated daily and averaged over the entire phenological period. Using daily λ inputs, the three models produced stomatal conductance ( g s ) with accuracies ranked as OSCvjd (R 2 = 0.73) > OSCd (0.63) > OSCvcd (0.40). When a long-term constant λ was applied, model performance declined with accuracies ranked as OSCvj (0.66) > OSC (0.52) > OSCvc (0.38). Discussion The OSC model also produced intercellular CO 2 concentration ( c i ) and photosynthesis ( A ) reasonably well (R 2 = 0.78 and 0.48, respectively). Under moderate meteorological conditions (air temperature 30-40 °C and vapor pressure deficit 1-2 kPa), the OSC model showed its best performance with a mean absolute relative error of 35.2% for g s estimation. Overall, the OSCMs provided a mechanistic approach to simulate citrus leaf gas exchange requiring minimal species-specific traits and routine meteorological inputs. This modeling strategy supports rapid assessment of plant physiological status and estimation of foliar carbon-water fluxes in orchard management under subtropical climates.
Why it matches plant phenotyping methods柑橘葉のガス交換・気孔コンダクタンスを推定するモデル群を開発し、実測値との精度比較で検証しており、植物生理形質の取得・推定法が研究の中心である。
abstractWe developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms
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.
Why it matches plant phenotyping methods葉の蛍光寿命測定を基盤に、NPQ成分を分離・定量するモデルを構築しており、植物の光防護状態を取得・抽出する方法が研究の中心です。
abstractBased 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.
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 match · UnverifiedEurope 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.
Why it matches plant phenotyping methods時間・スペクトル分解蛍光による光合成状態・光防御の評価法を開発し、新規パラメータと解析パイプラインを提示しているため、植物フェノタイピング手法が中心である。
abstractWe 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.
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
MaizeRiceSoybeanField / plotMultimodalLiDAR / point cloudRGB / grayscaleMultispectral / hyperspectralThermalWhole plant / canopy / plot / field
Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.
Why it matches plant phenotyping methodsマルチモーダルセンシング、画像解析、同期機構、形質抽出アルゴリズムを統合した植物フェノタイピング基盤の開発と検証が中心であり、ストレス検出や形質定量も実証している。
abstractTraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform.
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
Why it matches plant phenotyping methods植物機能形質のリモートセンシング推定を対象に、SCOPEモデルに基づく反演フレームワークと複数の反演戦略を開発・比較し、ノイズへの頑健性と精度を検証している。形質取得・推定手法が研究の中心である。
abstractwe 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.
Citrus anthracnose is a destructive fungal disease caused by Colletotrichum gloeosporioides, which causes leaf damage, fruit rot, and yield loss in citrus production. This study proposes an early detection method for citrus leaf anthracnose that integrates spectral and physiological data. Artificial inoculation experiments showed that the infected leaves exhibited yellowish-brown lesions, and the reflectance derived from visible-near-infrared (VNIR) spectroscopy and Fourier transform near-infrared (FTNIR) spectroscopy significantly decreased. Stomatal conductance and photosynthetic rate declined 4 days after inoculation. Physiological damage to leaves caused by fungal infection was more severe than mechanical damage. Three wavelength extraction algorithms [particle swarm optimization (PSO), bootstrapping soft shrinkage (BOSS), and least absolute shrinkage and selection operator (LASSO)] were combined with three machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), and categorical boosting (CatBoost)] to perform feature-level fusion on spectral data, photosynthetic parameters, and vegetation indices to improve classification accuracy. The fusion model had high classification accuracy (0.958–0.989) and Matthews correlation coefficient (MCC) (0.917–0.978). The model achieved the best performance in distinguishing leaves with early disease symptoms from healthy leaves, with an accuracy of 0.989, an F1 score of 0.989, and an MCC of 0.978. This research provides a reliable theoretical basis and technical support for the precise identification and early prevention and control of citrus anthracnose.
Why it matches plant phenotyping methods柑橘葉の病害状態をスペクトル・生理計測から推定する早期検出法を開発し、特徴抽出と機械学習モデルの性能を評価しており、植物表現型取得・推定が中心である。
abstractThis study proposes an early detection method for citrus leaf anthracnose that integrates spectral and physiological data.
Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAIₚ as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAIₚ model performed better than the comparison model, showing higher accuracy and adaptability (R² = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R² = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.
Why it matches plant phenotyping methods植物キャノピーのLAIおよび光合成LAIを非破壊・連続推定するセンサー／逆解析法を開発し、破壊サンプリング等で検証している。植物形質取得とモデル連携が研究の中心である。
abstracta non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R² ≥ 0.94) and the photosynthetic leaf area index (LAIₚ, R² ≥ 0.91)
Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.
Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。
abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.
Photosynthesis sustains life on Earth, yet we still lack comprehensive understanding of the biochemical and environmental factors that affect this fundamental process. Steady-state models of C 3 photosynthesis provide a powerful framework but rely on reliable estimation of numerous parameters from gas-exchange data. Despite methodological advances, how model structure and data choice influence parameter accuracy and consistency remains poorly explored. Here, we systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements. Using synthetic photosynthesis response curves generated from the examined models with sampled parameter values, we applied Bayesian inference to quantify parameter uncertainty and estimation performance for the considered models. We showed that while key parameters of C 3 photosynthesis, such as maximum rate of RuBP-saturated carboxylation and of electron transport through photosystem II, can be reliably estimated from a single A-Ci curve, other parameters, such as leaf mitochondrial respiration and CO 2 compensation point, require expanded sampling of light response space. We also demonstrated the advantage of using simultaneous estimation of all model parameters over biasing the estimation by keeping some parameters fixed to prior values. Usage of barley gas-exchange data further demonstrated that parameter consistency across models can be evaluated comparing different levels of measurements and depends strongly on both model formulation and data type. Together, our study provides practical guidance for selecting photosynthesis models, designing phenotyping strategies and choosing parameterization approaches for steady-state C 3 photosynthesis.
Why it matches plant phenotyping methodsガス交換データから光合成パラメータを推定するモデルと測定設計を体系的に評価しており、植物生理形質の取得・推定手法が研究の中心である。
abstractwe systematically evaluate parameterization across nine steady-state photosynthesis models and different levels of gas-exchange measurements.
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
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 · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published5 Feb 2026Advances in Agrogeophysics: Techniques and Applications in AgricultureCited by 0 · OpenAlex ↗
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 .
Why it matches plant phenotyping methodssEITを用いて根圏の水分動態・根構造・根の活動を時空間的に取得し、他センサーとの時系列解析で植物の水輸送・生理状態を評価しており、植物状態のセンシング手法の実質的な適用が中心です。
abstractThe rhizosphere was monitored with spectral electrical impedance tomography ( sEIT ) to capture soil water content ( SWC ) dynamics, root structure, and activity.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate, spatially explicit quantification of the fraction of absorbed photosynthetically active radiation (fPAR) in tall conifer plantations is essential for productivity modelling and breeding, yet standard nadir-view optical UAV imagery yields only two-dimensional surface estimates. We developed an unmanned aerial workflow that fuses centimeter resolution LiDAR point clouds with five band multispectral imagery to produce a three-dimensional voxelized canopy structure in which top-of-canopy multispectral reflectance values are propagated downward within each vertical column. Ground measurements of fPAR and chlorophyll fluorescence were collected contemporaneously and used to calibrate Random Forest, XGBoost, Support Vector Machine (SVM), and Partial Least Squares Regression models built from 14 spectral indices. Random Forest explained 84 % of fPAR variance (RMSE = 0.12), outperforming alternative algorithms. Application of the trained Random Forest model to the voxelized canopy (0.01 m × 0.01 m × 2 m) across 28 ha generated three-dimensional fPAR maps that revealed a 26 ± 4 % increase from lower to upper crowns and a seasonal shift of up to 9 %. Compared with conventional plot-level inversion, the workflow significantly reduced field labour and improved prediction accuracy. The fusion pipeline provides a species-specific tool for high-throughput phenotyping, precision silviculture, and genomic selection in slash pine plantations under clear-sky conditions (solar zenith angle 20-30°); transferability to other sites, species, or illumination conditions requires further validation.
Why it matches plant phenotyping methodsLiDAR・マルチスペクトル融合と機械学習により、樹冠内fPARを3次元推定する高スループット植物フェノタイピング手法を開発・検証しており、方法が中心である。
abstractWe developed an unmanned aerial workflow that fuses centimeter resolution LiDAR point clouds with five band multispectral imagery to produce a three-dimensional voxelized canopy structure
Vertical farming offers a promising solution to global food security and urbanization challenges, yet its widespread adoption is hindered by high costs, particularly for lighting. Addressing this requires enhancing light use efficiency (LUE) through intelligent control strategies. While numerous studies have investigated the effects of light intensity on lettuce growth, relatively few have explored the potential benefits of stage-specific light regulation. In this study, we first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception. Utilizing this quantitative framework, we conducted a dynamic light experiment with lettuce in a commercial plant factory to evaluate four dynamic light-intensity strategies. The proposed 3D phenotyping pipeline demonstrated promising performance for canopy information extraction, with RMSEs for plant height, canopy diameter, and projected leaf area of 0.79 cm, 1.05 cm, and 44.3 cm², respectively. The “high-low-high” dynamic lighting strategy, applying higher light intensity during the early and late growth stages and lower intensity during the mid-growth stage, successfully optimized canopy morphology for better light capture. This treatment significantly increased shoot fresh and dry weights by 28 % and 65 %, respectively, compared to constant lighting. Furthermore, it enhanced LUE based on incident and intercepted light integrals by 67 % and 19 %, while reducing electricity consumption per unit of fresh weight by 24 %. Nutritional quality analysis showed the treatment increased soluble sugars and starch contents. By integrating advanced 3D phenotyping with dynamic light intensity control, this study demonstrates a prototype for intelligent decision-making to enhance yield and energy use efficiency in practical vertical farming.
Why it matches plant phenotyping methods自動3Dフェノタイピングパイプラインを開発し、マルチビュー再構成で植物体形態と光遮断を定量化、精度評価も実施しており、フェノタイピング手法が研究の中心である。
abstractwe first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception.
Abstract Sucrose is the central unit of carbon and energy in plants. As the product of photosynthesis, it is transported from source–to–sink tissues across both short and long distances. Subcellular sucrose concentrations strongly influence rates of transport within cells, tissues, and organs. Moreover, as a central metabolite, its concentration influences the rates of many enzymatic reactions. Measuring sucrose concentration with subcellular resolution remains challenging, especially for the cytosol, which hosts many critical enzymatic reactions and, in many cells, occupies only a thin layer between the vacuole and the plasma membrane. Here, we review the methods that have been utilized to measure subcellular sucrose concentrations in plant cells. The approaches covered include microautoradiography, non-aqueous fractionation, Fourier transform infrared (FTIR) microspectroscopy, Raman microspectroscopy, mass spectrometry imaging, Förster resonance energy transfer (FRET) nanosensors, direct sampling, and theoretical modelling. We provide perspectives on the use cases for these methods and discuss developments towards resolving subcellular sugar concentrations in live tissues.
Why it matches plant phenotyping methods植物組織内の細胞内ショ糖濃度という生理形質を測定する手法群をレビューし、各手法の利用場面と発展を論じているため、植物フェノタイピング手法レビューに該当する。
abstractHere, we review the methods that have been utilized to measure subcellular sucrose concentrations in plant cells.
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.
Why it matches plant phenotyping methodsPAMによる光合成量子収率測定のアーティファクトを検証し、照明条件と測定 geometry が植物生理形質の取得精度に与える影響を評価しているため、測定法の技術的検証が中心である。
abstractThese findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology.
Leaf gas exchange is the key driver of forest carbon uptake and directly determines forest carbon sink activity. Additionally, plants release a variety of biogenic volatile organic compounds (VOCs) acting as stress signals of trees. However, continuous hourly resolved measurements of leaf gas exchange and VOC emissions in tall tree canopies are challenging and remain scarce. To this end, we developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest. We additionally measured sap flux density (Js), radial growth and tree water deficit (TWD) to gain a holistic picture of seasonal leaf and stem water and carbon flux dynamics during the summer of 2024. During midsummer, we found a gradual reduction of stomatal conductance (gs) and VOC emissions of sun, but not shade branchlets of P. menziesii in response to moderate atmospheric and edaphic drying. Decreased gs led to a downregulation of transpiration (E), Js, and carbon isotope discrimination accompanied by an increase in TWD and intrinsic water used efficiency. Leaf gas exchange of shade branchlets remained unaffected due to microclimatic buffering effects. Contrarily, sun leaves of F. sylvatica, profited from sunny midsummer conditions and increased leaf gas exchange, whereas shade leaves benefitted from more diffuse light during early summer exhibiting similar carbon assimilation, transpiration and VOC emissions as sun leaves. For both species we found a clear time lag of four to five hours between maximum leaf and stem water fluxes and a delay of up to 20 hours for the recovery of TWD, highlighting the role of stem water reserves. Pronounced seasonal and diurnal differences of leaf gas exchange, stem water fluxes and VOC emissions showed, that continuous data are essential to better understand variability of ecosystem flux dynamics.
Why it matches plant phenotyping methods樹木葉のガス交換を連続測定する24チャンバーのin situ測定システムを開発し、植物の生理形質・状態を取得する方法が研究の中心であるため。
abstractwe developed a sophisticated in-situ leaf gas exchange measurement system with 24 cuvettes deployed on mature Fagus sylvatica (n=3) and Pseudotsuga menziesii (n=3) individuals in a mixed temperate forest.
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Abstract We present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping that quantifies dynamic decoupling between a thermal channel (e.g., leaf tem-perature proxy ∆T or canopy temperature) and a photochemical channel (e.g., ΦPSII, NPQ, or fluorescence-derived yields). The core measurement is a signed loop-area in a phase planespanned by the two signals under a symmetric perturbation (light or VPD ramp; option-ally sinusoidal forcing). We formalize this as the Sakib Thermo-Photochemical Hys-teresis Index (Sakib-Index) and provide mathematically grounded normalizations: the Sakib Coupling Coefficient (SCC) and the Sakib Phase-Lag Constant (SPLC). We show how loop area connects to phase-lag for periodic forcing and propose minimalcomputational checks for robustness (closure, sampling invariance, and directionality). Tendata-based illustrations are generated from open-access plant datasets (tomato chlorophyllfluorescence/reflectance and cottonwood leaf-temperature microclimate records), plus sixconceptual diagrams clarifying the assay pipeline.
Why it matches plant phenotyping methods植物ストレスの熱・光化学シグナルから新たな定量表現型を抽出する測定プロトコルと計算指標を中心に提案しており、方法開発に該当する。
abstractWe present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published11 Jan 2026FMDB Transactions on Sustainable Computing SystemsCited by 0 · OpenAlex ↗
The purpose of Precision Agriculture is to incorporate technology into various agricultural processes to increase efficiency and productivity. In fact, Precision Agriculture uses advanced technologies such as sensors and data analytics to improve crop yields. However, a significant challenge in this area is effectively integrating multiple data sources to accurately predict crop health and yield using all available information. This problem arises because traditional models typically use spectral analysis or deep learning techniques independently. Due to this separation, neither method generates the desired results. Researchers propose a solution to this issue by combining spectral analysis and deep learning for multimodal data fusion in precision agriculture. Our integrated approach begins with the collection of multispectral data from drone- or satellite-based sensors to characterise crop types. Spectral analysis will determine each crop type's chlorophyll and water content, which affect plant health. Deep learning will be used to analyse the intricate interconnections between crop yields and derived attributes to understand their relationships better. Integrated use of these two technologies will give us a broader range of data and knowledge about crop variety health and yield than single-use or standalone applications.
Why it matches plant phenotyping methodsマルチスペクトルセンサーと深層学習を統合し、作物のクロロフィル・水分量、健康状態、収量を推定する方法が研究の中心である。
titleIntegrating Deep Learning and Spectral Analysis for Multi-Modal Data Fusion in Precision Agriculture for Enhancing Crop Health Monitoring and Yield Prediction
• Multi-sensor phenotyping links soil, canopy, and atmosphere in real time • PLSR with VIP retrieves photosynthetic rate (A) and stomatal conductance (Gs) • Temporal dynamics captured with GAMs under contrasting water regimes • Targeted blue and red bands, together with a wide NIR spectrum, dominate trait prediction beyond NDVI proxies • Scalable for breeding and on-farm monitoring with minimal ground truthing Understanding the soil-plant-atmosphere continuum (SPAC) is essential for breeding and advancing precision agriculture. Despite advances in hyperspectral monitoring, few studies have captured dynamic photosynthetic traits, such as net photosynthetic rate (A) and stomatal conductance (Gs), limiting insight into their temporal fluctuations and utility in breeding for stress resilience. This study integrates plant, soil and atmosphere sensor data, with statistical modelling to monitor season-long, fine-scale physiological and environmental variables, including A, Gs, vapor pressure deficit, soil moisture and crop water stress. A multi-sensor high-throughput phenotyping platform (HTPP) with a novel soil moisture system enabled high-resolution monitoring. Partial least squares regression (PLSR) models were used to predict photosynthetic traits from hyperspectral bands (∼400-1000 nm) and selected 20 vegetation indices (VIs). Temporal dynamics of both observed and predicted values were fitted using generalized additive models (GAMs) to describe the seasonal trajectories of photosynthetic traits, crop stress status and soil moisture across genotypes and water regimes. In wheat field trials, hyperspectral data predicted A and Gs with high accuracy (Root mean square error of prediction 3.71 and 58.93, respectively; R-squared 0.72 and 0.70, respectively) and the predicted temporal dynamics closely matched ground-truth measurements. Additionally, soil moisture and crop water status were monitored throughout the season, along with physiological traits. This approach provides scalable, data-driven solutions to support breeding for resilient cultivars and improvements in crop management, as the predicted data can be integrated into mechanistic crop models to establish empirical relationships with parameters that vary throughout the growing season.
Why it matches plant phenotyping methods植物の光合成速度と気孔コンダクタンスをマルチセンサー・ハイパースペクトルデータから推定し、精度検証と時系列解析を行う高スループット表現型計測手法が中心である。
abstractA multi-sensor high-throughput phenotyping platform (HTPP) with a novel soil moisture system enabled high-resolution monitoring.
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.
Why it matches plant phenotyping methods自律走行ロボットに搭載したLIFTセンサーで光合成効率を高スループット測定し、作物の生理形質と構造形質を圃場でマッピングする方法・プラットフォームが研究の中心である。
abstractwe installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm')
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.
Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。
titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
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.
Why it matches plant phenotyping methods植物ストレスを対象としたマルチモーダル画像・分光・熱・蛍光センシングと機械学習による表現型抽出を中心に扱う方法レビューであり、植物フェノタイピング手法の範囲に明確に該当する。
titlePlant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.
The tea plant (Camellia sinensis) is economically and nutritionally important because of its bioactive compounds. Photosynthesis directly affects tea's growth and productivity, requiring a detailed study of its relationship with cultivation outcomes. We developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction. The ISBNet architecture was optimized for precise leaf–stem segmentation from point cloud data, achieving 0.897 average precision (AP) for leaves and 0.793 AP for stems. We then created a plant leaf morphology-adapted meshing algorithm optimized for plant leaf morphology, achieving an average mesh reduction of approximately 96% while maintaining morphological fidelity compared with conventional meshing methods. We generated multiple tea plant canopies representing distinct planting patterns, and used a ray tracing algorithm to simulate the spatiotemporal distribution of light within these structures. Canopy photosynthesis simulation revealed significant cultivar-specific differences, with 'Yuehuang 1' exhibiting the highest photosynthetic activity. Dense planting (10 cm spacing) significantly enhanced canopy photosynthetic rates compared with wider spacing (20 cm), and a strong linear correlation (r = 0.99) was identified between total leaf area and daily canopy photosynthetic rate across cultivars. This work establishes a methodological foundation for precision agriculture optimization in perennial crops, providing quantitative guidance for maximizing tea plantations' productivity through optimal cultivar selection and spatial configuration.
Why it matches plant phenotyping methods茶樹キャノピーの3D再構築、葉・茎セグメンテーション、形態適応メッシュ化、光線追跡による光合成推定を統合した方法開発が中心であり、植物形態・光合成状態の定量化に直接つながる。
abstractWe developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction.
Stomatal conductance (gs) is indicative of plant carbon dioxide uptake via photosynthesis and water loss via transpiration, making it a crucial plant biophysical trait. Direct measurement of gs is labor-intensive and usually not scalable to large fields. Using manual measurements to estimate parameters of gs models is even more labor-intensive and prone to sampling errors. This study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data, which not only disentangles the impacts of genotype-specific stomatal traits and environmental conditions but also enables the prediction of gs in new environments. The methodology involved using simulated thermal imagery data generated from a 3D biophysical model to train a machine learning model that could be applied to real thermal images to predict stomatal model parameters and gs itself. The method was evaluated by comparing predictions against manual gs measurements, all of which were not part of the model training process, as the model was trained against only simulated images. When compared against manual gs measurements using a porometer, the prediction R2 was 0.7, which is likely comparable to the accuracy of the manual porometer-based gs measurements (relative to a leaf gas exchange system). The developed pipeline enables high-throughput gs model parameter calibration and gs estimation.
Why it matches plant phenotyping methods熱画像と機械学習を用いて植物の気孔コンダクタンスを推定・モデル較正するパイプラインを開発し、手動測定と比較検証しており、植物フェノタイプ取得法が研究の中心である。
abstractThis study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data
Accurate assessment of the physiological and mechanical condition of trees in urban environments represents a key component of risk management and the planning of protection measures. This study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy. Multispectral analysis was used to calculate the NDRE index of physiological activity, while the 3D trunk model was employed for precise positioning of the Arbotom sensors. Tomographic measurements performed at heights of 40 cm and 200 cm identified degradation zones with a reduction in load-bearing cross-sectional area of 27-39% (lower section) and 43-52% (upper section). The NDRE index indicated localized areas of reduced physiological activity within the crown, while the VTA method confirmed the presence of fungi of the genus Ganoderma. The integrated results indicate that the tree currently maintains a stable mechanical structure, with localized degradation zones that do not yet affect its static stability. The presented multi-sensor approach is highlighted as an efficient tool for detection, evaluation, and risk management in urban forestry, however as this research was conducted on a single Tillia platyphyllos specimen, the findings should be interpreted as a case study and methodological demonstration rather than as results directly generalizable to a broader population of urban trees.
Why it matches plant phenotyping methods樹木の生理状態と構造安定性を推定するため、UAVマルチスペクトル画像、3Dフォトグラメトリ、音響トモグラフィーなどを統合した測定手法が中心であり、方法論的実証として報告されている。
abstractThis study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy.
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.
Why it matches plant phenotyping methods植物の光合成性能（ΦPSII、ETR）をリアルタイム測定し、その値に基づく照明制御システムを作製・長期検証しており、植物表現型取得と制御手法が研究の中心である。
abstractBiofeedback 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).
Accurate estimation of the fraction of absorbed photosynthetically active radiation (FAPAR) is crucial for understanding plant productivity and ecosystem dynamics. A number of indirect measurement techniques are used for estimating FAPAR with hand-held instruments, but researchers have identified discrepancies among different techniques when using them to validate satellite land products. Many researchers have also utilised photosynthetically active radiation (PAR) sensors to obtain quantitative measurements of PAR, but these lack robust measurement frameworks and protocols. Only very limited research has started on automated wireless PAR network systems to measure at finer temporal scales as well as to reduce human error and logistical costs. This study evaluates the performance of two flux (2f) and four flux (4f) FAPAR measurement systems and digital hemispherical photography (DHP) across multiple vegetation types (e.g., vineyard, broadleaf deciduous forest, savanna woodland) and different temporal scales (instantaneous and daily integrated). Results reveal strong agreement (R² > 0.99, RMSE ≤ 0.04) between 2f- and 4f-FAPAR for all three study sites, with minimal overestimation (bias ≤ 0.04) by the 2f systems, suggesting that it can substitute, over similar environments, the more complex and costly 4f setup without substantially compromising accuracy. Daily integrated FAPAR exhibited greater stability and lower uncertainty compared to instantaneous FAPAR, underscoring its importance for long-term ecosystem monitoring. However, instantaneous FAPAR remains essential for satellite product validation due to its alignment with satellite overpass times. Additionally, 2f-FAPAR showed a good relationship with DHP-derived FAPAR. The findings highlight the potential of the 2f wireless PAR network as an automated, cost-effective, and reliable tool for canopy light absorption studies, offering substantial advantages for both ground-based ecosystem monitoring and remote sensing applications.
Why it matches plant phenotyping methodsFAPARという植物キャノピーの生理・機能形質を対象に、無線PARセンサーネットワークと複数の測定方式を比較評価し、精度・安定性・代替可能性を検証している。測定法の評価が研究の中心である。
abstractThis study evaluates the performance of two flux (2f) and four flux (4f) FAPAR measurement systems and digital hemispherical photography (DHP) across multiple vegetation 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 (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.
Why it matches plant phenotyping methods植物のクロロフィル蛍光・光合成性能をリアルタイムに測定し、その値に基づく照明制御システムを作成・長期検証しており、植物生理状態の取得とフィードバック手法が中心である。
abstractBiofeedback 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).
Accurate yield estimation is crucial for ensuring national food security and balancing supply and demand. Remote sensing (RS) process models are commonly used for regional-scale crop yield estimation, with the maximum carboxylation rate at 25° C (Vm25) being a key parameter influenced by genetic varieties, environmental conditions, and spatio-temporal variations. However, most remote sensing process models use a fixed Vm25value to simulate maize yield at regional scales. These models ignore variations in Vm25across time, space, and environmental conditions, leading to uncertainties in simulation. To address this issue, we developed a convolutional neural network (CNN) model combined with Vm25-related variables to estimate dynamic Vm25values for maize in Ningxia (NX), a typical semi-arid region of China. By integrating the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) with three drought-related hydrometeorological factors—Evapotranspiration (ET), Vapor Pressure Deficit (VPD), and Soil Water Content (SWC)—the model's prediction accuracy of Vm25was significantly improved, achieving an R2of 0.88 and an RMSE of 2.21 μmol m-2s-1on the test set. These dynamic Vm25values were then integrated into the RS process model (PRYM-Maize-Dr) to improve maize yield simulations under drought conditions in NX. Validation using data from 2010 to 2021 showed that, at the city level, the R2increased from 0.67 to 0.78 and RMSE decreased from 0.57 t ha-1to 0.43 t ha-1, while at the county level, the R2increased from 0.59 to 0.71 and RMSE decreased from 0.60 t ha-1to 0.53 t ha-1. These results highlight the potential of integrating RS process models with optimized Vm25for accurate spatio-temporal crop yield estimation at the regional scale.
Why it matches plant phenotyping methodsCNNとリモートセンシング変数により、トウモロコシの生理形質Vm25を動的推定する手法を開発・検証しており、単なる収量測定ではなく植物形質の取得・抽出が中心である。
abstractwe developed a convolutional neural network (CNN) model combined with Vm25-related variables to estimate dynamic Vm25values for maize
Crop traits are the integrated outcome of genetic variation, environmental conditions, and their complex interactions, rendering accurate prediction from genetic markers alone a persistent challenge. Here, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes from a multiple-parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model in predicting the photosynthetic rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP enabled us to survey genetic variability in enzyme kinetic parameters, which can be used to identify targets for the improvement of photosynthesis. This approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the accuracy of photosynthetic trait predictions.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワーク自体が研究の中心であり、遺伝マーカーとガス交換測定を統合した植物生理形質の推定手法を開発・評価している。
abstractHere, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes
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 · publicAll codes and data to ensure the reproducibility of the results can be accessed at https://github.com/Rudan-X/KineticGP .Open asset ↗GitHub · Rudan-X/KineticGPlines:231-264Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
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.
Why it matches plant phenotyping methodsバジルの水ストレス応答を、RGB・深度・クロロフィル蛍光画像と3D-CNNで非破壊推定するフェノタイピング手法が研究の中心である。
abstractThis 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.
Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection. Key features • Protocol for detached leaves handled in laboratory conditions. • Based on differential (dark-adapted versus irradiated) hyperspectral images of plant leaves. • Data analysis includes machine learning methods and the calculation of a vegetation index. • Requires irradiation equipment apart from the hyperspectral camera set.
Why it matches plant phenotyping methods葉の反射ハイパースペクトル画像から葉緑体位置を検出・分類する手法と指標を開発し、高スループット測定として提示しており、植物表現型取得が中心である。
abstractWe present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light.
Reproduction assets foundThe protocol explicitly deposits its authors' analysis code (HyperspectralImageProcessing.m, including the pretrained CNN classifier for chloroplast positioning) on GitHub and makes the original hyperspectral images of Arabidopsis and Nicotiana leaves used in the paper's figures available on figshare. Both are paper-‐Code · publicAll code has been deposited to GitHub: https://github.com/plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detection (access date, 08/18/2025)Open asset ↗plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detectionhtml-lines:104-130Dataset · publicOriginal files with hyperspectral images of Nicotiana benthamiana and Arabidopsis thaliana (WT and phot2) leaves, including recordings shown in Figure 3 and Figure 4 of this protocol, can be downloaded from https://figshare.com/articles/dataset/Hyperspectral_images_of_Arabidopsis_thaliana_and_Nicotiana_benthamiana_leaves/30402409?file=58898569Open asset ↗html-lines:104-130Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methods凍結融解耐性の評価プロトコルと複数の生理指標を比較・検証し、LT50測定の妥当性を評価しているため、表現型取得法が中心的です。
abstractYet, estimates of FTST of Antarctic plants have remained inconsistent across studies, largely due to methodological differences in freezing regimes and injury assessment metrics.
Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber plants were compared with physiological and metabolic data. Voreas plants were unable to survive seven days of low-temperature stress due to a drastic increase in electrolyte leakage and a decrease in the net photosynthesis rate, stomatal conductance, and transpiration rate. Gergana plants survived chilling by preserving cell membrane integrity and photosynthesis efficiency. During chilling treatment, the content of most metabolites in both cultivars was reduced compared to the controls, yet it was much more pronounced in Voreas. We observed an increased accumulation of cinnamic acid on the seventh day only in the Gergana cultivar. A MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves. Differences in absorption spectra were observed among control, stressed, and recovered plants, across different days of stress, and between the studied cultivars. The most significant differences were in the 1300-1600 nm range, much smaller for Gergana than Voreas. Aquagrams of the two cultivars also reveal differences in their responses to low temperatures and changes in water molecular structure in the leaves. The errors of prediction for the days of chilling by using PLS models were from 0.96 to 1.14 days for independent validation, depending on the spectral data of different leaves used. Near-infrared spectroscopy and aquaphotomics can be used as additional tools for early detection of stress and investigation of low-temperature tolerance in cucumber cultivars.
Why it matches plant phenotyping methods近赤外分光法とアクアフォトミクスを用いて、キュウリ葉の低温ストレス状態を非破壊的に検出・予測する手法を評価しており、植物表現型取得が中心です。
abstractA MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves.
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
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 · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Apple fruit quality is primarily determined by Vitamin C (VC), Soluble Saccharides (SSs), Titratable Acid (TA), and the Soluble Saccharides/Titratable Acid (SSs/TA). This study aims to establish a prediction model based on the Back Propagation (BP) neural network by analyzing the intrinsic relationships between these quality indicators and the photosynthetic physiological characteristics of fruit trees, providing a new method for the precise prediction and regulation of fruit quality. Using 'Fuji' apple as the material, fruit quality indicators, leaf photosynthetic parameters, canopy structure indicators, and carbon-water-nitrogen metabolism indicators were systematically measured. Correlation analysis was employed to identify key influencing factors, BP neural network models with different hidden layer structures were constructed, and the optimal feature subset was screened through feature importance analysis, single-factor sensitivity analysis, and ablation experiments, ultimately establishing a simplified and efficient prediction model. Pn, Gs, SPCI, and DUE showed significant positive correlations with VC, SS, and SS/TA, whereas N and NLT were significantly positively correlated with TA content. SUE was identified as a common core driving factor for VC, SS, and SS/TA. The BP neural network demonstrated strong predictive performance for the four quality indicators, with the optimal model achieving validation set R 2 values of 0.87, 0.86, 0.86, and 0.89, respectively. The simplified model developed through feature screening exhibited further improved performance: the validation set R 2 for the VC prediction model increased to 0.93, while MAE and MAPE decreased by 32% and 35%, respectively. Photosynthetic characteristics and nitrogen metabolism status of the fruit trees serve as key physiological foundations determining apple quality. The quality prediction model based on the BP neural network achieved high accuracy, and its predictive performance was significantly enhanced after feature refinement, providing an effective tool for precise apple quality prediction and smart orchard management.
Why it matches plant phenotyping methodsリンゴ果実品質という植物形質を対象に、BPニューラルネットワーク、特徴量選択、感度分析、アブレーション実験を組み合わせた予測手法を開発・検証しており、形質推定法が研究の中心である。
abstractThis study aims to establish a prediction model based on the Back Propagation (BP) neural network
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.
Why it matches plant phenotyping methodsChlF・Vis-NIR・機械学習を統合し、植物の低温ストレス重症度を非破壊推定する手法の開発と検証が中心である。
abstractthis 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.
Accurate prediction of photosynthetic parameters is pivotal for precision viticulture, as it enables non-invasive monitoring of plant physiological status and informed management decisions. In this study, spectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf), in Vitis vinifera (cv. Müller-Thurgau) grown in an experimental vineyard in Lower Franconia (Germany). Measurements were obtained on 25 July, 7 August, and 12 August 2024 using a LI-COR LI-6800 system and a PSR+ hyperspectral spectroradiometer. Various machine learning models (SVR, Lasso, ElasticNet, Ridge, PLSR, a simple ANN, and Random Forest) were evaluated, both as standalone predictors and as base learners in a stacking ensemble regressor with a Random Forest meta-learner. First derivative reflectance (FDR) preprocessing enhanced predictive performance, particularly for ΦPSII and ETR, with the ensemble approach achieving R2 values up to 0.92 for ΦPSII and 0.85 for A at 1 nm resolution. At coarser spectral resolutions, predictive accuracy declined, though FDR preprocessing provided some mitigation of the performance loss. Diurnal patterns revealed that morning to mid-morning measurements, particularly between 9:00 and 11:00, captured peak photosynthetic activity, making them optimal for assessing vine vigor, while midday water potential declines indicated favorable timing for irrigation scheduling. These findings demonstrate the potential of integrating hyperspectral data with ensemble machine learning and FDR preprocessing for accurate, scalable, and high-throughput monitoring of grapevine physiology, supporting real-time vineyard management and the use of cost-effective sensors under diverse environmental conditions.
Why it matches plant phenotyping methodsハイパースペクトル測定と機械学習によるブドウの光合成・水ポテンシャル推定が研究の中心であり、複数モデルの性能評価と前処理比較も実施しているため。
abstractspectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
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.
Why it matches plant phenotyping methods作物の水状態に関する表現型形質の取得技術と診断モデルを体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractThis paper systematically reviews research progress in using high-throughput phenotyping to obtain water-sensitive phenotypic traits and construct crop water demand diagnosis models.
Faba bean ( Vicia faba L.) is a key protein crop, but its cultivation and yield stability are hindered by a number of environmental stresses. Stomata regulate gas exchange between the plant and atmosphere, playing a central role in photosynthesis and mediating plant responses to a wide range of environmental stressors. This study aimed to investigate variations in photosynthetic regulation in faba bean, and to examine leaf temperature and the response to short-term acute ozone (O₃) exposure as proxies for stomatal function. Here, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions. A subset of extreme genotypes, identified based on relative leaf tempreture from the initial screening, was exposed to a 450 ppb O₃ treatment. Our results revealed strong positive relationship between photosynthetic efficiency and relative leaf temperature. A three-fold difference in relative leaf temperature was observed among genotypes. The O₃ treatment caused signicantly less damage in genotypes with higher leaf temperature compared to those with lower leaf temperature (p < 0.001). By combining a HTPP platform with elevated O₃ stress treatment, we identified faba bean genotypes with contrasting stomatal responses to the O₃ exposure. Our results advance understanding of the regulation mechanisms of photosynthetic light reactions and the role of stomatal function in modulating faba bean responses to environmental stressors. • High-throughput phenotyping reveals large variation in leaf temperature among faba bean genotypes. • Leaf temperature strongly affects photosynthetic regulation in faba bean. • The tested genotypes with higher leaf temperatures display increased ozone tolerance.
Why it matches plant phenotyping methodsHTPPプラットフォームを用いて196遺伝子型の葉温度、光合成、気孔機能を高スループット測定し、表現型に基づく選抜とオゾン応答評価を行っており、フェノタイピング手法の応用が研究の主要部分です。
abstractHere, we used a high-throughput plant phenotyping (HTPP) platform to screen 196 faba bean genotypes for photosynthetic and stomatal function under controlled conditions.
Cost-effective remote sensing solutions are critically needed to democratize precision agriculture technologies. While hyperspectral and LiDAR systems deliver high accuracy, their prohibitive costs limit widespread adoption. This study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument, achieving crop trait prediction accuracy comparable to systems costing 10–50 times more. We developed a comprehensive framework integrating spectral indices, geometric parameters, and texture metrics from commodity RGB sensors to predict five critical cotton traits: leaf area index (LAI), intercepted photosynthetically active radiation (IPAR), above-ground biomass, lint yield, and seed cotton yield. The progressive integration approach employed Random Forest regression with four feature configurations: baseline color indices (CIbₐₛₑ), refined color indices (CIᵣₑf), geometric parameters (CIᵣₑf + GP), and texture metrics (CIᵣₑf + GP + T). Field experiments across three trials over two growing seasons (2022–2023) with varying genotypes, planting densities, and sowing dates provided 2,126 ground truth measurements for model development and validation. The optimal multi-modal model achieved R² = 0.97 for IPAR (rRMSE = 6 %), R² = 0.91 for LAI (rRMSE = 15 %), and R² = 0.85 for biomass (rRMSE = 32 %), with lint yield and seed cotton yield demonstrating R² values of 0.92 and 0.77, respectively. Variance partitioning analysis revealed texture features as the dominant contributor (16.2 % ± 7.1 %), followed by spectral indices (9.1 % ± 4.2 %) and geometric parameters (8.0 % ± 2.8 %), with substantial shared variance (45–65 %) indicating strong feature complementarity. Phenological analysis demonstrated that flowering-stage imagery outperformed boll opening stage measurements, while stage-general models showed superior robustness. Cross-temporal validation confirmed model generalizability, with trial-general models achieving R² values of 0.91–0.97 for IPAR across diverse environmental conditions. The framework enables sub-meter spatial resolution trait mapping while maintaining operational simplicity and cost-effectiveness, demonstrating that systematic feature engineering can democratize high-precision phenotyping technologies for broader agricultural applications.
Why it matches plant phenotyping methodsUAV-RGB画像から複数の綿形質を推定する特徴統合フレームワークを開発・検証しており、形質取得・抽出手法が研究の中心である。
abstractThis study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument
Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco ( Nicotiana tabacum ), a well-characterized model plant system for studies of plant health and productivity. Leaves were harvested from plants cultivated under two distinct conditions: low light (LL), representing unhealthy leaves with reduced photosynthesis and high light (HL), representing healthy leaves with highly active photosynthesis. Higher-order correlation data were collected and analyzed using machine learning (ML) techniques, specifically a Convolutional Neural Network (CNN), to classify the photon emitter states. This CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL, demonstrating significantly different quantum profiles between the two conditions. These quantum fingerprints serve as a foundation for a novel unified analysis of plant growth parameters associated with different photosynthetic states. By employing CNN, the emitter profiles were able to reproducibly classify the leaves as healthy or unhealthy. This model achieved high probability values for each classification, confirming its accuracy and reliability. The findings of this study pave the way for broader applications, including the application of advanced quantum and machine learning technologies in plant health monitoring systems.
Why it matches plant phenotyping methods量子発光体による葉の光子プロファイル取得とCNN解析を組み合わせ、光合成状態および植物の健康状態を分類する手法が研究の中心である。
abstractThis CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL
Reproduction assets foundThe article's Data availability statement explicitly deposits the paper's time-tagged photon correlation data (the raw measurements underlying the quantum fingerprinting and CNN analysis) in the Dryad Digital Repository, a public, paper-specific dataset.Dataset · publicay: conceptualization, funding acqui-
sition, supervision, project administration, visualization,
writing – original draft, writing – review & editing.
Conflicts of interest
There are no conflicts to declare.
Data availability
Data for this article, including time tangled photon data, are
available at Dryad Digital Repository at https://doi.org/10.5061/dryad.1rn8pk15f.Supplementary information is available. See DOI: https://
doi.org/10.1039/d5an00326a.
Acknowledgements
This research was funded in part by the Faculty Industry
Applied Research (FIAR) program, by the University at
Buffalo’s Center of Excellence in Materials Informatics.
References
1 E. Murchie and T. Lawson, J. Exp. Bot., 2013, 6Open asset ↗Dryad Digital Repository · 10.5061/dryad.1rn8pk15fpdf-raw-page:7 lines:1-96Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Agricultural and Forest Meteorology.
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.
Why it matches plant phenotyping methods高解像度SIFの再構成手法と公開データセットを開発し、タワー観測およびGPPで検証しており、植生の光合成活動という生理状態の推定が中心である。
abstractwe 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.
Aquatic plants are key contributors to oxygen production and ecosystem stability. This study quantifies oxygen generation capacity of Hydrilla, Vallisneria , and Potamogeton under varying concentrations of potassium bicarbonate (KHCO 3 ) using a dual-limb apparatus to measure oxygen output via water displacement. The experiment was complemented by gas chromatography-thermal conductivity detector (GC-TCD) analysis and numerical simulations to validate the results. An in silico diffusion model was developed to simulate oxygen release dynamics assuming uniform oxygen generation across plant surfaces and steady-state mass transport through the surrounding medium. The findings indicate that KHCO 3 significantly enhances photosynthetic activity and oxygen production, with Hydrilla exhibiting the highest oxygen generation rate, followed by Potamogeton and Vallisneria . The optimal concentration of KHCO 3 was determined to be 5 mg/mL, beyond which oxygen production declined due to osmotic stress and ionic imbalances. GC-TCD analysis confirmed oxygen (∼90%) as the primary gas produced, while simulated results closely aligned with the experimental data, reinforcing the robustness of the in silico analysis. This study highlights the role of bicarbonate ions in enhancing carbon availability for aquatic photosynthesis, thereby optimizing oxygen generation rate. The experimental methodology coupled with a numerical framework based on spatial diffusion model, as discussed in this endeavor, is novel in estimating oxygen generation rate from whole-plant in a closed system, enabling reproducible scaling for state-of-the-art environmental technologies. The insights gained from this in silico endeavor are expected to have broad implications for wastewater treatment (enhancing aerobic biodegradation), aquaculture (maintaining high dissolved oxygen), and carbon capture (biomass-based CO 2 sequestration). Future research could focus on the exploration of long-term physiological effects of KHCO 3 supplementation on oxygen generation and improvisation of modeling framework to incorporate biological feedback mechanisms into the underlying analysis.
Why it matches plant phenotyping methods全植物の酸素生成速度という生理形質を測定・推定する装置と拡散モデルを開発し、実験およびGC-TCDで検証しており、方法が中心的です。
abstractThe experimental methodology coupled with a numerical framework based on spatial diffusion model, as discussed in this endeavor, is novel in estimating oxygen generation rate from whole-plant in a closed system, enabling reproducible scaling for state-of-the-art environmental technologies.
Accurate simulation of canopy photosynthesis is essential for predicting dry matter accumulation and crop yield. However, most current crop models overlook the effect of vertical distribution of leaf nitrogen and chlorophyll content on photosynthetic capacity at different canopy layers, resulting in greater uncertainties and weaker mechanistic explanation. Here, we developed a novel canopy photosynthesis model that establishes a bridge between chlorophyll content and photosynthetic nitrogen (PN, defined as total leaf nitrogen minus non-photosynthetic nitrogen) across different canopy heights, and then employs chlorophyll content as a reliable proxy forsimulating photosynthesis. The model was calibrated and validated using data from five field experiments under diverse treatments. Results indicate that leaves at higher canopy positions, receiving more light, contain higher nitrogen content and chlorophyll to support greater photosynthetic rates. The nitrogen extinction coefficient (KN), which characterizes the decline in available of leaf nitrogen, decreases exponentially with increasing LAI, varying among canopy depths, cultivars and growth stages. Chlorophyll shows a stronger correlation with photosynthesis compared to leaf nitrogen. By capturing these dynamics, the model enhances the accuracy of photosynthesis prediction by 60%, particularly correcting the overestimation of canopy photosynthesis and dry matter accumulation during post-flowering. These findings advance the understanding and modelling of canopy-scale photosynthesis in crop models and provide insights for better integration with chlorophyll-related remote sensing data.
Why it matches plant phenotyping methodsキャノピーの光合成をクロロフィル量と垂直窒素分布から推定する新規モデルを開発し、5つの圃場実験で較正・検証しており、植物生理形質の推定手法が研究の中心である。
abstractHere, we developed a novel canopy photosynthesis model that establishes a bridge between chlorophyll content and photosynthetic nitrogen (PN, defined as total leaf nitrogen minus non-photosynthetic nitrogen) across different canopy heights, and then employs chlorophyll content as a reliable proxy forsimulating photosynthesis.
Published26 Nov 2025The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Forest ecosystems in semi-arid boreal regions, such as those in Darkhan-Uul province, Mongolia, serve as critical reservoirs of biodiversity and carbon while facing escalating anthropogenic and climatic pressures. Despite covering 22.4% (~733 km2) of the province, these birch- and larch-dominated forests exhibit declining resilience due to unsustainable land-use practices, illegal logging, and climate-induced disturbances, necessitating advanced monitoring frameworks for sustainable forest management (SFM). This study introduces the Spectral Forest Index (SFI), a novel composite metric derived from Sentinel-2 multispectral data within a Google Earth Engine (GEE) platform, to quantify spatiotemporal variations in forest health, productivity, and species composition. By integrating normalized difference and ratio-based indices (e.g., NDVI, RVI), the SFI synthesizes canopy structural attributes, photosynthetic activity, and biomass dynamics across monthly intervals (May–October 2020–2024), enable monitoring of forest cover, health, and species composition, with quality control measures ensuring data reliability. Results reveal pronounced spatial heterogeneity in forest degradation, with SFI depressions strongly correlated with overgrazing and anthropogenic land conversion, while regenerative trajectories align with targeted reforestation initiatives. The SFI’s sensitivity to ecological stressors (e.g., drought, pest infestations) underscores its utility as a scalable, policy-relevant tool for monitoring carbon sequestration potential and guiding adaptive management. This research advances remote sensing applications in SFM, offering a transferable framework for reconciling ecological preservation with socio-economic demands in vulnerable boreal ecosystems.
Why it matches plant phenotyping methodsSentinel-2データとGoogle Earth Engineを用いて、森林の健康状態、生産性、種組成、バイオマス動態を定量化する新規SFI指標を開発しており、植物キャノピー状態の取得・抽出手法が研究の中心である。
abstractThis study introduces the Spectral Forest Index (SFI), a novel composite metric derived from Sentinel-2 multispectral data within a Google Earth Engine (GEE) platform, to quantify spatiotemporal variations in forest health, productivity, and species composition.
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.
Why it matches plant phenotyping methods蛍光画像から気孔形態・分布を半自動抽出するソフトウェア群を開発し、精度検証と植物表現型への適用を行っており、フェノタイピング手法が研究の中心である。
abstractwe present a lightweight and efficient tool, QuickSpotter, for semi-automated stomatal annotation from fluorescence images.
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.
Why it matches plant phenotyping methods植物のストレス状態を遠隔で測定するクロロフィル蛍光LiDARセンサーの開発・性能実証が研究の中心であり、植物状態の計測手法に該当する。
abstractwe 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.
Accurate almond yield prediction is essential for supporting decision-making across multiple scales, from individual growers to international markets. This is crucial in the Mediterranean region, where diminishing water resources pose significant challenges to the almond industry. In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. The almond prediction models were calibrated and validated using data provided by commercial growers, along with meteorological reanalysis and remote sensing products. The remote sensing products included: i) spectral indices, ii) vegetation biophysical traits retrieved from Sentinel-2, and iii) actual evapotranspiration (ET a ) estimated using the Priestley-Taylor two-source energy balance (TSEB-PT) model driven by Copernicus-based data. Almond yield data were collected from commercial orchards located in Spain’s Ebro and Guadalquivir basins from 2017 to 2022. Data collected from growers enables the establishment of almond water production functions at the orchard scale, yielding results comparable to those reported in experimental study sites. Almond yield prediction models calibrated with remote sensing data demonstrated predictive accuracy comparable to that of models relying on ground-truth variables provided by farmers, such as irrigation, orchard age, tree density, and cultivar. Among them, the PM CRS model—which integrates the fraction of absorbed photosynthetically active radiation (fAPAR), the normalized difference moisture index (NDMI), canopy chlorophyll content (C ab ), ETa, and meteorological data—achieved a RMSE of 399.1 kg ha - ¹ in July. These findings highlight the potential of remote sensing-based models for accurately estimating almond yield. Furthermore, the PM CRS model proved scalable and effective when applied across four almond-producing regions in the Ebro basin. Future improvements may be realized through enhanced ET a retrieval using upcoming thermal satellite missions, integration of irrigation estimates, and the adoption of advanced machine learning and deep learning algorithms.
Why it matches plant phenotyping methods衛星リモートセンシング由来の植物生理・生物物理形質と蒸発散を用いて果樹園単位のアーモンド収量を推定し、機械学習モデルを較正・検証している。収量形質の取得・推定ワークフローが研究の中心である。
abstractIn this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms.
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.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を抽出クロロフィルと比較・較正し、季節時系列へ適用して信頼性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.
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 match · UnverifiedCrossref · checked 15 Sept 2026
Published12 Nov 2025International Journal of Bio-resource and Stress ManagementCited by 0 · OpenAlex ↗
The study was conducted in the month of August, 2023 at the College of Forestry, Kerala Agricultural University, Kerala, India to identify the peak drought stress period and optimize phenotyping techniques for drought tolerance screening in teak seedlings under tropical humid conditions. The experiment subjected eight-month-old vegetatively propagated teak seedlings to controlled drought conditions over 20 days. Morpho-physiological parameters such as number of leaves, relative water content (RWC), photosynthetic rate, stomatal conductance, transpiration rate, and chlorophyll fluorescence were monitored bi-daily. The results revealed that the 9th and 10th days after withholding irrigation marked the maximum drought stress period, with significant reductions in photosynthesis (0.372 mole CO2 m-2 s-1), stomatal conductance, and RWC (51.14%). Biochemical analysis showed increased levels of proline, glycine betaine, and total soluble sugars, confirming stress adaptation. Upon rewatering, partial recovery was observed in physiological traits, while biochemical markers indicated ongoing stress response adjustments. Correlation and regression analyses highlighted strong interrelations between photosynthesis and traits such as stomatal conductance, RWC, and chlorophyll fluorescence. The findings were revalidated through repeated trials, confirming the 10th day as the optimal time for drought phenotyping in teak seedlings in given condition. This study enhances our understanding of teak’s drought response and offers critical insights for breeding programs and sustainable plantation management strategies.
Why it matches plant phenotyping methods乾燥ストレス評価におけるフェノタイピング時期・手法の最適化を主題とし、反復試験で再検証しているため、単なる生理測定ではなく方法開発・検証に該当する。
abstractidentify the peak drought stress period and optimize phenotyping techniques for drought tolerance screening in teak seedlings
Salinity is one of the major abiotic stresses affecting the growth and yield of wheat crops, particularly in arid and semi-arid regions, where irrigation water or soil with high salt content is often present. With increasing soil salinization and abrupt climate change at the global level, identifying salt-tolerant wheat genotypes has become crucial. The present study aimed to characterize and screen the salt tolerance of 25 wheat genotypes at 25, 52, 69, 90, and 118 Days After Sowing (DAS), under field conditions using thermography and bio-physiological parameters. Wheat genotypes were irrigated with saline irrigation water (with threshold EC of 4dSm/m) and performances of the genotypes were monitored using thermal image-based indices e.g., CWSI (Crop Water Stress Index), IG (index of Stomatal Conductance) and bio-physiological parameters i.e., Photosynthesis (Pn), Stomatal conductance (Ig), Transpiration rate, Leaf Area Index (LAI), Normalized difference vegetation index (NDVI), Relative water content (RWC), Total leaf chlorophyll, Membrane stability index (MSI), Osmotic pressure (OP) of leaf, Leaf Na and K. With these biophysical parameters, a new screening index named as Normalized Salinity Stress Tolerance Index (NSSTI) was developed using different multivariate analysis e.g., Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA). Based on the criteria developed in this study, NSSTI could classify the 25 wheat genotypes for salinity stress into: 6 - tolerant, 16 - moderate, and 3 - sensitive genotypes. DA confirmed the classification by NSSTI with 92-100% accuracy based on canonical discriminant functions. Further, thermal image-derived CWSI and IG differentiated tolerant and sensitive genotypes across all DAS under salt stress conditions. Irrespective of different DAS, NSSTI showed significant (p < 0.01) correlation with CWSI (0.70-0.83) and IG (0.78-0.84). The study also identified transpiration rate, RWC, OP, NDVI, and Pn as important parameters to characterize and screen wheat genotypes under salinity stress conditions at different DAS. The newly developed index - NSSTI, exhibited significant (p < 0.01) correlations with wheat yield (0.76-0.84) and biomass (0.73-0.82), indicating the usefulness of NSSTI in evaluating and screening wheat genotypes for salt tolerance. The identified wheat genotypes and key bio-physiological traits can be used in breeding programs to develop advanced salt-tolerant wheat lines. In future, the newly developed salinity stress index NSSTI would play a potential role in the screening and selection of salt-tolerant wheat genotypes under field conditions.
Why it matches plant phenotyping methods熱画像からCWSI・気孔コンダクタンス指標を抽出し、多変量解析で新規の耐塩性スクリーニング指標NSSTIを開発・検証しており、表現型取得と解析手法が研究の中心である。
abstractusing thermography and bio-physiological parameters
Aim: This study aimed to establish a phenomic-based screening protocol for cold tolerance in African marigold (Tagetes erecta L.) by integrating non-invasive imaging with physiological and biochemical analyses, addressing the gap between field crop and ornamental breeding applications where cold stress significantly constrains cultivation by affecting growth, development, and productivity. Methodology: Ten marigold genotypes were evaluated under controlled polyhouse and natural cold stress conditions across two growing seasons. High-throughput plant phenotyping utilizing RGB, near-infrared, and thermal imaging quantified key traits including morphological characteristics (via RGB), tissue water content (via near-infrared), and thermal regulation (via thermal imaging), complemented by targeted physiological and biochemical analyses. Results: Significant genotypic variation was observed, as cold stress caused 70.7% reduction in plant area and 24.3% decrease in the photosynthetic rate. Genotype Af./W-4 exhibited superior cold tolerance through enhanced photosynthetic maintenance, minimal reductions in greenness (4.4%), membrane stability (11%), and photosynthetic rate (14.2%), followed by genotypes PB and Af./W-6. Multivariate analysis indicated that key determinants of cold stress performance include traits like plant area, caliper length, greenness, and photosynthetic rate. Interpretation: Integration of non-invasive imaging with biochemical analysis successfully differentiated cold-tolerant from the susceptible genotypes. This comprehensive approach provides an efficient screening methodology for identifying climate-resilient genotypes in ornamental crops, potentially accelerating cold-tolerant genotype development for sustainable floriculture production. Key words: African marigold, Cold tolerance, Genotypic-variation, High through put phenotyping, Tagetes erecta L.
Why it matches plant phenotyping methods非侵襲イメージングを用いた高スループット植物表現型解析とスクリーニングプロトコルの確立が中心であり、冷ストレス耐性の形質抽出・評価に実質的に関与している。
abstractThis study aimed to establish a phenomic-based screening protocol for cold tolerance in African marigold (Tagetes erecta L.) by integrating non-invasive imaging with physiological and biochemical analyses
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract In agroecosystems, the variable expression of crop functional traits is expected to play a role in key processes, including plant nutrient cycling and water acquisition, that confer ecosystem resistance and/ or resilience to environmental change. The ability to estimate crop trait data is therefore critical to predict crop responses to environmental change, enabling more informed diagnosis of crop performance and on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping— specifically reflectance spectroscopy— has emerged as a key element of plant trait research, capable of estimating plant traits more rapidly. However, little is known about whether or not reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits. Using wine grapes ( V. vinifera subsp. vinifera ) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy and the subsequent partial least squares regression modelling approach to quantify intraspecific variation in 12 functional traits across 12 different cultivars. Results showed significant differences in traits, especially in the photosynthetic and hydraulic traits, among closely related cultivars, falling along a resource-conservative to resource-acquisitive axis of variation. We also found that reflectance differentiated this fine-scale trait variation, specifically in leaf chemical and morphological traits, contributing to higher accuracy, and indicating that this HTP approach is viable for detailed trait estimation in diverse agroecosystems.
Why it matches plant phenotyping methods反射分光とPLS回帰によるブドウの複数機能形質推定を主目的とし、ハイスループット表現型解析手法の性能・実用性を評価しているため。
abstractthis study aimed to assess the ability of reflectance spectroscopy and the subsequent partial least squares regression modelling approach to quantify intraspecific variation in 12 functional traits across 12 different cultivars.
Chlorophyll fluorescence parameters (CFPs), especially maximum photosynthetic efficiency of optical system II (Fv/Fm), are the intrinsic photosynthesis probes of crop stress and photosynthetic function. Hyperspectral image (HSI) offers a rapid alternative to traditional pulse amplitude modulation for Fv/Fm, but selecting the uninformative wavelengths reduce accuracy. To address this, a Wavelet Cluster (WCL) method based on Continuous wavelet transform (CWT) was proposed to enhance sensitive wavelengths extraction. Spectral data was preprocessed by Savizky-Golay smoothing (SG) and Multiple scattering correction (MSC), followed by CWT decomposition with bior3.3, gaus4 and meyr wavelet functions to form WCL. Sensitive wavelet coefficients (WCs) were selected using Monte Carlo uninformative variable elimination (MC-UVE), and the Partial Least Squares Regression (PLSR) and Random Forest (RF) modeling methods were established. The results showed that (1) the reflectance spectrum decreased with the increase of Fv/Fm in potato leaf. (2) WCL effectively captured chlorophyll fluorescence spectral features. (3) The WCL-RF model outperformed WCL-PLSR, with a calibration set Rc2 of 0.75, RMSEc of 0.0209, a prediction set Rp2 of 0.73, RMSEp of 0.0225, respectively. This study demonstrates the potential of WCL for accurate Fv/Fm detection, and supports for potato canopy photosynthetic activity assessment.
Why it matches plant phenotyping methodsジャガイモ葉のクロロフィル蛍光指標Fv/Fmという植物生理形質を、ハイパースペクトル画像と新規のウェーブレットクラスタ解析で推定し、モデル性能も評価しているため、フェノタイピング手法が中心である。
abstractTo address this, a Wavelet Cluster (WCL) method based on Continuous wavelet transform (CWT) was proposed to enhance sensitive wavelengths extraction.
Burgeoning global demand for crop products and the negative impact of climate change on crop production are driving the need to improve yield by developing new elite crop varieties without expanding planted area or increasing agronomic inputs. Improvement in photosynthesis is critical for enhancing crop productivity. Even though leaf photosynthesis is well-studied, the photosynthetic potential of non-foliar green tissues like pods in Brassicaceae and Fabaceae species remains underexplored. This review emphasizes pod photosynthesis in determining seed yield and quality in Brassicaceae and Fabaceae crops. At present, accurate and efficient phenotyping methods are unavailable, limiting understanding and genetic improvement of pod photosynthesis. Novel approaches like chlorophyll fluorescence and hyperspectral reflectance are promising for high-throughput phenotyping of pod photosynthetic traits. This review further discusses genetic targets and regulatory mechanisms for enhancing pod photosynthesis, including transcription factors like GOLDEN2-LIKE and GATA that may regulate photosynthetic capacity in pods, suggesting potential genetic manipulation strategies to boost crop productivity. In conclusion, unlocking the genetic and physiological bases of pod photosynthesis offers opportunities for advancing crop breeding to ensure sustainable food security amidst climate change and increasing global population pressures. Future research should focus on developing high-throughput phenotyping tools and elucidating genetic pathways to maximize pod photosynthesis in crops.
Why it matches plant phenotyping methods莢の光合成形質を対象とするレビューであり、蛍光・ハイパースペクトルによる高スループット表現型計測手法を中心的に論じているため。
abstractAt present, accurate and efficient phenotyping methods are unavailable, limiting understanding and genetic improvement of pod photosynthesis.
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.
Why it matches plant phenotyping methodsUAVリモートセンシング、蛍光情報、機械学習、作物モデルを統合した器官レベルの窒素動態推定フレームワークを開発・評価しており、植物状態の取得・推定方法が研究の中心である。
abstractWe 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.
Camellia oleifera, a distinctive and economically vital woody oil species in China, holds significant ecological and economic importance. However, the increasing frequency and intensity of drought events due to global climate change severely threaten its growth and yield stability. This study established controlled drought conditions in a greenhouse environment, and measured Soil and Plant Analysis Development (SPAD) values of two-year-old grafted container-grown seedlings to assess chlorophyll content and photosynthetic potential. Substrate moisture content (Volumetric Water Content, VWC, %), substrate temperature (℃) at upper, middle, and lower container positions, as well as greenhouse air temperature (℃) and relative humidity (RH, %), were monitored. A hybrid deep learning model, Temporal Convolutional Network-Bidirectional Long Short-Term Memory with dual attention mechanisms (TCN-BiLSTM-D2), was developed to predict SPAD values using these environmental variables. Results identified a critical substrate moisture threshold: plant mortality reached 100 % when VWC dropped below 5 %. Substrate temperature exhibited strong positive correlations with air temperature (r = 0.85–0.86) but negative correlations with relative humidity (r = -0.55 to −0.56), while substrate moisture exhibited strong negative correlations with both air temperature and substrate temperature (r = -0.82 to −0.67) and positive correlation with relative humidity (r = 0.30–0.37). SPAD values were significantly correlated with moisture in the middle and lower substrate layers (r = 0.16–0.63). Cultivars CL40 and CL53 exhibited significant negative SPAD responses to rising temperatures (r = -0.36 to −0.06). The model incorporated Feature Focus Attention (FFA) and Multiple Soft Attention (MSA), collectively termed D2, to dynamically weight input features based on their predictive relevance. This enhancement achieved exceptional performance, with a coefficient of determination (R²) of 0.982, Mean Squared Error (MSE) of 0.001, and Mean Absolute Percentage Error (MAPE) of 3.79 %. The TCN-BiLSTM-D2 model substantially outperformed conventional methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Recurrent Neural Network (RNN), and Temporal Convolutional Network (TCN). This framework enables non-destructive, high-throughput phenotypic monitoring and early warning of dynamic environmental stress, providing a robust tool for drought-resistance research in C. oleifera and practical support for the optimization of irrigation, the improvement of cultivation, and drought-tolerant breeding.
Why it matches plant phenotyping methodsSPADという植物生理形質を環境変数から推定する深層学習モデルを開発し、既存モデルとの性能比較・検証を行っており、表現型取得・推定手法が研究の中心である。
abstractA hybrid deep learning model, Temporal Convolutional Network-Bidirectional Long Short-Term Memory with dual attention mechanisms (TCN-BiLSTM-D2), was developed to predict SPAD values using these environmental variables.
Accurate estimation of agroecosystem carbon fluxes is essential for assessing cropland sustainability and climate resilience. This study integrates Leaf Area Index (LAI) retrieval from Radiative Transfer Model (RTM) inversion into AgroC, an agroecosystem model, from Unmanned Aerial System (UAS) platform to enhance carbon fluxes estimates, including Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Total Ecosystem Respiration (TER). By replacing the internally developed LAI in the AgroC model with interpolated LAI time series derived from UAS, improved spatiotemporal representativeness of agroecosystem carbon fluxes is observed under both the Farquhar-von Caemmerer-Berry (FvCB) and the Light Use Efficiency (LUE) photosynthesis approaches. Temporally, the highest GPP accuracy was achieved by the AgroC FvCB model integrated with UAS-derived LAI (RMSE = 3.19 gC m⁻² d⁻¹, KGE = 0.89), while the best NEE estimation was obtained with the AgroC LUE model integrated with UAS-derived LAI (RMSE = 2.10 gC m⁻² d⁻¹, KGE = 0.89). Spatially, the superior performance of the AgroC FvCB model in integrating UAS-derived LAI enabled high-resolution (1 m) mapping of GPP and NEE, effectively capturing within-field spatial variations in a winter wheat field. The daily Pearson correlation coefficient ( r ) overtime ranged from 0.16 in non-vegetated areas to 0.94 in vegetated zones for GPP, and up to 0.88 for NEE. Despite the advantages taking physical basis in RTM inversion for LAI retrieval and biochemical constraints considered in FvCB approach, the limitation in TER improvement requires further investigation to refine RTM-AgroC coupling for cropland carbon fluxes modelling using UAS platforms.
Why it matches plant phenotyping methodsUAS画像からRTM逆解析でLAIという植物群落形質を推定し、その時系列をモデルへ統合・評価するワークフローが主要な技術的要素であるため、植物フェノタイピング手法の実質的応用として含める。
abstractThis study integrates Leaf Area Index (LAI) retrieval from Radiative Transfer Model (RTM) inversion into AgroC, an agroecosystem model, from Unmanned Aerial System (UAS) platform
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The 3D heterogeneity in nitrogen content and temperature within the canopy affects canopy photosynthesis. Currently, there are no methods for efficiently assessing the heterogeneous 3D-distribution of leaf nitrogen content and leaf temperature and integrating that information into a 3D model of canopy photosynthesis. We therefore developed a high-throughput pipeline for collecting canopy photosynthesis parameters in maize (Zea mays) by combining several innovations. First, we used readily obtained SPAD502Plus meter readings to infer local leaf nitrogen content. Second, a Bayesian inference method allowed us to parameterize a C4 leaf photosynthesis model. Third, we used neural radiance fields (NeRFs) to recreate 3D plant architecture and SPAD distribution. Finally, we developed an indoor ray tracing and energy balance model to estimate local light distribution and leaf temperature within a canopy. SPAD values showed a distinct 3D pattern, suggesting within-canopy variation in photosynthesis. Bayesian inference efficiently parameterized the C4 leaf photosynthesis model, with estimated parameter values correlating well with SPAD values. In addition, NeRF more accurately reconstructed 3D architecture and estimated 3D SPAD distribution than traditional methods. This resulted in calculated leaf temperatures being similar to measured values. Different model assumptions can cause significant differences in simulated canopy photosynthetic rate. Omitting 3D SPAD heterogeneity alone produced a 1% to 8% difference in simulated canopy photosynthetic rate. Ignoring leaf temperature heterogeneity led to a difference in the calculated canopy photosynthetic rate of only 1% to 3% near the optimal temperature, but of up to 38% at 35 °C. This pipeline can be realized by high-throughput phenotyping platforms, making it suitable for exploring genetic differences and optimizing ideotype design for improved canopy photosynthesis.
Why it matches plant phenotyping methodsトウモロコシ群落の葉窒素・温度・光合成関連形質を3Dで取得・推定する高スループットパイプラインを開発しており、表現型取得手法が研究の中心である。
abstractWe therefore developed a high-throughput pipeline for collecting canopy photosynthesis parameters in maize (Zea mays) by combining several innovations.
An autonomous IoT crop phenotyping system has been developed that integrates microclimatic, optical, and soil measurements with low-energy LoRaWAN connectivity. The ESP32-S3 node, lifepower, and periodic surveys (1 hour) ensure long-term operation. The optical module is based on the AS7262/AS7263 Fresnel lens spectrometers; the PHAR metrics are validated relative to the LI-190SB quantum sensor. According to field measurements in 2024. The diurnal profiles match, and the spectral features - PPFD regression model explains 89% of the variance (R2=0.89). The three-block architecture (aboveground/underground/control) is complemented by a modular infrared CO2 gas analyzer and a ToF laser sensor for calculating plant biomass growth and potential prediction of phenophases. It is shown that an inexpensive sensor assembly provides a reproducible assessment of biophysical parameters sufficient for rapid diagnosis of crop heterogeneity and subsequent integration with productivity models. Keywords: PHENOTYPING, INTERNET OF THINGS, IoT, AGROECOLOGICAL MONITORING, PRECISION AGRICULTURE, CROP HETEROGENEITY, REMOTE SENSING, LORAWAN, PAR
Why it matches plant phenotyping methods植物の生育・バイオマス・フェノフェーズ等を取得するIoTセンシング基盤の開発と、量子センサーとの検証が中心である。
abstractAn autonomous IoT crop phenotyping system has been developed that integrates microclimatic, optical, and soil measurements with low-energy LoRaWAN connectivity.
We introduce a novel model for characterizing the CO₂-response curve in photosynthesis, addressing the limitations of the Farquhar-von Caemmerer-Berry (FvCB) model by providing a more comprehensive framework for understanding photosynthetic responses to varying CO 2 concentrations in C 3 plants. The FvCB model, while instrumental in interpreting the photosynthetic response to CO 2 , does not directly estimate critical parameters such as maximum net photosynthetic rate, transit point from RuBP- to TPU-limited photosynthesis, and the CO 2 compensation point in the presence of day respiration ( R day ). Our new model, referred to as Model I, incorporates these parameters and accounts for the R day , offering a nuanced understanding of plant physiological responses to CO₂ concentrations. The research also developed Model II, which does not require an explicit R day , addressing the challenges in measuring R day and providing an alternative for analyzing the CO₂-response curve. Both models were validated against empirical data, demonstrating their effectiveness in studying plant photosynthesis and photorespiration. The study concludes that the new models advance the FvCB model by predicting photosynthetic and photorespiratory responses under varying conditions, which is crucial for agricultural practices and ecosystem management in the context of climate change.
Why it matches plant phenotyping methodsC3植物の光合成・光呼吸応答曲線から生理形質を推定する新規モデルを開発し、実測データで検証しており、表現型取得・推定法が研究の中心である。
abstractWe introduce a novel model for characterizing the CO₂-response curve in photosynthesis
Improving light-use efficiency (LUE) is essential for boosting crop productivity, particularly in controlled-environment agriculture. Despite recent advances, most studies still rely on destructive measurements or one-dimensional data, which limits insight into the structural–physiological coordination underlying LUE. We established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.). Integrating hyperspectral imaging with multiview three-dimensional (3D) reconstruction, we developed a noninvasive, high-throughput system that simultaneously estimates 3D plant architecture, photosynthetic physiology—net photosynthetic rate (A) and relative chlorophyll content (SPAD)—and aboveground biomass (AGB) across 35 cultivars. A modeling pipeline combining StandardScaler (SS) normalization, genetic algorithm (GA) feature selection, and artificial neural networks (ANN) achieved robust prediction of A (R²=0.72), SPAD (R²=0.87), and AGB (R²=0.85). Spectral contribution analysis revealed distinct sensitivities: SPAD across 400–700 nm, A near 430 and 680 nm, and AGB across 500–580 nm. The 426–430 nm blue band emerged as a key region: high-efficiency cultivars showed distinctive reflectance (42.93–59.03 %), consistent with superior photosynthetic performance. Structurally, high-efficiency types exhibited “large-and-loose” canopies, with greater plant height (+64.37 %), projected area (+59.42 %), and convex-hull volume (+166.3 %), alongside reduced compactness (−23.48 %). Network analysis indicated progressively tighter coupling between spectral and structural traits from low- to high-efficiency groups, consistent with adaptive coordination for light capture and use. These results identify actionable phenotypic markers for selecting high-LUE cultivars and provide a transferable platform for phenomics-driven breeding and management in controlled-environment crops. • A multimodal framework enables non-destructive, high-throughput phenotyping in lettuce. • 66 key spectral and structural features linked to light-use efficiency were identified. • A photosynthetic trait network reveals coordination of pigments and canopy architecture. • Breeding targets for blue-light response and canopy structure optimization are proposed.
Why it matches plant phenotyping methodsレタスの構造・生理形質を推定するマルチモーダル表現型プラットフォームを開発し、非破壊・高速測定と予測性能を評価しており、表現型取得手法が研究の中心である。
abstractWe established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.).
High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit light capture and overall yield potential. In response, some maize plants naturally reorient their canopies to optimize light capture, a process known as canopy reorientation. Understanding this adaptive response and its impact on light capture is crucial for maximizing agricultural yield potential. This study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown Zea mays L. with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception. Using 3D point clouds derived from field data, virtual fields for a diverse set of maize genotypes were constructed and validated against field PAR measurements across ten inbreds (R 2 = 0.79). Results show that off-row-parallel leaf orientations intercepted on average ≈22% more PAR than on-row-parallel and ≈14% more than random orientations at 30 in. row spacing. We further present detailed analyses of the impact of canopy orientations, plant and row spacings, and planting row directions on PAR interception throughout a typical growing season. By elucidating the relationship between canopy architecture and light interception, this study offers valuable guidance for optimizing maize breeding and cultivation strategies across diverse agricultural settings. • Virtual measurement framework for photosynthetically active radiation (PAR) of field maize. • Framework validated with actual field measurements. • Explored PAR interception under varying maize leaf azimuth angles and canopy reorientation. • Analyzed the impact of planting row directions on PAR interception. • Investigated the effects of planting densities on PAR interception.
Why it matches plant phenotyping methods圃場3D再構成とPARモデルを統合した植物光 interception の仮想測定フレームワークを開発・実測検証しており、方法が研究の中心である。
abstractThis study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown Zea mays L. with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception.
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.
Why it matches plant phenotyping methodsBOSSEを用いてリモートセンシングによる植物形質・機能多様性推定手法をシミュレーションベンチマークし、検証・比較する研究であり、植物フェノタイピング手法が中心である。
abstractThe Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies.
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 match · UnverifiedEurope PMC · checked 15 Sept 2026
Published20 Oct 2025Molecular breeding : new strategies in plant improvementCited by 0 · OpenAlex ↗
Photosynthesis is the cornerstone of life on earth. Precisely evaluating the photosynthetic rate is critical to uncovering its underlying mechanisms. Numerous methods and tools have been developed to assess plant photosynthetic rate. However, these methods typically measure the carbon assimilation rate indirectly during photosynthesis, highlighting the need for a new method to determine plant photosynthetic rate precisely. This study developed an easy and reliable method using 13 C isotope-labeled CO 2 and isotope element spectrometer technology to measure plant photosynthetic rate (C13MP). C13MP can precisely quantify the abundance of assimilated carbon by photosynthesis in plants. C13MP can also evaluate the carbon flow from leaves to the other organs by tracking the 13 C isotope. The photosynthetic rates determined with C13MP were significantly correlated with maize biomass-related and yield-related phenotypes, suggesting the reliability of this method. In conclusion, C13MP can easily, precisely, and reliably evaluate carbon assimilation in various parts of the plant, providing a new method for measuring plant photosynthetic rate. Supplementary information The online version contains supplementary material available at 10.1007/s11032-025-01601-0.
Why it matches plant phenotyping methods植物の光合成速度と炭素移行を定量する新規測定法を開発し、作物表現型との相関で信頼性を検証しており、フェノタイピング手法が中心である。
abstractThis study developed an easy and reliable method using 13 C isotope-labeled CO 2 and isotope element spectrometer technology to measure plant photosynthetic rate (C13MP).
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
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Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Plant phenotyping relevance match · UnverifiedEurope 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.
Why it matches plant phenotyping methods植物の光合成機能と感染症状の早期検出を目的に、クロロフィルa蛍光を植物状態の測定・評価手法として中心的に検討しているため。
abstractThis 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.
Canopy photosynthesis, rather than leaf photosynthesis is highly related to plant biomass and yield formation. Studying canopy photosynthesis and identifying parameters that control it can help optimize agricultural management and realize crop yield potential. Compared with traditional parameters, canopy occupation volume (COV) offers an integrative parameter on canopy architecture related to canopy photosynthetic rates. In this study, we developed a high throughput method to derive COV for different rice cultivars. We first used multi-perspective two-dimensional imaging to perform three-dimensional point cloud reconstruction of rice plants, and developed a suite of pipelines to calculate plant height, leaf count, tiller count, and biomass, with R 2 values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further employed point cloud data to reconstruct the surface of rice plants and construct a virtual canopy model of the rice population. Light distribution was simulated using a ray tracing algorithm, followed by calculation of simulated canopy photosynthetic rates via photosynthetic rate (A)-incident light intensity (Q) curve fitting. Furthermore, we systematically explored the relationships between canopy phenotypes and photosynthetic rates, and found that COV was the most effective predictor of canopy photosynthesis, achieving an R 2 value of 92.1%. Adjusting atmospheric transmittance showed that COV strongly correlates with canopy photosynthesis under different light conditions, with higher accuracy observed under diffuse light. Varying planting density confirmed that this correlation remains strong at the community level. In summary, this study demonstrates that COV is closely linked to simulated canopy photosynthesis and that the developed pipeline can support future agronomic and breeding research.
Why it matches plant phenotyping methodsイネの多視点画像から3D点群を再構成し、COVや複数の植物形質を高スループットに推定するパイプラインを開発しており、表現型取得・抽出法が研究の中心である。
abstractIn this study, we developed a high throughput method to derive COV for different rice cultivars.
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物の画像ベース表現型を含むマルチオミクス統合と機械学習解析を自動化するツールを開発・適用しており、表現型解析ワークフローが中心的です。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
The fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is a crucial indicator of photosynthetic characteristics in crop growth monitoring and yield estimation. Traditional vegetation indices (VIs) struggle to achieve accurate estimations throughout the entire crop growth period due to canopy structural changes, particularly during the senescence phase. This study explores a novel VI, the Normalized Dynamically Adjusted Vegetation Index (NDAVI), for estimating green fAPAR (fAPAR green ) throughout the rice growth cycle, including periods of structural changes when senescent leaves are present in the canopy. A two-year replicated field experiment was designed, incorporating different nitrogen treatments and rice cultivars with varying plant architectures. Unmanned Aerial Vehicle (UAV) remote sensing technology was employed to investigate the effectiveness of VIs in estimating rice fAPAR green throughout the entire growth period. Results indicate that while traditional VIs show some correlation with fAPAR green across the whole growth period, the overall data distribution is relatively dispersed. The proposed NDAVI indirectly considered the relative changes in canopy chlorophyll concentration, effectively mitigating the impact of canopy senescence on fAPAR green estimation. In the two-year rice experiment, NDAVI demonstrated a significant linear relationship and goodness of fit with fAPAR green (R 2 > 0.88). Furthermore, the NDAVI-based prediction model exhibited robust performance in inter-annual validation (R 2 > 0.85, RMSE <0.06). This study integrates remote sensing absorption coefficients with vegetation indices to establish a novel index that accounts for crop absorption characteristics. The proposed method enables accurate estimation of crop fAPARgreen even in canopies with prominent senescent leaves.
Why it matches plant phenotyping methodsUAVリモートセンシングと新規NDAVIにより、イネのfAPARを推定する方法を開発し、交差年検証まで実施しており、植物形質取得が研究の中心です。
abstractThis study explores a novel VI, the Normalized Dynamically Adjusted Vegetation Index (NDAVI), for estimating green fAPAR (fAPAR green ) throughout the rice growth cycle
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.
Why it matches plant phenotyping methods除草剤処理を題材にしているが、時間分解クロロフィル蛍光イメージングによる植物ストレス状態の空間的・早期評価が研究の中心であり、植物フェノタイピング手法の実質的な適用に該当する。
abstractthe 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.
Boron is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. This study evaluated the potential of leaf spectroscopy combined with machine learning to identify boron-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under boron treatments ranging from 0.5 to 8 ppm, measuring leaf boron accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance. The results revealed substantial genotypic variation in boron exclusion, with some genotypes maintaining low leaf boron concentration despite high substrate concentrations. Classification models (partial least squares discriminant analysis and random forest classification) outperformed regression models (partial least squares regression and random forest regression) in distinguishing boron-excluding genotypes, achieving 68 % to 79 % accuracy within just eight days after stress initiation. Reflectance-based vegetation indices such as the Normalized Difference Vegetation Index, Photochemical Reflectance Index, Structure Insensitive Pigment Index, and Chlorophyll Index indicated that boron stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf boron concentration proved more challenging, simulations showed that even modest prediction accuracies (~60 %) can substantially boost genetic gains if larger populations are screened and selection intensities are increased. These findings underscore the value of leaf spectroscopy for high-throughput phenotyping, allowing breeders to rapidly identify and advance boron-tolerant rootstocks.
Why it matches plant phenotyping methods葉分光と機械学習を用いた耐性根株の迅速な表現型推定・選抜が中心であり、反射スペクトルからホウ素耐性や関連生理形質を高スループットに評価する方法を実質的に適用・検証している。
abstractThis study evaluated the potential of leaf spectroscopy combined with machine learning to identify boron-tolerant rootstocks rapidly and cost-effectively.
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods高スループット画像表現型データセットが中心で、画像から農業形質を抽出したデータ、処理済み画像、解析スクリプトを提供しているため。
titleA high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
High-throughput plant phenotyping (HTPP) technologies are rapidly transforming plant science by enabling real-time, non-invasive, and large-scale monitoring of complex morphological, physiological, and biochemical traits. However, existing platforms often lack integration across sensing modalities and analytical depth necessary for early and comprehensive phenotypic trait analysis. In this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants. RGB imaging facilitated detailed morphological analysis by extracting color-based traits, quantifying plant height and canopy area, and accurately distinguishing growth stages. SWIR hyperspectral imaging (HSI) enabled non-invasive biochemical assessment by detecting drought-responsive compounds, such as flavonoids, phenolics, and antioxidant activities, while also supporting the classification of stress severity. This spectral profiling revealed key biochemical alterations triggered by water deficit. MSFI liquid crystal tunable filter (LCTF-based) measured chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (t-Chl) levels, providing critical insights into photosynthetic performance under drought stress. Thermal imaging further enhanced drought assessment by capturing canopy temperature variations, which were used to derive thermal indices for indirect estimation of soil volumetric water content (SVWC). By integrating complementary imaging modalities, the proposed system captured comprehensive phenotypic responses with high predictive accuracy for early detection of drought stress and assessment of plant health. Advanced machine learning (ML) and deep learning (DL) models further enhanced trait extraction and classification, enabling robust analysis of complex, high-dimensional data. This automated, multimodal platform offers scalable, non-invasive crop monitoring, providing precise insights to support drought resilience and precision agriculture.
Why it matches plant phenotyping methods複数の画像・センシングモダリティを統合した自動高スループット植物表現型解析システムを開発し、形態・生理・生化学的形質および乾燥ストレスを抽出することが研究の中心である。
abstractIn this study, we developed a fully automated, multimodal HTPP system combining RGB, shortwave infrared (SWIR) hyperspectral, multispectral fluorescence imaging (MSFI), and thermal imaging to characterize drought-stressed watermelon (Citrullus lanatus) plants.
Solar greenhouses are energy-efficient facilities for year-round crop production. To better understand the effects of shading caused by the insulation quilts on the microclimate and canopy light interception, this study systematically investigated summer cucumber production inside an insulated plastic greenhouse in Beijing, China. Monitoring experiments were conducted across the seedling, flowering, fruiting, and maturity stages, and were combined with 3D simulation modeling. Specifically, the radiation transmittance of greenhouse roof was analyzed at different solar altitude angles. A functional-structural plant model (FSPM) was employed to evaluate the spatiotemporal distribution of light within the greenhouse. Shaded areas resulting from the insulation quilts were calculated, and the daily light integral (DLI) on the canopy under various shading patterns was quantified. Results showed that insulation quilt shading reduced indoor temperature by an average of 4.2 °C and increased relative humidity by 7.5 %, but also caused a significant reduction in canopy light availability. The average DLI during the seedling, flowering, fruiting, and maturity stages was merely 9.1, 10.8, 16.8 and 20.8 mol/m²/d, respectively, which was significantly lower than the commonly recommended range of 20–30 mol/m²/d for optimal growth. Considering the combined effects of temperature, humidity and photosynthetic requirements, supplementary lighting at night is necessary to ensure optimal crop development. The modeling framework proposed in this study represents an initial step toward quantitatively evaluating shading effects in greenhouse. This approach is not constrained by geographic location and can be directly applied to other greenhouse types and crop species.
Why it matches plant phenotyping methods温室内のキャノピー光環境・光遮蔽を3Dモデルで定量化する再利用可能な計算手法が中心であり、植物キャノピーの光利用状態を推定しているため。
abstractA functional-structural plant model (FSPM) was employed to evaluate the spatiotemporal distribution of light within the greenhouse.
The application of in-field and aerial spectroscopy to assess functional and phylogenetic variation in plants has led to novel ecological insights and supports global assessments of plant biodiversity. Understanding how plant genetic variation influences reflectance spectra will help harness this potential for biodiversity monitoring and improve understanding of why plants differ in functional responses to environmental change. Here, we use a well-resolved genetic mapping population derived from Multi-parent Advanced Generation Inter-cross (MAGIC) lines of Nicotiana attenuata to associate genetic differences with differences in leaf spectra between plants in a field experiment in their natural environment. We analyzed the leaf reflectance spectra using a hand-held spectroradiometer (350-2500 nm) on 616 fully genotyped plants of N. attenuata grown in a randomized block design. We tested three approaches to conducting Genome-Wide Association Studies on spectral variants. We introduce a new Hierarchical Spectral Clustering with Parallel Analysis (HSC-PA) method. This method efficiently captured the variation in our high-dimensional dataset and allowed us to discover a novel association, between a locus on chromosome 1 and the 734-1143 nm spectral range, spanning the red-edge and near-infrared regions that are sensitive to leaf structure and photosynthetic activity. This locus contains a candidate gene annotated as carbonic anhydrase, an enzyme involved in CO2 hydration and regulation of photosynthetic efficiency, suggesting a physiological link between variation in leaf optical properties and carbon assimilation. In contrast, an approach treating single wavelengths as phenotypes identified the same associations as HSC-PA, but without the statistical power to pinpoint significant associations. An index-based approach, which reduces complex spectra to a few dimensionless variables, detected two significant associations for ARDSI_Cw (a water-content-related index) with loci on chromosome 1 near genes annotated as a Zeta toxin domain-containing protein, and an Exocyst subunit Exo70 family protein. While these findings are biologically plausible, they represent a very narrow subset of the spectral variation captured by HSC-PA. The HSC-PA approach supports a comprehensive understanding of the genetic determinants of leaf spectral variation which is data-driven but human-interpretable, and lays a robust foundation for future research in linking plant genetics with biodiversity monitoring, large-scale ecological assessment and remote-sensing applications.
Why it matches plant phenotyping methods葉の反射スペクトルを植物表現型として取得し、高次元スペクトルを解析する新規HSC-PA手法を導入・評価しており、植物フェノタイピング手法が研究の中心的な技術的貢献である。
abstractWe analyzed the leaf reflectance spectra using a hand-held spectroradiometer (350-2500 nm) on 616 fully genotyped plants of N. attenuata
Field / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldPhysiological trait estimationYield / biomass estimationBiomass / plant weightGrowth / development / phenologyPhotosynthesis / fluorescenceWater status / transpiration
The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO2 agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO2 fluxes and evapotranspiration measured by eddy covariance (for NEE, R2 = 0.57, RMSE = 0.97 gC·m−2; for ETR, R2 = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R2 = 0.71, RMSE = 93.3 g·m−2). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m−2 for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation.
Why it matches plant phenotyping methods衛星リモートセンシングと作物モデルの同化手法を用いて、カバークロップのバイオマス、フェノロジー、発達を推定し、実測データで検証しているため、植物形質の取得・推定が実質的な方法要素となっている。
abstractestimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution.
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.
Why it matches plant phenotyping methods葉の反射スペクトルからPSII熱耐性（Tcrit）を推定・分類する方法を評価しており、植物生理形質の取得・リモート推定が研究の中心である。
abstractHere, 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.
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.
Why it matches plant phenotyping methodsマルベリーの生理・ストレス応答を対象に、ハイスループット表現型解析ツールとプラットフォームを体系的にレビューしており、フェノタイピング手法が中心です。
abstractPhenotyping platforms such as leaf area meters, chlorophyll fluorescence imaging, and portable photosynthetic systems allow rapid assessment of photosynthetic efficiency and stress responses.
Field / plotMultispectral / hyperspectralPhysiological trait estimationPhotosynthesis / fluorescence
Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresponding wavelet coefficients, a relative reflectance is retrieved independently of the fluorescence, i.e. without introducing a coupling between reflectance and fluorescence. The fluorescence can then be derived as the remaining offset. This method can be applied to arbitrary chosen wavelength windows in the whole spectral range, such that all the spectral data available is exploited, including the separation into several frequency (i.e. width of absorption lines) levels and without the need of extensive training datasets. At the same time, the assumptions about the reflectance shape are minimal and no spectral shape assumptions are imposed on the fluorescence, which not only avoids biases arising from wrong or differing fluorescence models across different spatial scales and retrieval methods but also allows for the exploration of this spectral shape for different measurement setups. WAFER is tested on a synthetic dataset as well as several diurnal datasets acquired with a field spectrometer (FloX) over an agricultural site. We compare the WAFER method to two established retrieval methods, namely the improved Fraunhofer line discrimination (iFLD) method and spectral fitting method (SFM) and find a good agreement with the added possibility of exploring the true spectral shape of the offset signal and free choice of the retrieval window. (abbreviated)
Why it matches plant phenotyping methods植物の光合成状態に関連するSIFを分光データから抽出する新手法を開発し、合成・実測データで既存手法と比較検証しており、表現型取得手法が中心である。
abstractIn this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval)
Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and illumination varies, making accurate segmentation and detection challenging. To address these issues, we propose a lightweight banana leaf detection and counting method deployable on embedded devices, which integrates a space–depth-collaborative reasoning strategy with multi-scale feature enhancement to achieve efficient and precise leaf identification and counting. For complex background interference and occlusion, we design a multi-scale attention guided feature enhancement mechanism that employs a Mixed Local Channel Attention (MLCA) module and a Self-Ensembling Attention Mechanism (SEAM) to strengthen local salient feature representation, suppress background noise, and improve discriminability under occlusion. To mitigate feature drift caused by environmental changes, we introduce a task-aware dynamic scale adaptive detection head (DyHead) combined with multi-rate depthwise separable dilated convolutions (DWR_Conv) to enhance multi-scale contextual awareness and adaptive feature recognition. Furthermore, to tackle instance differentiation and counting under occlusion and overlap, we develop a detection-guided space–depth position modeling method that, based on object detection, effectively models the distribution of occluded instances through space–depth feature description, outlier removal, and adaptive clustering analysis. Experimental results demonstrate that our YOLOv8n MDSD model outperforms the baseline by 2.08% in mAP50-95, and achieves a mean absolute error (MAE) of 0.67 and a root mean square error (RMSE) of 1.01 in leaf counting, exhibiting excellent accuracy and robustness for automated banana leaf statistics.
Why it matches plant phenotyping methodsバナナ葉の検出・計数という植物形態・生育指標を対象に、複雑環境での画像解析手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a lightweight banana leaf detection and counting method deployable on embedded devices
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Hyperspectral indices integrated with physiology predicted metabolites such as Rubisco activity across early, mid, and late flowering drought, establishing a rapid, non-destructive framework to detect sink limitations and identify cotton resilience to stage-specific stress and fiber quality decline.
Why it matches plant phenotyping methods綿花のスペクトル表現型と生理形質を機械学習で統合し、乾燥ストレス耐性や品質低下を非破壊・迅速に推定する枠組みが中心であるため。
abstractHyperspectral indices integrated with physiology predicted metabolites such as Rubisco activity
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methodsリモートセンシング・高スループット表現型解析プラットフォームを中心に、作物のストレス関連形質測定とマルチオミクス統合をレビューしており、フェノタイピング手法が中核である。
titleMulti-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought–Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture
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.
Why it matches plant phenotyping methods時間・スペクトル分解クロロフィル蛍光から新規パラメータと解析パイプラインを開発し、光合成エネルギー分配と光阻害状態を推定する手法が研究の中心である。
abstractWe 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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methods植物ストレスの非破壊検出手法を、リモートセンシング、画像計測、MLとともに体系的にレビューしており、植物状態の取得・推定方法が中心である。
titleA comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches
Accurate and efficient assessment of single-leaf photosynthetic rate (AL) is essential for applications in crop yield assessment, stress perception, and biological breeding. However, existing AL prediction methods typically require extensive training data and often rely on point estimation, which poses challenges for few-shot learning and high-reliability predictions. To alleviate this dilemma, a comprehensive AL estimation model was developed in this study. Using the combination of meta-learning (ML) and multilayer perceptron (MLP), the model initially constructed a base prediction model and then fine-tuned it for unseen tasks. Compared to the current popular methods, the proposed model achieved higher prediction accuracy with fewer training samples. Introducing only about 5 % of new samples from the dataset was sufficient to achieve satisfactory predictive performance. Additionally, this study introduced quantile regression (QR) method to obtain the 95 % confidence interval of AL, mitigating the limitations of high reliability in predictions. Finally, by employing Gaussian kernel density estimation (GKDE) to derive the probability density under each environmental condition, we constructed an AL prediction model with reliable uncertainty estimation capabilities. Through detailed validation using multiple datasets from various species and interpretability analysis, the proposed method has proven to be universally applicable and reasonable. This research highlights the high generalization ability of ML-MLP for unseen datasets and extends AL predictions from point estimation to interval prediction by QR-GKDE, thereby facilitating the researches on crop cultivation. This research significantly enhances precision agriculture by providing robust methodologies for crop monitoring and stress perception.
Why it matches plant phenotyping methods単葉光合成速度という植物生理形質を推定する機械学習モデルを開発し、複数データセットで詳細に検証しているため、フェノタイピング手法が中心である。
abstracta comprehensive AL estimation model was developed in this study
Agro-Photovoltaic (APV) systems optimize land use by integrating agriculture with renewable energy production, offering a sustainable solution to contemporary address the growing global food demand and mitigate climate change impacts . However, the heterogeneous shading conditions inherent to APV systems may affect crop growth and productivity, highlighting the need for innovative and accurate monitoring methods to ensure consistent production levels. In this context, the present study proposed and tested a hybrid approach that combines proximal sensing with a process-based crop model to estimate biomass accumulation within an APV system. Firstly, the performance of two-dimensional (2D) and three-dimensional (3D) imagery in capturing the phenotypic adaptations of alfalfa ( Medicago sativ a L.) over two consecutive growing seasons (i.e., 2023 and 2024) was assessed by empirically modeling fluctuations in the fraction of absorbed photosynthetically active radiation (fAPAR). Although the comparative analysis always revealed a strong correlation between observed and image-derived fAPAR values (R² = 0.74), the 2D-based models demonstrated higher accuracy than the 3D approach (RMSE = 0.07 vs 0.09, rRMSE = 10.83 % vs 17.16 %, AIC = −44.91 vs −56.27, respectively). Consequently, the daily fAPAR estimates derived from 2D data were used to force the SSM-iCrop2 model, providing accurate predictions of alfalfa biomass accumulation both in 2023 (R² = 0.96, RMSE = 36.26 g m⁻², rRMSE = 21.35 %, AIC = 151.37) and 2024 (R² = 0.88, RMSE = 46.98 g m⁻², rRMSE = 30.55 %, AIC = 129.07) when compared with field survey data. These findings demonstrate how the proposed image-driven modeling approach can be used as a potential tool for monitoring plant adaptation to varying shading conditions in APV systems. Moreover, its proven ability to predict crop yield with high spatio-temporal accuracy could inform mowing management strategies, optimizing production efficiency while aligning with sustainable agricultural and energy goals.
Why it matches plant phenotyping methods画像からfAPARを推定し、2D・3D手法を比較検証してアルファルファのバイオマスを予測する方法が研究の中心である。
abstractthe present study proposed and tested a hybrid approach that combines proximal sensing with a process-based crop model to estimate biomass accumulation within an APV system.
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.
Why it matches plant phenotyping methods植物のスペクトル画像から葉の生理状態・温度指標を抽出し、機械学習で除草剤作用機構を診断する手法の開発と精度検証が研究の中心であるため、植物フェノタイピング手法として採用。
abstractthis study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).
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.
Why it matches plant phenotyping methods非定常条件での蛍光法による葉肉コンダクタンス測定を同位体法と検証し、ガス交換と組み合わせて光合成誘導形質を評価しており、測定法の検証が中心的です。
abstractHere, 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.
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹キャノピー向けの3D再構成アルゴリズム開発が中心的に記述され、構造・光合成形質の取得に用いられているため。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Drought priming represents a potential strategy to bolster wheat yields in the face of recurring droughts, and there is a need to identify responsive cultivars and decipher the underlying mechanisms of priming. Here, the responses of 157 wheat cultivars to drought-priming were phenotyped using a high-throughput phenotyping (HTP) platform across two growing seasons, and a drought priming index (DPI) was devised to assess the priming sensitivity for each cultivar. A DPI comprehensive score (DPICS) was derived from 13 sensitive traits identified by principal component analysis, and significant variations in this score led to the classification of the cultivars into two distinct groups, one sensitive to drought priming and one not. The sensitive group contained 58 cultivars that had higher DPI values for traits including yield components, harvest index, post-anthesis assimilation, photochemical efficiency, canopy coverage, and normalized difference vegetation index, and lower DPI values for traits including remobilization of dry matter stored pre-anthesis, non-photochemical quenching, plant senescence reflectance index, and canopy temperature. A genome-wide association study (GWAS) based on the DPI identified 499 significant markers related to drought priming using a commercially Wheat660 SNP array. Notably, one marker situated on chromosome 5B consistently appeared in both the growing seasons that were studied. This marker resides within a 261.2 kb genomic block containing seven genes, including the candidate gene TraesCS5B03G1259700, which exhibited distinct transcriptional memory related to drought priming. Our results suggest that integrating HTP and GWAS has great potential for deciphering the genetic basis of acquired drought tolerance induced by priming and could facilitate the breeding of improved wheat varieties that can respond to recurring drought events.
Why it matches plant phenotyping methodsHTPプラットフォームを用いた多形質の取得と、乾燥プライミング感受性指標（DPI）の開発・適用が研究の中心であり、植物表現型解析手法の実質的な応用に該当する。
abstractthe responses of 157 wheat cultivars to drought-priming were phenotyped using a high-throughput phenotyping (HTP) platform across two growing seasons
Introduction Atmospheric CO 2 elevation significantly impacts plant carbon metabolism, yet accurate quantification of respiratory parameters-photorespiration rate (R p ) and mitochondrial respiration rate in the light (R d )-under varying CO 2 remains challenging. Current CO 2 -response models exhibit limitations in estimating these parameters, hindering predictions of crop responses under future climate scenarios. Methods Low-oxygen treatments and gas exchange measurements, calculating CO 2 recovery/inhibition ratio in of wheat ( Triticum aestivum L. ) and bean ( Glycine max L. ) were employed to elucidate the biological significance and interrelationships of R p and R d . Model-derived estimates of R p and R d were compared with measured values to assess the accuracy of three CO 2 -response models (biochemical, rectangular hyperbola, modified rectangular hyperbola). Furthermore, the effects of ambient CO 2 concentration (0~1200 μmol·mol -1 ) on the measured R p and R d were quantified through polynomial regression. Results The A/C a model achieved superior fitting performance over the A/Ci model. However, significant disparities persisted between A/Ca-derived R p /R d estimates and measurements ( p 2 concentration exhibited dose-dependent regulation of respiratory fluxes: R p-measured ranged from 4.923 ± 0.171 to 12.307 ± 1.033 μmol (CO 2 ) m -2 s -1 (wheat) and 4.686 ± 0.274 to 11.673 ± 2.054 μmol (CO 2 ) m -2 s ⁻ ¹ (bean), while R d-measured varied from 0.618 ± 0.131 to 3.021 ± 0.063 μmol (CO 2 ) m -2 s -1 (wheat) and 0.492 ± 0.069 to 2.323 ± 0.312 μmol (CO 2 ) m -2 s -1 (bean). Polynomial regression revealed strong non-linear correlations between CO 2 concentrations and respiratory parameters (R ² > 0.891, p p- C a : R ² = 0.797). Species-specific CO 2 thresholds governed peak R p (600 μmol·mol -1 for wheat vs. 1,000 μmol·mol -1 for bean) and R d (400 μmol·mol -1 for wheat vs. 200 μmol·mol -1 for bean). Discussion These findings expose critical limitations in current respiratory parameter quantification methods and challenge linear assumptions of CO 2 -respiration relationships. They establish a critical framework for refining photosynthetic models by incorporating CO 2 -responsive respiratory mechanisms. The identified non-linear regulatory patterns and model limitations provide actionable insights for advancing carbon metabolism theory and optimizing crop carbon assimilation strategies under rising atmospheric CO 2 , with implications for climate-resilient agricultural practices.
Why it matches plant phenotyping methodsCO₂応答モデルによる光呼吸・明所ミトコンドリア呼吸の推定精度を実測ガス交換値と比較検証しており、植物生理状態の取得・定量法が研究の中心です。
abstractModel-derived estimates of R p and R d were compared with measured values to assess the accuracy of three CO 2 -response models
Abstract Leaf Chlorophyll Concentration (LCC) is a vital biochemical parameter for assessing plant status due to its essential role in physiological activities, photosynthesis, and overall plant health. In order to illustrate the development of potato crops and offer advice for precision agriculture management, research was conducted on non-invasive testing methods for chlorophyll levels and methods for mapping crop yield in potatoes. The objective of this study is to examine the spatial distribution of chlorophyll content and yield of potato crops using Sentinel 2 data, SPAD chlorophyll measurements, and laboratory analyses. Artificial intelligence (AI) using the Random forest (RF) classification method was used to study the spatial distribution of crop type and discriminate the potato crop. The overall accuracy and kappa statistics for the spatial distribution derived from Sentinel 2 satellite imagery for potato crops in the study area were 0.79 and 82.5%, respectively. Stepwise Multilinear regression model (SWMLR) between Spectral vegetation indices (Normalized Difference Vegetation Indexed NDVI, Modified Chlorophyll Absorption Ratio Index (MCARI), Leaf Chlorophyll Index (LCI), derived from spectral vegetation indices (SVI), (SPAD chlorophyll and chemical analysis through potato crop growth stages (S1, S2 and S3) were correlated to estimate chlorophyll content and crop yield map. The model accuracy between vegetation indices and Total chlorophyll showed that models based on VIS and selected spectral bands derived from ASD to predict total chlorophyll(chlt) and SPAD chlorophyll values achieved a high coefficient of determination (R 2 ) at the different growth stages, which were 0.983 and 0.986. The produced map for the potato crop, total chlorophyll derived from Sentinel 2, showed high accuracy at 0.966 and 0.974 based on SPAD, VIS, and selected spectral bands, respectively. The study showed that the estimation and mapping of Chlt and SPAD values of a potato crop under an irrigation system pivot can be done with the help of RS and AI techniques.
Why it matches plant phenotyping methodsSentinel-2、近接センサー、SPAD、分光データとAI・回帰モデルを用いて、ジャガイモのクロロフィル量と収量を推定・マッピングする技術的評価が研究の中心であり、植物形質の取得・推定方法を扱っている。
abstractresearch was conducted on non-invasive testing methods for chlorophyll levels and methods for mapping crop yield in potatoes
ABSTRACT The leaf area index (LAI) is a vital parameter in crop eco‐physiology because it substantially influences key processes such as evapotranspiration, light interception, photosynthesis and ultimately seed yield. Together with plant height, the LAI serves as a crucial indicator of crop growth and yield potential. Understanding the dynamic variations in LAI and developing reliable predictive models are essential for advancing research and improving agricultural practices. In this study, we employed a simple, experimentally validated logistic model driven by growing degree days (GDDs) to predict the LAI of fodder maize cultivated under both pulse and continuous irrigation regimes in the dry and semiarid regions of Varamin, Iran. Additionally, we propose a novel logistic equation that permits LAI estimation across different irrigation treatments (60%, 80% and 100% water requirements) independent of GDD and plant height. Evaluations via the R 2 , RMSE and NSE indices demonstrated high accuracy in LAI estimation throughout the entire growth period, with the logistic model consistently outperforming the Gaussian model. Our results highlight the usefulness of LAI modelling for monitoring crop development and devising effective management strategies while emphasizing the importance of integrating advanced modelling techniques into agricultural management, especially in water‐scarce regions. These findings offer promising insights.
Why it matches plant phenotyping methodsLAIという植物形質を推定するロジスティックモデルを開発・検証し、他モデルとの性能比較も行っており、形質推定手法が研究の中心である。
abstractwe employed a simple, experimentally validated logistic model driven by growing degree days (GDDs) to predict the LAI
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.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、形態・生理測定および自動ハイスループット画像解析プラットフォームを体系的にレビューしているため。
abstractThis narrative review synthesizes current approaches that span traditional morphology, developmental staging, and precision measurement of vegetative and reproductive traits.
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.
Why it matches plant phenotyping methodsOJIP蛍光法による植物・藻類・シアノバクテリアのPQ-redox推定を中心に、複数蛍光計の系統比較、測定条件、感度・適用範囲を評価しており、植物の生理状態を取得する方法の検証研究である。
abstractHere, 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).
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.
Why it matches plant phenotyping methodsイネの塩ストレス回復性をスクリーニングするプロトコルを開発・最適化し、高スループット画像ベース表現型解析を中核的に用いているため。
abstractTo 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.
Rapid and accurate monitoring of photosynthetic indicator is of great significance for understanding crop growth and development, and predicting yield. Hyperspectral imagery has become a powerful tool for evaluating photosynthetic capacity due to its non-destructive nature in sensing crop radiation. Most photosynthetic indicators have instantaneous ideal values, which cannot fully reflect the photosynthetic capacity of crop populations in field environments. This study introduces a novel indicator “one day photosynthesis” (ODP) based on the various photosynthetic indicators including net photosynthetic rate (Pn), stomatal conductance (Gs), internal CO₂ concentration (Ci), and transpiration rate (Tr). We performed trend fitting on the time-series photosynthetic indicators obtained at a frequency of two hours, and then calculated the projection area of the fitting curve on the time axis. Later on, the ODP was calculated by assigning weight to the projection area using the CRITIC and correlation method, and the feasibility of ODP was tested using the growth of hundred-grain weight (HGW). Finally, we constructed the ODP estimation model based on canopy hyperspectral data, and further estimated the yield. The results showed that the correlation coefficients between ODP and the growth of HGW were 0.831, 0.882, 0.856, and 0.833 at 10, 20, 30, and 40 days after flowering, respectively. The R² of the ODP estimation model based on hyperspectral vegetation indices (VIs) in the four growth stages were 0.71, 0.83, 0.79, and 0.75, respectively. Moreover, ODP also showed high accuracy and adaptability in different sites, years, sowing dates, and cultivars. We noticed that ODP also has good accuracy in estimating the maize yield, as the R² of estimated yield on the base of measured and estimated ODP was 0.770 and 0.716 respectively. Furthermore, the VIs screened by ODP modeling can also be used for yield estimation, and this VIs screening method is superior to the yield estimation model built based on the correlation between VIs and yield. This study findings provides a novel insight regarding the new ODP indicator that has potential application prospects for efficient estimation of maize photosynthetic capacity and yield.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から新規指標ODPを推定し、光合成能力と収量を定量化する手法の開発・検証が研究の中心である。
abstractThis study introduces a novel indicator “one day photosynthesis” (ODP)
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.
Why it matches plant phenotyping methods作物のキャノピー分光情報（SIF、反射スペクトル）やLAI・収量などの植物状態を機械論的に推定するモデルを開発し、観測データで較正・検証しており、表現型推定手法が中心である。
abstractwe 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
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.
Why it matches plant phenotyping methods植物病害の症状・生理状態を画像およびセンサーで検出する手法を中心に扱う包括的レビューであり、植物フェノタイピング手法のレビューに該当する。
abstractThis paper provides a comprehensive review of advancements in plant disease detection, moving from traditional to modern and AI-driven approaches.
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.
Why it matches plant phenotyping methodsRGB画像指標から植物の乾燥ストレス応答を推定・分類する非破壊的手法を開発し、交差検証で性能評価しているため、表現型取得・推定法が中心である。
abstractThis 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.
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型別の光合成パラメータを推定するニューラルネットワーク手法であり、植物生理形質の抽出が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper deposits its maize gas exchange phenotype measurements (Zenodo 15966533), the synthetic neural-network training dataset (Zenodo 15926601), and the C4TUNE analysis/training code with predicted genotype parameters (GitHub pwendering/C4TUNE), all with explicit availability statements and public URLs.Dataset · publicwere tuned as described above (“Surrogate
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model”). The final model was trained for 30 epochs with a batch size of 8.
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The neural networks were implemented using Python 3.10.14 using the PyTorch library version
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Data availability
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The gas exchange measurements for maize genotypes are available at
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https://doi.org/10.5281/zenodo.15966533. Part of these data has been used in another study
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linking photosynthesis-related traits and hyperspectral reflectance data 43
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artificial data set for neural network training is available at
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https://doi.org/10.5281/zenodo.15926601.657
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CC-BY-NC-ND 4.0 International license
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(whOpen asset ↗zenodo · 10.5281/zenodo.15966533pdf-raw-page:21 lines:1-94Code · public22
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Custom code for the generation of the artificial dataset as well as code for neural model
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definition and training are available at https://github.com/pwendering/C4TUNE. This
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repository also contains the predicted parameters for the maize genotypes.
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References
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1. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater
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yield. Annu. Rev. Plant Biol. 61, 235–261 (2010).
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2. Croce, R. et al. Perspectives on improving photosynthesis to increase crop yOpen asset ↗github · pwendering/C4TUNEpdf-raw-page:22 lines:1-69Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワークの開発が研究の中心であり、植物生理形質の推定手法として適格。
abstractHere we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes
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 · publicAll codes and data to ensure reproducibility of
the results can be accessed at: https://github.com/Rudan-X/KineticGPOpen asset ↗GitHub · Rudan-X/KineticGPpdf-page:19 lines:1-43Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methodsセンサーを用いた非破壊フェノタイピングと灌漑勾配プラットフォームを検証し、センサー指標と植物形質・収量を比較しているため、方法が中心的である。
abstractefficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors.
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.
Why it matches plant phenotyping methods既存の生理・色彩形質を用いてストレス種を識別する解析手法を再評価し、特徴量の比較と混同行列による性能検証を行っており、フェノタイピング手法の評価が中心である。
abstractThis paper presents a reanalysis of physiological and phenotyping data from drought and salt stress experiments
Plant phenotyping relevance match · UnverifiedCrossref · 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.
Why it matches plant phenotyping methods植物の光合成・クロロフィル蛍光・ストレスなどを hyperspectral imaging で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法の方法論的レビューに該当する。
abstractThis review explores HSI's role in agriculture, particularly its integration with unmanned aerial vehicles, AI-driven analytics, and machine learning.
Abstract. Current climate change is largely due to the continuing increase in the anthropogenic greenhouse effect, with major environmental repercussions, especially in agriculture. The increase of global warming, salinity of water resources and frequency of extreme weather events has devastating consequences on the primary sector, in particular on the photosynthetic activity of crops and, therefore, their agricultural yield. The current climate crisis, in fact, leads to an increase in water requirements, the proliferation of weeds, and the depletion of nutrients in the soil, necessitating the massive use of fertilisers, herbicides and pesticides, which, in turn, trigger substantial alterations in ecosystem balances. In response to these critical issues, precision agriculture (PA) constitutes a data-driven approach based on the interpretation of multispectral and thermal datasets obtained by different remote sensing techniques and the use of latest-generation sensors to recognise the state of health of crops and, therefore, optimise agricultural production with a more rational and sustainable management of resources.This paper presents the results of a survey campaign carried out in October 2023 on two citrus fields located in south-eastern Sicily (Italy) to highlight the health status of crops just before the harvesting period. By using multispectral and thermal sensors installed on a drone, different vegetation indices have been calculated to identify, in each field, the areas with the highest photosynthetic activity and the zones characterised by a lack of water or other nutrients, on which targeted agronomic interventions should be planned as a priority.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像を用いて、柑橘作物の健康状態、光合成活性、水ストレスを推定するセンシング手法の適用が中心であり、単なるルーチン測定ではない。
titleestimating crop health and water stress by comparing UAV Multispectral and Thermal Imagery
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.
Why it matches plant phenotyping methodsトウモロコシのハイパースペクトル反射データによる形質推定について、複数の機械学習モデル、未知遺伝子型・季節への汎化性能、データ統合の影響を系統的かつネスト化交差検証で評価しており、フェノタイピング手法の検証が中心である。
abstractWe 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.
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 match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
Why it matches plant phenotyping methods作物キャノピーのSIFを葉・光合成系レベルへ推定するハイブリッド逆解析法を開発し、独立測定との比較で検証しているため、植物生理表現型の取得手法が中心である。
abstractTo 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.
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.
Why it matches plant phenotyping methods葉注入接種法とクロロフィル蛍光による早期病害・抵抗性評価を開発、最適化し、既存接種法との相関で検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, a simple and efficient leaf injection method was established and optimized by evaluating key factors, such as seedling age and inoculum concentration.
Why it matches plant phenotyping methods干ばつ耐性を評価するための高スループット水耕アッセイを開発・提示し、植物表現型（バイオマス、光合成、回復）を再現可能なプラットフォームで取得することが中心である。
abstractwe present a rapid, high-throughput hydroponic assay designed as an efficient pre-screening tool for evaluating potato cultivars and CRISPR-edited lines.
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.
Why it matches plant phenotyping methods植物の光合成速度という生理形質を、クロロフィル蛍光・環境データから予測するモデルを開発し、比較検証しているため、表現型取得・推定手法が中心です。
abstractwe introduce a novel approach that incorporates chlorophyll fluorescence (ChlF) parameters into a model for predicting Pn across diverse leaf ontogenies
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.
Why it matches plant phenotyping methodsアブラムシ抵抗性という植物状態を対象に、VIS–NIR–SWIR分光センシングと機械学習による識別・生理形質推定を開発的に評価しており、表現型取得法が中心である。
abstractWe examined the potential of spectral features (VIS–NIR–SWIR) to improve the current phenotyping methods in monitoring sorghum resistance mechanisms to SCA.
This study presents an advanced 3D visualization-based intelligent algorithm to assess and enhance Larix kaempferi carbon sequestration under drought stress. This approach addresses the critical impacts of drought on canopy structure and photosynthetic efficiency, significantly reducing carbon gain in larch plantations. Our research utilizes high-precision 3D canopy models combined with detailed physiological data to reveal the negative effects of drought on the cumulative leaf area index (cLAI) and maximum photosynthetic efficiency (Amax). The findings demonstrate that while drought stress reduces overall leaf area, the optimized leaf arrangement and minimized ineffective leaf area enable trees to more efficiently utilize water for photosynthesis, thereby preserving or even enhancing their carbon sequestration capacity. By leveraging 3D reconstruction technology, this study provides real-time, accurate data that significantly improves our understanding of forest ecosystem dynamics under extreme climatic conditions. The intelligent algorithm developed offers a robust tool for predicting and optimizing forest carbon sequestration, presenting new opportunities for forest management and conservation. The application of advanced 3D visualization and intelligent algorithms enhances decision-making processes for forest managers and stakeholders, promoting scientifically sound strategies for climate adaptation. This study underscores the transformative potential of cutting-edge 3D modeling technologies in advancing forest conservation and management practices.
Why it matches plant phenotyping methods3D可視化と知的アルゴリズムによる樹冠モデルから葉面積指数などの植物形質を推定する手法が中心であり、干ばつ下の樹冠構造・光合成状態の評価に実質的に適用している。
abstractThis study presents an advanced 3D visualization-based intelligent algorithm to assess and enhance Larix kaempferi carbon sequestration under drought stress.
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.
Why it matches plant phenotyping methodsハイパースペクトル反射データから多数の植物形質を推定する機械学習モデルを、未観測遺伝子型・季節で系統的に比較・検証しており、形質取得とモデル評価が研究の中心である。
abstractHyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methods高温ストレス下の茶苗について、クロロフィル蛍光画像からFv/Fmを用いてストレス状態を分類・検出するYOLOモデルを開発・改良しており、植物状態の画像ベース表現型取得が中心である。
abstractthis study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification
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.
Why it matches plant phenotyping methods葉の空間的な熱応答を評価するため、クロロフィル蛍光イメージングとNPQ動態による生理形質取得を中心的に実施しており、空間分解フェノタイピングが研究の主要手法である。
abstractUsing chlorophyll fluorescence imaging, we found that non-photochemical quenching (NPQ) kinetics captured additional physiological changes beyond those detected by SPAD
Quantitative descriptions of the complete canopy architecture are essential for accurately evaluating crop photosynthesis and yield performance to guide ideotype design. Although various sensing technologies have been developed for three-dimensional (3D) reconstruction of individual plants and canopies, they failed to obtain an accurate description of canopy architectures due to severe occlusion among complex canopy architectures. We proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model. A complete point cloud generation framework was developed for automated annotation of the training dataset by distinguishing surface points from occluded points within canopies. The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points. To further enhance feature extraction, a dynamic graph convolutional feature extractor (DGCFE) module was proposed to capture structural variations over the whole rapeseed growth period. The results demonstrated that CP-PCN achieved chamfer distance (CD) values of 3.35 cm -4.51 cm over four growth stages, outperforming the state-of-the-art transformer-based method (PoinTr). Ablation studies confirmed the effectiveness of the MRDG and DGCFE modules. Moreover, the validation experiment demonstrated that the silique efficiency index developed from CP-PCN improved the overall accuracy of rapeseed yield prediction by 11.2% compared to that of using incomplete point clouds. The CP-PCN pipeline has the potential to be extended to other crops, significantly advancing the quantitatively analysis of in-field population canopy architectures.
Why it matches plant phenotyping methods作物群落キャノピーの3D形態を復元する点群補完法を開発し、既存法との比較、アブレーション、収量予測への有効性検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractWe proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model.
Reproduction assets foundThe paper's availability statement explicitly deposits all source code and test data (rapeseed canopy point cloud completion, CP-PCN) on GitHub at the allowed URL.Code · publicn Wang, Yi Feng,
Mengjie Gong and Guangyu Wu, for their participation in the experiments, and
to the Jiaxing Academy of Agricultural Sciences for their assistance with the
experimental data acquisition.
Availability of supporting data and source code
All source codes and test data involved in this study are available on
GitHub (https://github.com/Ziyue-Guo/RP-PCN.git).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests
or personal relationships that could have appeared to influence the work
reported in this paper.
Contributions
Z. G. designed the study, conducted the experiments, and wrote the
manuscript. Y. S. contributed to the expeOpen asset ↗Ziyue-Guo/RP-PCNpdf-layout-page:42 lines:1-42Plant phenotyping relevance match · UnverifiedbioRxiv · Crossref · checked 15 Sept 2026
Photosynthesis provides energy and organic substrates to most life. In plants, photosynthesis dominates chloroplast physiology but represents only a fraction of the tightly interconnected metabolic network that spans the entire cell. Here, we explore how photosynthetic activity affects energy physiology within and beyond the chloroplast. We developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology to investigate pH, MgATP2- and NADH/NAD+ dynamics at dark-light transitions in Arabidopsis mesophyll cells. Our findings reveal a stromal alkalinization signature induced by photosynthetic proton pumping, extending to the cytosol and mitochondria as an alkalinization wave. Photosynthesis leads to increased MgATP2- levels in both the stroma and cytosol. Additionally, we observed reduction of the NAD pool driven by photosynthesis-derived electron export. Arabidopsis lines defective in chloroplast NADP- and mitochondrial NAD-dependent malate dehydrogenases show more reduced cytosolic NAD redox status even in darkness, highlighting the involvement of chloroplasts and mitochondria in shaping cytosolic redox metabolism via malate metabolism. Our study sets a novel methodological standard for precision live-monitoring of photosynthetic cell physiology. Applying this technology reveals signatures of photosynthetic physiology within and beyond the chloroplast with unprecedented resolution. Those signatures link photosynthetic activity and the fundamental biochemical functions of phototrophic cells. Significance statementBy applying novel live microscopy monitoring using fluorescent protein biosensors in plant cells, we reveal that dark-light transitions trigger profound re-orchestration of subcellular pH, ATP and NAD redox physiology not limited to chloroplasts but extending into the cytosol and the mitochondria.
Why it matches plant phenotyping methods植物細胞内のpH、ATP、NAD酸化還元状態を測定するライブイメージング手法を開発し、技術標準として提示・適用しており、表現型取得法が研究の中心である。
abstractWe developed a new standard for the live-monitoring of subcellular energy physiology by combining confocal imaging of genetically encoded fluorescent protein biosensors with advanced on-stage illumination technology
Introduction Phenotyping is critical in tree breeding, but traditional methods are often labour-intensive and not easily scalable. Resistance to biotic and abiotic stress is a key focus in tree breeding programmes. While heritable traits derived from spectral remote sensing have been identified in trees, their application to tree phenotyping remains unexplored. This study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don. Methods Using UAV-based hyperspectral and thermal imaging during a severe DNB outbreak in a clonal trial in New Zealand, we computed narrow-band hyperspectral indices (NBHIs), canopy temperature indices, radiative transfer inverted plant traits, and solar-induced fluorescence. Visual severity scores and remote sensing indices were modelled using spatially explicit mixed-effect linear models integrating pedigree and genomic data in a single-step genomic evaluation. Multi-trait models and sampling simulations were used to evaluate the potential of remote sensing indices to supplement or replace traditional phenotyping. Results Remote sensing indices exhibited narrow-sense heritability values comparable to severity scores (up to 0.37) and high absolute correlation coefficients with severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs were the most informative, reflecting physiological impacts of DNB. Combining partial visual scoring with NBHIs maintained high estimated breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy (0.59) at 20% scoring. EBV correlation with full scoring was above 0.8 even at 20% scoring. Using solely the most heritable NBHI achieved 0.71 breeding value accuracy and 0.79 absolute EBV correlation with severity scores, suggesting NBHIs can replace visual scoring with minimal precision loss. Discussion By utilising UAV-based hyperspectral and thermal imaging to capture single-tree phenotypes related to disease in a forestry trial and pairing the data to genomic evaluation, this study establishes that remote sensing data offers an efficient, scalable alternative to traditional phenotyping. Our approach constitutes a major step towards characterising specific physiological responses, facilitating the discovery of the genetic architecture of physiological traits, and significantly enhancing genetic improvement.
Why it matches plant phenotyping methodsUAVハイパースペクトル・熱画像を用いて単木の病害関連形質を取得し、従来の視覚評価との比較・代替可能性まで検証しており、フェノタイピング手法が中心である。
abstractThis study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don.
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹林冠の3D再構成アルゴリズム開発と生理形質推定が明示されており、植物表現型取得法が研究の中心的要素である。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets generated during the study (phenotype/physiological measurements and 3D canopy reconstruction outputs) in a public GitHub repository with an authors' URL, making it a paper-specific, publicly actionable asset.Dataset · publicThe datasets generated during this study are available in the GitHub repository: https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025 .Open asset ↗https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025lines:270-306Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Grass pollen is largely overlooked in investigating grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, deep learning can quantify small variations in pollen morphology visible under superresolution microscopy. We use the abstract features output by deep learning to estimate the taxonomic diversity and physiology of fossil grass pollen assemblages. Using a semi-supervised learning strategy, we trained convolutional neural networks (CNNs) on superresolution pollen images of modern grasses and unlabeled fossil Poaceae. Our models captured features that reflected both the taxonomic diversity of grass communities along an elevational gradient and morphological differences between C3 and C4 species. We applied our trained models to fossil grass pollen assemblages from a 25,000-year lake-sediment record from eastern equatorial Africa (Mt. Kenya) and correlated past shifts in grass diversity with atmospheric CO2 concentration and proxy records of local temperature, precipitation, and fire occurrence. We quantified changes in grass diversity using morphological variability of fossil pollen assemblages, approximated by the Shannon entropy of CNN features. Our data show that grassland species diversity was strongly reduced between 21,500 and 16,000 years ago, coincident with most severe regional cooling during the last ice age. C3:C4 ratios reconstructed using a gradient-boosted decision tree classifier infer a gradual decrease in C4 grasses since the late-glacial to Holocene transition, associated with decreasing fire activity and elevated temperatures. Our results demonstrate that CNN features of pollen morphology can advance palynological analysis, enabling robust estimation of grass diversity and C3:C4 ratio in ancient grassland ecosystems. SignificanceAlthough the pollen of most grass species are morphologically indistinguishable using traditional optical microscopy, we show that they can be differentiated through deep learning analyses of superresolution images. Abstracted morphological features derived from convolutional neural networks can be used to quantify the biological and physiological diversity of grass pollen assemblages, without a priori knowledge of the species present, and used to reconstruct past changes in the taxonomic diversity and relative abundance of C4 grasses in ancient grasslands. This approach unlocks ecological information previously unattainable from the fossil pollen record and demonstrates that deep learning can solve some of the most intractable identification problems in the reconstruction of past vegetation dynamics.
Why it matches plant phenotyping methods超解像花粉画像とCNN特徴量を用いて、花粉形態からイネ科の多様性およびC3:C4比を推定する手法を開発・適用しており、植物形質抽出が研究の中心です。
abstractdeep learning can quantify small variations in pollen morphology visible under superresolution microscopy
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.
Why it matches plant phenotyping methods植物への除草剤処理を目的とするが、スペクトル画像から葉温度や蛍光などの植物状態を抽出し、機械学習で作用機序を診断する画像解析手法が中心であるため、植物フェノタイピング手法として含める。
abstractTherefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).
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.
Why it matches plant phenotyping methodsイネいもち病による植物生理状態をクロロフィル蛍光で定量・識別する手法を評価しており、病害影響の表現型取得が研究の中心である。
abstractHere, we assessed the potential of chlorophyll fluorescence (CF) for quantifying rice blast impact on plant physiology.
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10-day filtered timeseries: 1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and 2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsFPARという作物・植生キャノピーの明示的な状態量を対象に、MODIS/VIIRSデータのNRTフィルタリング、相互校正、品質評価を開発・記述しており、単なる農業利用ではなく再利用可能な測定データセットと抽出手法が中心である。
abstractwe present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications.
Reproduction assets foundThe paper's own filtered and intercalibrated MODIS/VIIRS FPAR dataset (the paper's core output) is explicitly described as open and freely available in near real time via the JRC Data Catalogue and the ASAP website, both of which appear in allowed_urls. No author analysis code is mentioned.Dataset · publicec.europa.eu/, last
access: 30 September 2025) early warning system. The
FPAR dataset is accompanied by associated quality layers
and has a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR Open asset ↗1aac79d8-0d68-4f1c-a40f-b6e362264e50pdf-raw-page:3 lines:1-86Dataset · publichas a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR data; (ii) to present the intercalibration performed
between the filtered MODIS-FPAR and the filtered VIIRS-
FPAR; (iii) to evaluate tOpen asset ↗ASAP websitepdf-raw-page:3 lines:1-86Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
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.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。
abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methods植物ストレス応答を測定する高スループット表現型解析システムの開発・技術的実証が中心であり、複数センサーと自動取得ワークフローを統合している。
abstractPhenoGazer, 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.
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 (SIFRₑd and SIFNIR). 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 (fₑₛc), while absorbed photosynthetically active radiation (APAR) and intensity component from hue-saturation-intensity color model (IHSI) were used to account for illumination. And driver factor for SIF extraction was explored. Results showed a strong relationship (R² ≥ 0.95) between original simulated radiance from sub-nanometer spectrometers and radiance resampled to 3, 5, and 7 nm FWHM. SIFNIR 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 SIFNIR by fₑₛc, APAR, and IHSI, improved correlations with LCC (R = 0.77, 0.74, and 0.86) compared to unmodified SIFNIR (R = 0.65). Estimation model based on SIF modified by fₑₛc, APAR and IHSI presented RP² of 0.73, 0.75, 0.84 and outperformed SIF (RP² = 0.52), and photosynthetically active radiation and APAR showed R of 0.52 and 0.72 with SIFNIR, 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.
Why it matches plant phenotyping methods植物表現型測定のためのSIF抽出法を、スペクトル分解能・群落構造・照明の影響を考慮して開発・検証しており、LCC推定という植物形質への適用も中心的である。
abstractThis study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy.
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.
Why it matches plant phenotyping methods狭帯域ハイパースペクトル画像からSIFを抽出し、冠層構造・照明の影響を補正して葉緑素含量を推定する方法を、シミュレーションと実験で検証・改良しており、植物表現型取得が中心である。
abstractThis study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy.
Norway spruce (Picea abies Karst L.) is one of the most ecologically and economically significant tree species in Europe, accounting for nearly half of the continent's forest economic value. However, drought is a significant stress factor associated with increasing Norway spruce mortality across Europe. Provenance trials, a traditional approach to assess adaptive variation, face limitations stemming from the finite number of sites, seed sources involved, and their required labor-intensive nature. In response, we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics, transcriptomics, and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days. Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform, it was possible to characterize early and late drought stress responses. Consistent with phenotypic data, mRNA-seq, and metabolic profiles revealed apparent differences between treatments. While during the drought stress the metabolic data indicated an increased production of ABA, α-tocopherol, zeaxanthin, lutein, and phenolics, mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts. Although drought responses were largely conserved between the two provenances, they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool, and molecularly in transcriptional and phenolic profiles. In conclusion, our study demonstrates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.
Why it matches plant phenotyping methods多センサー高スループット表現型解析法の開発と、50以上の生理・生育形質による評価が研究の中心であり、単なるストレス実験のルーチン測定ではない。
abstractwe developed a comprehensive multisensor high-throughput phenotyping method
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.
Why it matches plant phenotyping methods植物ストレス応答を測定する高スループット表現型解析システムの開発・実証が中心であり、複数センサーと自動取得ワークフローを統合している。
abstractPhenoGazer, 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.
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.
Why it matches plant phenotyping methods植物の成長・生理形質を取得するマルチスペクトル自動イメージング基盤を開発し、蛍光パラメータを検証してストレス事例で実証しているため、フェノタイピング手法が中心である。
abstractHere, 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.
Efficient measurement of photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and electron transport rate (Jmax), is essential for advancing research and breeding aimed at enhancing crop productivity. Traditional methods are time-intensive, which limits their scalability. Remote sensing presents an opportunity for estimating these traits; however, it often lacks an affordable platform for effective spatial mapping, a critical aspect of phenotyping. This study explored the use of unmanned aerial vehicle (UAV) multispectral data to estimate and spatially map photosynthetic traits in tea chrysanthemums during the branching and budding stages under an open canopy. Over six field experiments across varieties conducted in 2022–2023, we captured canopy reflectance using UAV-mounted multispectral sensors, calculated spectral indices, and measured the photosynthetic traits of the upper leaves using a portable photosynthesis system. The results indicated that certain indices, particularly those incorporating green and red-edge bands, effectively estimated photosynthetic traits, with the simplified canopy chlorophyll content index (SCCCI) yielding the most accurate Vcmax estimates (R² = 0.52) and the chlorophyll vegetation index (CVI) providing the best estimates for Jmax (R² = 0.38). The integration of variable selection with partial least squares regression (PLSR) modeling further enhanced the precision of the model (Vcmax: R² = 0.70; Jmax: R² = 0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングとPLSRにより、光合成形質を推定・空間マッピングする方法が研究の中心であり、迅速な植物フェノタイピングへの応用も明示されている。
titleHigh-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Karst L.) is one of the most ecologically and economically significant tree species in Europe, accounting for nearly half of the continent's forest economic value. However, drought is a significant stress factor associated with increasing Norway spruce mortality across Europe. Provenance trials, a traditional approach to assess adaptive variation, face limitations stemming from the finite number of sites, seed sources involved, and their required labor-intensive nature. In response, we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics, transcriptomics, and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days. Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform, it was possible to characterize early and late drought stress responses. Consistent with phenotypic data, mRNA-seq, and metabolic profiles revealed apparent differences between treatments. While during the drought stress the metabolic data indicated an increased production of ABA, α-tocopherol, zeaxanthin, lutein, and phenolics, mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts. Although drought responses were largely conserved between the two provenances, they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool, and molecularly in transcriptional and phenolic profiles. In conclusion, our study demonstrates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.
Why it matches plant phenotyping methodsマルチセンサー高スループット表現型解析法を開発し、50以上の生理・成長形質を測定して乾燥ストレス応答評価に適用しており、表現型取得法が研究の中心である。
abstractwe developed a comprehensive multisensor high-throughput phenotyping method
= 0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータで作物の光合成形質を推定する高スループット表現型解析が中心であり、センサープラットフォームの実質的な適用に該当する。
titleHigh-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits.
Reproduction assets foundThe paper's authors publicly deposited the calibration and validation datasets of UAV-based spectral indices and photosynthetic trait measurements (Vcmax/Jmax) in a GitHub repository, directly reproducing this paper's phenotyping measurements and analysis inputs.Dataset · publicThe calibration and validation datasets of UAV-based spectral indices and photosynthesis supporting our results are available in the GitHub repositories at https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.git .Open asset ↗https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.gitlines:107-117Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Capturing crop physiological information by phenotyping is a key trend in smart agriculture. However, current studies underutilize spatial structural information in phenotypic imaging. To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability, we conducted controlled experiments on ‘Toyonoka’ strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions. The results revealed that NPQ/1D-Parallel/TENT, Y(NO)/2D-Region/INEM, and qP/1D-Parallel/TENT presented the highest mutual information, with the maximum net photosynthetic rate (Pₘₐₓ), relative electrolyte conductivity (REC), and total chlorophyll content (Chlₐ ₊ b), respectively. The difference between the Photosynthetic Physiological Potential Index (PPPI) and relative negative accumulated temperature (RNAT)/650 effectively was used to calculate the cold damage risk (CDRI). An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest, achieving an R² of 0.98, an RMSE of 0.337, a classification accuracy of 92.13 %, and a Kappa coefficient of 0.904. qP/1D-Parallel/TENT contributed the most to the model. This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.
Why it matches plant phenotyping methods植物の生理状態・低温障害リスクを、表現型画像由来の空間変動特徴と機械学習で推定し、複数モデルの性能比較・検証を行っており、表現型取得・解析手法が中心である。
abstractCapturing crop physiological information by phenotyping is a key trend in smart agriculture.
Common beanLeafRootStem / branchGrowth / development / phenologyPhotosynthesis / fluorescenceStress response / tolerance
In the course of climate change, drought is becoming one of the most important abiotic stress factors in agroecosystems and significantly affects agricultural productivity. Common bean (Phaseolus vulgaris L.), one of the most important legumes with a high protein content for human consumption, is very sensitive to water deficit. Thus, it is important to understand the physiological and developmental effects of water deficit on the bean. Thanks to technological advances, traditional phenotyping methods have evolved towards high-throughput phenotyping (HTP), which utilizes various imaging technologies for rapid and non-destructive monitoring of plant traits. This review examines the effects of water deficit on bean morphology (roots, leaves, stems, and generative organs), physiology (photosynthesis, antioxidant activity, phytohormones), and gene expression. We will also describe the HTP techniques used to quantify this water deficit-induced response through different imaging techniques and evaluate their applicability for the generation of reliable phenotypic data and the selection of drought-tolerant genotypes for further breeding and genetic progress.
Why it matches plant phenotyping methods乾燥ストレス下のインゲンマメ形質を定量するハイスループット画像技術をレビューし、表現型データの信頼性と適用性を評価するため、方法レビューとして中心的に該当する。
abstractThis review examines the effects of water deficit on bean morphology (roots, leaves, stems, and generative organs), physiology (photosynthesis, antioxidant activity, phytohormones), and gene expression.
The study evaluates the accuracy of two FvCB model sub-models (I and II) in estimating the maximum electron transport rate for CO 2 assimilation ( J A-max ) by comparing estimated values with observed maximum electron transport rates ( J f-max ) in four C 3 species: Triticum aestivum L., Silphium perfoliatum L., Lolium perenne L., and Trifolium pratense L. Significant discrepancies were found between J A-max estimates from sub-model I and observed J f-max values for T. aestivum , S. perfoliatum , and T. pratense ( p J A-max for T. aestivum . Sub-model II consistently produced higher J A-max estimates than sub-model I. This study highlights limitations in the FvCB sub-models, particularly their tendency to overestimate J A-max when accounting for electron consumption by photorespiration ( J O ), nitrate reduction ( J Nit ), and the Mehler reaction ( J MAP ). An alternative empirical model provided more accurate J f-max estimates, suggesting the need for improved approaches to model photosynthetic electron transport. These findings have important implications for crop yield prediction, ecological modeling, and climate change adaptation strategies, emphasizing the need for more accurate estimation methods in plant physiology research.
Why it matches plant phenotyping methods植物の最大電子伝達速度という生理形質について、FvCBモデルの推定精度を観測値と比較検証し、代替モデルも評価しているため、方法検証が研究の中心である。
abstractThe study evaluates the accuracy of two FvCB model sub-models (I and II) in estimating the maximum electron transport rate for CO 2 assimilation
Field / plotLeafPhysiological trait estimationPhotosynthesis / fluorescenceStomatal traits
Global environmental change has severe impacts on plants and ecosystems. Anthropogenic carbon emissions, for example, lead to elevated CO 2 (eCO 2 ) in the atmosphere which can stimulate photosynthesis (A n ) and stomatal conductance (g s ) with impacts for carbon, water and nutrient pools and fluxes in terrestrial ecosystems. In fact, approximately 25% of the annual anthropogenic CO 2 emissions are taken up and stored in the biosphere which slows down the growth of CO 2 in the atmosphere and dampens climate change. The stimulation of A n and g s by eCO 2 is thought to critically contribute to this carbon uptake. If eCO 2 will continue to stimulate A n and g s and ecosystem carbon uptake in the future is, however, unclear. This is because important questions regarding the effects of eCO 2 on A n and g s and how these effects are influenced by different plant species or different environmental agents such as nutrient and water availability are unresolved. Experiments are often too short-lived to resolve the complexity of interactions by which eCO 2 affects A n and g s in natural ecosystems. Also, monitoring programs are often not sufficiently long-term to capture in-situ responses of plants to rising atmospheric CO 2 . New and innovative tools are therefore needed to understand how eCO 2 and other global change drivers impact A n and g s in plants and to resolve with this a key uncertainty in the coupled carbon-climate system. The analysis of archived plant material, e.g. in herbarium collections, offers an exciting new opportunitiy to complement experiments and long-term monitoring programmes to reconstruct the long-term in-situ physiological responses of plants to environmental change. In my presentation, we will introduce a new approach that allows for the first time the quantitative reconstruction of A n and g s from the carbon isotope composition and nitrogen content per unit leaf area in archived plant material. I will show how we have applied this new approach to 3000 plant samples from the Herbaria Basel that have been collected across Switzerland from 1850 to today to infer for the long-term in-situ physiological responses of plants to global environmental change. Our data indicate a uniform 20% increase of A n between 1850 and today and a small, yet steady decline in g s . Most interestingly we found very little differences in these responses among different plant functional types or among plants originating from different habitats (wet - dry or nutrient poor - nutrient rich), suggesting a uniform in-situ physiological response of plants to eCO 2 . Our data contributes a new approach to assess the long-term physiological responses of plants to global environmental change and has important implications for modelling past and future carbon and water relations in terrestrial ecosystems.
Why it matches plant phenotyping methodsアーカイブ植物試料から炭素同位体組成と葉面積当たり窒素量を用いて光合成速度と気孔コンダクタンスを定量推定する新手法を開発し、大規模試料へ適用しているため、植物フェノタイピング手法が中心である。
abstractwe will introduce a new approach that allows for the first time the quantitative reconstruction of A n and g s from the carbon isotope composition and nitrogen content per unit leaf area in archived plant material.
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.
Why it matches plant phenotyping methods夜間の蛍光測定による光合成形質フェノタイピングを開発・評価し、昼間測定との比較で環境変動低減と遺伝率向上を検証しているため、手法が研究の中心である。
abstractHigh-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity.
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
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Alter P. Dreissen A. Luo F.-L. MatsubaOpen asset ↗lines:613-651Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis 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.
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 match · UnverifiedOpenAlex · 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.
Why it matches plant phenotyping methodsPhenoLabの高スループット反射・蛍光イメージングを用いて、病害抵抗性と複合ストレス応答を非破壊的に評価し、スペクトルシグネチャを検証しており、表現型取得法が研究の中心です。
abstractWe 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.
Drought stress constitutes one of the most severe constraints to global agricultural productivity. Early drought detection is pivotal for sustainable agriculture, yet current approaches overlook critical dimensions of plant sensitivity. While advancements in photosynthetic parameter analysis (e.g., gas exchange, and chlorophyll fluorescence) have enhanced drought monitoring, three understudied factors limit progress: (1) differential drought sensitivity across plant organs (e.g., root nodules vs. leaves); (2) the selection of sensitive photosynthetic parameters and optimal measurement timing for stress detection; and (3) the identification of leaf layers most responsive to water deficits. By synthesizing insights from nodule physiology in legumes, cross-species evidence on multi-layered leaf senescence, and the temporal dynamics of stress sensitivity, this paper proposes a 'whole-plant sensitivity analysis' framework. Integrating organ-, parameter-, and time-specific perspectives, this paper aims to refine early drought detection in the field and enhance plant resilience research.
Why it matches plant phenotyping methods植物の乾燥ストレスを光合成パラメータで早期検出する測定設計を中心に、器官・パラメータ・測定時期を統合した枠組みを提案するレビューであり、表現型取得法が中核です。
abstractWhile advancements in photosynthetic parameter analysis (e.g., gas exchange, and chlorophyll fluorescence) have enhanced drought monitoring
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
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.
Why it matches plant phenotyping methods葉の蛍光寿命測定に基づく定量的速度論モデルを構築し、光防護・消光状態を分解、予測、定量する手法が研究の中心である。
abstractBased 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.
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.
Why it matches plant phenotyping methodsPAM蛍光データから光合成性能を推定する機械学習モデルを開発・比較評価しており、植物生理形質の取得・推定手法が研究の中心である。
abstractTo improve prediction accuracy, we applied a machine learning model.
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 match · UnverifiedEurope PMC · checked 6 Sept 2026
There is a consensus on the role of protein conformational changes within the photosynthetic antenna that alter the spectral properties of the embedded pigments during regulated heat dissipation, but despite this, the molecular mechanisms involved are still poorly understood. The mechanisms, associated with the quenching of excessive energy, are however commonly seen in vitro as 'red spectral forms' of Chlorophyll a or red-shifted and broadened absorbance behaviour. Similar mechanisms are expected to occur in vivo, but so far, the spectral absorbance changes have not been described in detail at the whole plant canopy level. Here we derive the dynamic changes in surface absorbance features from canopy reflectance of tomato plants (Solanum lycopersicum L.), under increasing light exposure and drought. Specific features in the green (520 nm-peak) and the red-edge (695 nm-peak) region could indicate the quick activation of quenched conformational states under low light conditions, for all plant canopies. Under additional drought stress, further red-shifted and broadened absorbance changes appear, suggesting another conformational change. The latter changes disappeared upon drought recovery. Observing these antenna-related mechanisms from proximal sensing demonstrates the promising potential of imaging spectroscopy to detect the stepwise tuning of regulated energy dissipation of plants in a non-destructive way.
Why it matches plant phenotyping methodsキャノピー反射スペクトルから植物の光防御・エネルギー散逸状態を非破壊的に推定するイメージング分光法を中心に扱っており、単なる生理測定ではない。
abstractHere we derive the dynamic changes in surface absorbance features from canopy reflectance of tomato plants (Solanum lycopersicum L.), under increasing light exposure and drought.
Nonphotochemical quenching (NPQ) is a critical photoprotective mechanism in plants, safeguarding photosystem II (PSII) and PSI from photodamage under abiotic stress. However, it is unclear if different stressors lead to similar NPQ phenotypes, and the magnitude of natural variation (between and within plant species) in NPQ response to abiotic stress is unknown. Testing a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes. Our results show substantial variation in NPQ phenotypes across species, genotypes and treatments. In C3 crops, tobacco and soybean, multiple NPQ parameters generally increased under chilling and drought, while in C4 crops, maize and sorghum, NPQ traits were more variable including a decrease of multiple NPQ parameters. Low-N stress revealed genotype- and developmental stage-specific effects on NPQ, potentially reflecting distinct adaptive strategies and regulatory changes in NPQ stress response. A significant effect of ecotype and stress treatment was detected on most NPQ kinetics traits in Arabidopsis thaliana , however, the interaction between ecotype and treatment was stronger in drought than in chilling. Differential regulation of NPQ could be associated with a combination of changes in proton motive, ATPase synthase activity, and PSI redox state. Our findings highlight that interpreting relative changes in NPQ under abiotic stress is inherently complex and demands a broader integration of physiological data across multiple regulatory layers.
Why it matches plant phenotyping methods半高速スループットの葉ディスク法を用いてNPQ動態形質を測定し、複数種・遺伝子型・ストレス条件で適用しているため、植物生理フェノタイピング手法の実質的応用と判断します。
abstractTesting a semi-high-throughput leaf-disc approach for examining the NPQ kinetics parameters, we investigated NPQ under chilling, drought and low nitrogen stress across multiple species and/or genotypes.
Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance and climate change responses. Traditional LAD measurement methods are time-consuming and often limited to individual sites, hindering effective data acquisition at the ecosystem scale and complicating the modeling of canopy LAD variations. We present a deep learning approach that is more affordable, efficient, automated, and less labor-intensive than traditional methods for estimating LAD. The method uses unmanned aerial vehicle images processed with structure-from-motion point cloud algorithms and the Mask Region-based convolutional neural network. Validation at the single-leaf scale using manual measurements across three plant species confirmed high accuracy of the proposed method (Pachira glabra: R 2 = 0.87, RMSE = 7.61°; Ficus elastica: R 2 = 0.91, RMSE = 6.72°; Schefflera macrostachya: R 2 = 0.85, RMSE = 5.67°). Employing this method, we efficiently measured leaf angles for 57 032 leaves within a 30 m × 30 m plot, revealing distinct LAD among four representative tree species: Melodinus suaveolens (mean inclination angle 34.79°), Daphniphyllum calycinum (31.22°), Endospermum chinense (25.40°), and Tetracera sarmentosa (30.37°). The method can efficiently estimate LAD across scales, providing critical structural information of vegetation canopy for ecosystem modeling, including species-specific leaf strategies and their effects on light interception and photosynthesis in diverse forests.
Why it matches plant phenotyping methodsUAV画像と深層学習を用いて葉角度分布を推定する手法を開発し、手動測定で検証した植物フェノタイピング研究。
abstractWe present a deep learning approach that is more affordable, efficient, automated, and less labor-intensive than traditional methods for estimating LAD.
High-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding. This study aimed to develop multivariate models using spectral reflectance data to estimate physiological and yield traits in spring wheat genotypes exposed to different water regimes. Fifteen spring wheat varieties and one triticale genotype were evaluated in sixteen environments, which were generated by combining data from over four seasons in two Mediterranean locations in Chile, along with two water regimes (irrigated and water deficit). Measured traits were leaf pigments, leaf area index (LAI), leaf water potential (Ψleaf), gas exchange, chlorophyll fluorescence, grain yield, and carbon isotope composition (δ13C). Hyperspectral reflectance was recorded at the leaf level and canopy level (45° and 90°) at anthesis and grain filling and used to generate predictive models using partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), and elastic net (E.net) regression. Models explained over 60% of the trait variation (R2) for 70% of traits analysed. Fluorescence parameters (R2 = 0.78–0.88), δ13C (R2 = 0.80), leaf pigments (R2 = 0.50–0.74), Ψleaf (R2 = 0.72), and LAI (R2 = 0.68) had the most robust predictions. LASSO regression showed the highest R2 and accuracy, while canopy-level spectra at 90° excelled in predicting grain yield and LAI, and leaf-level spectra were best for fluorescence traits. These methods facilitated the identification of genotypes with superior water-deficit adaptation and yield potential, accelerating breeding, enhancing crop resilience to climate change, and improving food security.
Why it matches plant phenotyping methods葉・キャノピーのハイパースペクトル反射を用いて複数の植物生理・収量形質を推定する予測モデルを開発・比較しており、表現型取得と抽出が研究の中心である。
abstractHigh-throughput field phenotyping offers an efficient solution for identifying and selecting genotypes of interest in plant breeding.
Capturing crop physiological information by phenotyping is a key trend in smart agriculture. However, current studies underutilize spatial structural information in phenotypic imaging. To evaluate the feasibility of crop cold stress monitoring based on phenotypic spatial variability, we conducted controlled experiments on 'Toyonoka' strawberry plants under four dynamic cooling gradients and three stress durations and analyzed the dependence of their photosynthetic physiology and phenotypic traits on temperature-time interactions. The results revealed that NPQ/1D-Parallel/TENT, Y(NO)/2D-Region/INEM, and qP/1D-Parallel/TENT presented the highest mutual information, with the maximum net photosynthetic rate (P max ), relative electrolyte conductivity (REC), and total chlorophyll content (Chl a + b ), respectively. The difference between the Photosynthetic Physiological Potential Index (PPPI) and relative negative accumulated temperature (RNAT)/650 effectively was used to calculate the cold damage risk (CDRI). An XGBoost-based model integrating the PPPI and RNAT outperformed AdaBoost and RandomForest, achieving an R 2 of 0.98, an RMSE of 0.337, a classification accuracy of 92.13 %, and a Kappa coefficient of 0.904. qP/1D-Parallel/TENT contributed the most to the model. This study provides a scientific basis for phenotypic information mining and agro-meteorological disaster monitoring.
Why it matches plant phenotyping methodsイチゴの低温ストレスを対象に、表現型画像の空間変動特徴を抽出・融合し、光合成生理や冷害リスクを推定する手法を開発・評価しており、表現型取得・解析が研究の中心である。
abstractcurrent studies underutilize spatial structural information in phenotypic imaging
To investigate the dynamic changes in superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities in Tomato yellow leaf curl virus (TYLCV)-infected tomato leaves is essential for monitoring tomato growth, selecting disease-resistant varieties and disease control. Utilizing hyperspectral technique offers a rapid and non-destructive method to estimate antioxidant enzyme activity in tomato leaves. This study focuses on one a TYLCV-susceptible tomato variety (Hezuo 908) and two TYLCV-resistant varieties (Dingyanfen No. 3 and No. 5) to analyze the changes in photosynthetic characteristics and antioxidant enzyme activity following TYLCV infection. Hyperspectral data were used to quantify the correlation between antioxidant enzyme activity and spectral features under different pretreatments, as well as the relationship between enzyme activity and classical spectral indices. Several optimized indices were developed by iterating on the condition of R, forming a combined index. Considering efficiency and complexity, the support vector machine regression algorithm was employed to evaluate the predictive performance of the models. The results showed a decline in photosynthetic rate and relative chlorophyll content, while stomatal conductance initially decreased and then increased. The activities of the three antioxidant enzymes increased. activities of the three antioxidant enzymes post-TYLCV infection, with POD activity correlating with tomato variety resistance—a potential auxiliary index for antiviral variety identification. Among the models, the CAT prediction model performed the best, with a test set determination coefficient (R²) of 0.82, followed by POD with an R² of 0.67, and SOD R² of 0.43. These findings demonstrate that spectra enable rapid and non-destructive estimation of antioxidant enzyme activity in tomatoes under viral stress. This study provides valuable insights for antiviral variety breeding and the early warning of viral diseases.
Why it matches plant phenotyping methodsトマト葉の抗酸化酵素活性をハイパースペクトルデータから非破壊推定する手法を開発・評価しており、植物の生理状態・ウイルスストレスの表現型取得が中心である。
abstractUtilizing hyperspectral technique offers a rapid and non-destructive method to estimate antioxidant enzyme activity in tomato leaves.
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.
Why it matches plant phenotyping methods衛星SIF・PRIと最適化手法により植物の生理形質Vcmax25を推定・全球マッピングし、複数地点で検証しているため、植物フェノタイピング手法が中心です。
abstractwe 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.
Societal Impact Statement Characterizing variability in crop traits is key for understanding agroecosystem responses to environmental change. However, trait data are often time‐consuming to collect and therefore still limit our understanding and predictions of agriculture responses to environmental change. We tested the ability of reflectance spectroscopy—a high‐throughput technique—to rapidly amass trait data for multiple wine grape cultivars. Reflectance spectroscopy predicts important wine grape leaf traits including photosynthesis and biochemistry with a good degree accuracy, but in a fraction of the time compared to traditional techniques. Reflectance spectroscopy can therefore rapidly characterize wine grape phenotypes and, in doing so, inform predictions of how vines, clones and cultivars will respond to environmental change. Summary Reflectance spectroscopy has emerged as a powerful tool for non‐destructive and high‐throughput phenotyping in plants. While the ability of reflectance spectroscopy to predict traits across diverse plant species and ecosystems has received considerable attention, whether or not this technique is able to quantify within species trait variation—especially physiological traits—has been less extensively explored. Quantifying intraspecific variation in traits through reflectance spectroscopy is especially appealing in agroecology, where it may present an approach for better understanding crop performance, fitness and trait‐based responses to environmental conditions. We tested if reflectance spectroscopy coupled with partial least square regression (PLSR) predicts photosynthetic carbon assimilation ( A 420 ), RuBisCO carboxylation ( V cmax ) and electron transport ( J max ) rates, as well as leaf mass per area (LMA) and leaf nitrogen (N) concentrations, across six wine grape ( Vitis vinifera ) cultivars (Cabernet Franc, Cabernet Sauvignon, Merlot, Pinot noir, Viognier, Sauvignon blanc). PLSR models showed good capability in predicting intraspecific trait variation in wine grapes, explaining up to 55%, 58%, 62% and 62% of the variation in observed J max , V cmax , leaf N and LMA values, respectively. However, predictions of A 420 were less strong, with reflectance spectra explaining only up to 29% of the variation in this trait. Our results indicate that trait variation within species and crops is less well‐predicted by reflectance spectroscopy, than trait variation that exists among species. However, our results indicate that reflectance spectroscopy still presents a viable technique for quantifying trait variation in wine grapes specifically, and agroecosystems more broadly.
Why it matches plant phenotyping methods反射分光法とPLSRを用いてブドウ葉の光合成・生理・化学形質を非破壊推定し、予測性能を評価しており、植物表現型取得法が研究の中心である。
abstractReflectance spectroscopy has emerged as a powerful tool for non‐destructive and high‐throughput phenotyping in plants.
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.
Why it matches plant phenotyping methodsPVY抵抗性判定のためのクロロフィル蛍光測定を中心的な表現型解析手法として開発・適用し、ウイルス量および収量損失で妥当性を検証している。
abstractwe developed a phenotyping approach leveraging plant physiological responses against PVY infection.
Soil drought and salinization are key abiotic stressors for agricultural plants; the development of methods of their early detection is an important applied task. Measurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors. Potentially, RGB indices can be used to estimate narrow-band reflectance indices and/or photosynthetic parameters in plants. Analysis of this problem was the main task of the current work. We investigated relationships of six RGB indices (r, g, b, ExG, VEG, and VARI) to widely used narrow-band reflectance indices (the normalized difference vegetation index, NDVI, and photochemical reflectance index, PRI) and the potential quantum yield of photosystem II (Fv/Fm) in wheat and pea plants under soil drought and salinization. It was shown that investigated RGB indices, NDVI, PRI, and Fv/Fm were significantly changed under the action of both stressors; changes in some RGB indices (e.g., ExG) were initiated on the early stage of action of drought or salinization. Correlation analysis showed that RGB indices (especially, ExG, VARY, and g) were strongly related to the NDVI, PRI, and Fv/Fm; linear regressions between these values were calculated. It means that RGB indices measured by simple and low-cost color cameras can be used to estimate plant parameters (NDVI, PRI, and Fv/Fm) requiring sophisticated equipment to measure.
Why it matches plant phenotyping methodsRGB画像からNDVI、PRI、Fv/Fmなどの植物形質・生理状態を推定する手法の関係解析と回帰モデル構築が研究の主目的であり、方法開発として中心的です。
abstractMeasurement of red-green-blue (RGB) indices, which are calculated on basis of color images, is a simple method of proximal and remote sensing of plant health under the action of stressors.
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.
Why it matches plant phenotyping methods光合成の測定データから植物の生理形質を推定する3モデルを比較・検証し、Yeモデルの精度と適用性を中心的に評価しているため、植物フェノタイピング手法の検証研究に該当する。
abstractThis 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.
Maintaining energy homeostasis is a major challenge for plants in the current context of climate change. The Sucrose-non fermenting 1 (SNF1)-related kinase 1 (SnRK1) complex, a member of the SNF1-AMP-activated protein kinase (AMPK)-SnRK1 family of kinase complexes, is a central player in the regulation of cell energy homeostasis. The α-subunit of the complex, which possesses kinase activity and is known as SnRK1.1 or KIN10, plays a role in sensing energy status and coordinating metabolic reprogramming to counter any energy imbalance. The discovery of a dual and dynamic intracellular distribution of SnRK1.1 suggests that the activity and function of SnRK1 might be regulated by spatiotemporal changes. To investigate the spatiotemporal distribution of SnRK1.1, we developed a protocol to quantify its intracellular distribution using fluorescence confocal images acquired along the z-axis in plants expressing SnRK1.1–eGFP. Using the open-source software Fiji/ImageJ, we calculated the ratio between nuclear and non-nuclear SnRK1.1 fractions and defined this as the N/ER index. We validated our method by analyzing the response of SnRK1.1 to photosynthesis inhibition by DCMU, including changes in protein levels and phosphorylation status. In addition, comparison with results obtained using a commercial software-based approach confirmed the compatibility of the N/ER index with different segmentation and quantification tools. Originally designed for leaf tissue images, this protocol can be broadly applied to assess the role of intracellular spatiotemporal changes in a wide range of kinases or fluorescently tagged recombinant proteins. Finally, SnRK1.1 intracellular distribution may also serve as a proxy to assess changes in cellular energy status. One sentence summary New method to track SnRK1.1 distribution and changes in plant cell energy status
Why it matches plant phenotyping methods植物細胞内の蛍光画像からSnRK1.1の核／非核分布を定量する画像解析プロトコルを開発し、検証・他ソフトウェアとの比較も行っており、植物状態の測定法が中心である。
abstractwe developed a protocol to quantify its intracellular distribution using fluorescence confocal images acquired along the z-axis in plants expressing SnRK1.1–eGFP.
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
With global climate change ongoing, the frequency and intensity of extreme weather events have increased annually. Hulless barley ( Hordeum vulgare L. var. nudum), a primary crop cultivated in the Qinghai-Tibet Plateau mountains, frequently encounters multiple abiotic stresses including low temperature, high salinity, and drought. Among these stresses, drought has emerged as a critical environmental constraint affecting sustainable agricultural development worldwide. Establishing a drought resistance evaluation system for the hulless barley germplasm during its seedling stages could provide a theoretical foundation for screening and breeding drought-tolerant cultivars to address climate change challenges. This study employed two drought-sensitive (YC85 and YC88) and two drought-tolerant (ZY1252 and ZY1100) cultivars to develop an effective drought resistance evaluation protocol for hulless barley. Our findings identified several reliable indicators for assessing drought tolerance at the seedling stage: fresh mass, chlorophyll fluorescence parameters (F v /F m , NPQ, and R FD ), photosynthetic parameters (E and gsw), and reactive oxygen species (ROS) levels. The established evaluation system was subsequently applied to three uncharacterized cultivars (ZY673, ZY1403, and KL14). The results classified all three as drought-sensitive, with ZY1403 exhibiting the highest sensitivity. Our work has established a comprehensive drought resistance evaluation framework for Tibetan hulless barley. Furthermore, this study provides valuable insights for optimizing cultivation practices and water resource management strategies, offering theoretical guidance for agricultural adaptation to climate change.
Why it matches plant phenotyping methods幼苗の乾 drought resistance を評価するための指標選定・評価プロトコルと総合評価枠組みを開発し、別品種で適用しているため、表現型取得・評価法が研究の中心である。
abstractdevelop an effective drought resistance evaluation protocol for hulless barley
Identifying mutant traits is essential for improving crop yield, quality, and stress resistance in plant breeding. Historically, the efficiency of breeding has been constrained by throughput and accuracy. Recent significant advancements have been made through the development of automated, high-accuracy, and high-throughput equipment. However, challenges remain in the post-processing of large-scale image data and its practical application and evaluation in breeding. This study presents a comparative analysis of human and machine recognition, with validation of a randomly selected mutant at the physiological level performed on wild-type Arabidopsis thaliana and a candidate mutant of the M 3 generation, which was generated through mutagenesis with heavy ion beams (HIBs) and 60 Co-γ radiation. The mutant populations were subjected to image acquisition and automated screening using the High-throughput Plant Imaging System (HTPIS), generating approximately 10 GB of data (4,635 image datasets). We performed Principal Components Analysis (PCA), scatter matrix clustering, and Logistic Growth Curve (LGC) analyses, and compared these results with those obtained from traditional manual screening based on human visual assessment, and randomly selected #197 candidate mutants for validation in terms of growth and development, chlorophyll fluorescence, and subcellular structure. Our findings demonstrate that as the confidence interval level increases from 75 to 99.9%, the accuracy of machine-based mutant identification decreases from 1 to 0.446, while the false positive rate decreases from 0.817 to 0.118, and the false negative rate increases from 0 to 0.554. Nevertheless, machine-based screening remains more accurate and efficient than human assessment. This study evaluated and validated the efficiency (greater than 80%) of high-throughput techniques for screening mutants in complex populations of radiation-induced progeny, and presented a graphical data processing procedure for high-throughput screening of mutants, providing a basis for breeding techniques utilizing HIBs and γ-ray radiation, and offering innovative approaches and methodologies for radiation-induced breeding in the context of high-throughput big data.
Why it matches plant phenotyping methods植物画像取得・自動スクリーニングと機械／人手認識の比較検証が研究の中心であり、表現型選抜ワークフローの技術評価に該当する。
abstractThis study presents a comparative analysis of human and machine recognition
Advanced techniques capable of early and non-destructive detection of the impacts of water stress on trees and estimation of the underlying photosynthetic capacities on larger scale are necessary to meet the challenges of limiting plant growth and ecological protection caused by drought. We tested influence of continuous water stress on photosynthetic traits including Leaf Chlorophyll content (LCC) and Chlorophyll Fluorescence (ChlF) and combined hyperspectral reflectance as a high-throughput approach for early and non-destructive assessment of LCC and ChlF traits in Rhamnus leptophylla trees. LCC and ChlF parameters (NPQ, Fv'/Fm', ETR, ETRmax, Fm', qL, qP, Y(II) were measured alongside leaf hyperspectral reflectance from Rhamnus leptophylla suffering from constant drought during water stress. Water stress caused NPQ, Fv'/Fm', ETRmax, Fm', qL, qP, Y(II) and ETR continuous decline throughout the entire drought period. ChlF was more sensitive to drought monitoring than LCC. The original reflectance spectra and hyperspectral vegetation indices (SVIs) showed a strong correlation with LCC and ChlF. Reflectance in 540-560nm and 750-1100nm and selected SVI such as Simple Ratio (SR)752/690 can track drought responses effectively before leaves showed drought symptoms. Multivariate Linear Regression (MLR) and three machine learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were employed to develop models for estimating LCC and ChlF parameters. RF provided the best estimation accuracy for LCC compared to MLR, KNN and SVM, achieving an R 2 value of 0.895 for all LCC samples. The canopy layer significantly influenced the estimation accuracy of LCC, with the middle layer yielding the highest R 2 value. RF also demonstrated superior performance compared to MLR, KNN and SVM for estimating NPQ, Fv'/Fm', ETRmax, Fm', qL, qP, Y(II) and ETR, achieving R 2 value of 0.854 for NPQ, 0.610 for Fv'/Fm', 0.878 for ETRmax, 0.676 for Fm', 0.604 for qL, 0.731 for qP, 0.879 for Y(II), and 0.740 for ETR. Our results indicate that photosynthetic traits combined hyperspectral reflectance can monitor the effect of drought on trees effectively with significant potential for monitoring drought over large areas.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から光合成形質・クロロフィル形質を推定する手法を開発・比較評価しており、表現型取得が研究の中心である。
abstractcombined hyperspectral reflectance as a high-throughput approach for early and non-destructive assessment of LCC and ChlF traits
Plant height and SPAD values are critical indicators for evaluating peanut morphological development, photosynthetic efficiency, and yield optimization. Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role in ensuring food and edible oil security. This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. Multispectral UAV and ground data were collected across four growth stages (2023–2024). Using spectral indices and Texture features, four models (PLSR, SVM, ANN, RFR) were trained on 2024 data and independently validated with 2023 datasets. The ensemble machine learning models (RFR) significantly enhanced estimation accuracy (R2 improvement: 3.1–34.5%) and robustness compared to the linear model (PLSR). Feature stability analysis revealed that combined spectral-textural features outperformed single-feature approaches. The SVM model achieved superior plant height prediction (R2 = 0.912, RMSE = 2.14 cm), while RFR optimally estimated SPAD values (R2 = 0.530, RMSE = 3.87) across heterogeneous field conditions. This UAV-based multi-modal integration framework demonstrates significant potential for temporal monitoring of peanut growth dynamics.
Why it matches plant phenotyping methodsUAVマルチソースデータと機械学習により、ピーナッツの草丈およびSPAD値を推定するフレームワークを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractThis study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
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.
Why it matches plant phenotyping methods植物の成長・葉温・光合成効率・クロロフィル含量を取得するマルチスペクトル自動計測プラットフォームの開発と検証が中心であり、植物フェノタイピング手法として適格。
abstractHere, 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 .
With the rapid advancements of Internet of Things (IoT) technology, understanding sensor signals has become the key to acquiring plant physiological data and realizing the concept of “speaking plants”. This study developed a real-time and intelligent monitoring system using IoT-enabled weight sensors to continuously track the total weight of the shoot and root systems of tomato plants. A newly introduced variable, the photosynthetic leaf area index (LAIₚ), which plays a crucial role in capturing solar radiant heat, was used to balance energy components in the modeling of tomato transpiration processes. In this framework, a canopy transpiration model was developed and trained using data collected by sensors to enhance the online accuracy of tomato transpiration predictions. Supported by this model, in-depth analysis of weight sensor signals enabled the non-destructive, data-driven, and real-time extraction of LAI and LAIₚ. The parameter LAIₚ, the radiation absorbing portion of LAI, also serves as a measure for photosynthetic efficiency when related to the concurrently assessed growth rate. The results demonstrated strong consistency between simulated and measured values of LAI and LAIₚ (LAI simulation: R² = 0.99, NRMSE = 0.04; LAIₚ simulation: R² = 0.98, NRMSE = 0.04). This new WEP (Weight Effectiveness Photosynthesis) based modeling provides the required biofeedback for robust crop management, expanding the validity of model precision in cases when non-optimal environmental conditions dominate due to economic considerations. This study reveals that multiple critical insights can be obtained from complex process signals monitored by a single sensor, thereby providing a new perspective on multi-parameter estimates of plant growth and management.
Why it matches plant phenotyping methodsトマトの重量センサー信号とモデルにより、LAIおよび光合成関連LAIを非破壊・リアルタイム推定する方法を開発しており、植物表現型の取得・抽出が研究の中心である。
abstractThis study developed a real-time and intelligent monitoring system using IoT-enabled weight sensors to continuously track the total weight of the shoot and root systems of tomato plants.
SorghumLeafTissuePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
A non-destructive methodology for monitoring impedance changes in sorghum leaves was developed and recorded irrigation-dependent responses that differed between leaf tissues. Metal microneedles were used as impedance probes and were shown to cause minimal damage to the plant. The needles were placed on either the abaxial or adaxial side of the leaf midrib using small clamps and re-used hundreds of times with minimal signs of wear. Cross-sectional images verified the precision of microneedle placement near vascular bundles on the abaxial surface and in non-vascular hydrenchyma on the adaxial surface. Impedance measurements with microneedles displayed a significant decrease in resistance compared to planar electrodes due to bypassing the epidermal layer. A tissue-specific impedance response was seen in relation to irrigation where the non-vascular adaxial surface remained largely stable throughout a day of measurement, while impedance increased in the vascular abaxial surface during exposure to light and decreased following watering. Impedance data were also compared with simultaneous gas exchange measurements of photosynthesis and transpiration.
Why it matches plant phenotyping methodsソルガム葉の組織特異的な水分・生理応答を非破壊インピーダンス測定で取得する手法を開発し、電極比較や配置精度、再利用性も検証しているため、植物フェノタイピング手法が中心である。
abstractA non-destructive methodology for monitoring impedance changes in sorghum leaves was developed
Verticillium wilt (VW) is one of the most common and devastating diseases in cotton production, and early diagnosis is very important to alleviate the damage of VW. Recent studies have shown that early diagnosis and prevention of soil-borne diseases can be achieved by detecting spectral changes related to chlorophyll fluorescence and transpiration. However, there are no systematic studies to report the heterogeneity of photosynthetic characteristics and their spectral responses of plant leaves at the early stage of VW. In this study, the spatial heterogeneity characteristics in chlorophyll fluorescence of cotton leaves during the incubation period of VW were discussed, and the pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information, aiming to realize the early diagnosis of VW of cotton. The results showed that the chlorophyll fluorescence parameters Y(NPQ) (quantum yield of regulated energy dissipation) and NPQ/4 (non-photochemical quenching/4) values of cotton increased and the Y(II) (effective quantum yield of photosystem II) decreased significantly during the asymptomatic period of VW, indicating heterogeneity in photosynthetic capacity of leaves in the early stage of VW, i.e., VW developed from leaf margins to leaf center, and leaf margin was the area where chlorophyll fluorescence changed firstly. Furthermore, the multi-task learning model constructed with vegetation index and wavelet features accurately inversed the pixel-level heterogeneous characteristics of leaf Y(NPQ) and Y(II). The spectral information had the best inversion performance for the local heterogeneous regions of Y(II), with a classification accuracy of 85.6 %, a Kappa coefficient of 0.71, an r² (coefficient of determination) of 0.66, and a RMSE (root mean square error) of 0.06. According to the inversion results of the local heterogeneous region of Y(II), the accurate diagnosis of early-stage VW was realized, with an accuracy of 87.4 % and a Kappa coefficient of 0.75. This study will provide a new method for the early prevention and control of VW.
Why it matches plant phenotyping methods綿花葉のクロロフィル蛍光という植物生理形質を、ハイパースペクトル画像と機械学習で画素レベル推定し、萎凋病の早期診断へ応用しており、形質取得・抽出手法が中心的である。
abstractthe pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information
Nitrogen (N) utilization rate is a key index used to assess whether N fertilizer is applied rationally. In addition, it can reflect crop growth. However, research on multi-angular spectral real-time monitoring of physiological indexes of N efficiency (photosynthetic N-use efficiency [PNUE]) during wheat growth and advance prediction of final N-use efficiency (NUE) at maturity is scant. Consequently, the accuracy of existing methods is estimating N fertilizer utilization status with remotely sensed data is low, and the mechanisms underlying the relationship between reflectance and PNUE remain unclear. To address the knowledge gap, in the present study, two wavelength variable-selected algorithms, competitive adaptive reweighted sampling (CARS) and feature selection learning (ReliefF), were used to identify wavebands sensitive to PNUE. The screened feature bands were used as inputs in the input layer of four multivariate algorithms (Partial Least Squares Regression [PLSR], Support Vector Regression [SVR], Artificial Neural Network [ANN], and Random Forest [RF]) to determine the best model for monitoring PNUE and predicting NUE before wheat ripening. Compared to all machine learning methods, the PLSR-based CARS (CARS-PLSR) algorithm predicts PNUE with an accuracy >90 % at 13 observation angles. At last, we predicted the NUE according to the PNUE-NUE correlation and the CARS-PLSR-PNUE correlation. The lack of significant differences in slope and intercept across the five growth stages indicates that the CARS-PLSR model is a better PNUE tracker and NUE predictor in diverse field conditions. The combination of remote sensing techniques and integrated evaluation approaches provides accurate and timely information on crop N fertilizer utilization status, which could facilitate tailoring N fertilizer management to wheat requirements, thus maintaining N fertility for high photosynthetic yield, while minimizing N losses to the environment.
Why it matches plant phenotyping methods多角度スペクトル計測と特徴選択・機械学習を用いて、コムギのPNUEおよびNUEを推定・予測する手法が研究の中心であり、植物生理形質の取得・抽出に該当する。
abstracttwo wavelength variable-selected algorithms, competitive adaptive reweighted sampling (CARS) and feature selection learning (ReliefF), were used to identify wavebands sensitive to PNUE.
Hyperspectral remote sensing (RS) has demonstrated to be useful for estimating vegetation photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and the electron transport rate (Jmax). However, the spectral ranges used by RS models for predicting photosynthetic traits vary, and their predictive performance across different crop varieties is poor. This study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability. Leaf-level measurements linked high-throughput spectral reflectance observations with photosynthetic traits obtained via a portable photosynthesis system. Results revealed strong correlations between the green and red edge bands and photosynthetic traits. Among newly developed vegetation indices, the structure insensitive pigment index [SIPI₍₈₅₀,₆₉₁,₄₇₆₎] and SIPI₍₈₅₀, ₆₉₉, ₅₇₉₎ effectively predicted Vcmax and Jmax, respectively. In contrast, partial least squares regression (PLSR) modeling combined with reflectance from 400 to 1000 nm outperformed in estimating photosynthetic traits of tea chrysanthemum and exhibited excellent performance in a multi-variety validation dataset, indicating applicability across different varieties. Our findings suggest that increasing the modeling dataset's variability enhances the universality of RS estimation models for photosynthetic traits, providing a valuable tool for breeders to efficiently collect photosynthetic trait information.
Why it matches plant phenotyping methods葉のハイパースペクトル反射からVcmax・Jmaxを推定するセンサー計測・モデルを開発し、複数品種で検証しており、光合成形質の取得手法が中心である。
abstractThis study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability.
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.
Why it matches plant phenotyping methodsSIF衛星観測とqL測定を統合した作物生理・収量推定モデルを開発し、葉レベル測定およびGPP・郡別収量で技術検証しており、植物状態の取得・推定手法が中心である。
abstractWe 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.
Leaf gas exchange and chlorophyll fluorescence parameters (PGE-CFPs), which respond significantly and quickly to environmental stresses, have been used to assess the early responses of crop physiology to stresses. Most spectral estimations only focus on crop photosynthetic characteristics under a single environmental stress. Thus, the methods proposed previously are not suitable for the estimations under combined stresses (i.e., nitrogen and salt). In this research, the leaf spectral features of forage rape ( Brassica napus L.) under nitrogen stress (NSpe) and salt stress (SSpe) were fused to increase the accuracy of the spectral estimation of photosynthetic characteristics of forage rape under combined stresses in arid region of Xinjiang, China. The results showed that PGE-CFPs' spectral features were extracted with SPA (successive projections algorithm) after preprocessing. Among the SSpe- and NSpe-based models, the RF (random forest) models had higher estimation accuracy than the PLSR (partial least squares regression) and BPNN (backpropagation neural network) models. Specifically, the RF models had a PGE-CFPs estimation accuracy of 0.597-0.712, 0.640-0.715, and 0.377-0.461 under nitrogen stress (NS), salt stress (SS), and NS*SS, respectively. After fusing NSpe and SSpe, the accuracy in estimating PGE-CFPs of forage rape under NS, SS, and NS*SS were 0.729-0.755, 0.667-0.768, and 0.621-0.689, respectively. Then, the constructed models were further validated using field data, and the accuracy obtained was in the range of 0.585-0.711. Therefore, the feature fusion modeling method proposed has strong transferability and applicability. This research will offer a technical reference for crop photosynthesis monitoring at the early stage of environmental stresses.
Why it matches plant phenotyping methods分光特徴量の融合と機械学習により、複合ストレス下の植物光合成特性を推定する手法を開発・検証しており、表現型取得・推定が研究の中心である。
abstractthe methods proposed previously are not suitable for the estimations under combined stresses (i.e., nitrogen and salt).
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.
Why it matches plant phenotyping methodsChlF全曲線からICAとSVMでイネの干ばつストレス状態を推定する方法が研究の中心であり、植物の生理状態を直接評価するフェノタイピング手法に該当する。
abstractThis study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 13 Sept 2026
Abstract Boron (B) is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. In this study, we evaluated the potential of hyperspectral radiometry combined with machine learning to identify B-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under B treatments ranging from 0.5 to 8 ppm, measuring leaf B accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance (R 380 –R 1100 nm). Our results revealed substantial genotypic variation in B exclusion, with some genotypes maintaining low leaf B content despite high external concentrations. Classification models (Partial Least Squares Discriminant Analysis and Random Forest classification) outperformed regression models (Partial Least Squares Regression and Random Forest regression) in distinguishing B-excluding genotypes, achieving moderate to high accuracy within just eight days after stress initiation. Vegetation indices such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), Structure Insensitive Pigment Index (SIPI), and Chlorophyll Index (CI) indicated that B stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf B content proved more challenging, simulations showed that even modest prediction accuracies can substantially boost genetic gains if larger populations are screened, and selection intensities are increased. These findings underscore the value of hyperspectral radiometry for high-throughput phenotyping, allowing breeders to rapidly identify and advance B-tolerant rootstocks.
Why it matches plant phenotyping methodsブドウ台木のホウ素耐性を対象に、ハイパースペクトル計測と機械学習による表現型スクリーニング手法を開発・評価しており、表現型取得と分類が研究の中心である。
abstractwe evaluated the potential of hyperspectral radiometry combined with machine learning to identify B-tolerant rootstocks rapidly and cost-effectively.
Tea (Camellia sinensis L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan’s annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and quality, necessitating an accurate real-time monitoring system to enhance plantation management and production stability. This study surveys tea plantations at low, mid-, and high elevations in Nantou County, central Taiwan, collecting data from 21 fields using conventional farming methods (CFMs), which emphasize intensive management, and agroecological farming methods (AFMs), which prioritize environmental sustainability. This study integrates leaf area index (LAI), photochemical reflectance index (PRI), and quantum yield of photosystem II (ΦPSII) data with unmanned aerial vehicles (UAV)-derived visible-light and multispectral imagery to compute color indices (CIs) and multispectral indices (MIs). Using feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters. The results indicate that LAI was generally lower in AFMs, suggesting reduced leaf growth density and potential yield differences. However, PRI and ΦPSII values revealed greater environmental adaptability and potential long-term ecological benefits in AFMs compared to CFMs. Among regression models, MIs provided greater stability for tea plant physiological parameters, whereas feature ranking methods had minimal impact on accuracy. XGBoost outperformed all models in predicting parameters, achieving optimal results for (1) LAI: R2 = 0.716, RMSE = 1.01, MAE = 0.683, (2) PRI: R2 = 0.643, RMSE = 0.013, MAE = 0.009, and (3) ΦPSII: R2 = 0.920, RMSE = 0.048, MAE = 0.013. Overall, we highlight the effectiveness of integrating gradient boosting models with multispectral data to capture tea plant physiological characteristics. This study develops generalizable predictive models for tea plant physiological parameter estimation and advances non-contact crop physiological monitoring for tea plantation management, providing a scientific foundation for precision agriculture applications.
Why it matches plant phenotyping methodsUAV画像・マルチスペクトルデータと機械学習により、茶植物のLAI、PRI、ΦPSIIを推定する非接触フェノタイピング手法を開発・評価しており、方法が研究の中心です。
abstractUsing feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters.
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.
Why it matches plant phenotyping methods複数センサーを組み合わせ、根の活動・吸水、茎流、光合成などの植物状態を連続的かつ空間・時間分解して取得する監視手法が研究の中心であり、単なる routine measurement ではない。
abstractwe 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.
Conventional isotope applications in plant ecophysiology measure isotope ratios (e.g. δ2H, δ13C) of whole molecules. However, it is well established that isotope abundance varies AMONG the CH groups of metabolites (isotopomers), because they are biochemically distinct. This variation reflects enzyme isotope fractionations and encodes metabolic information, but it is unclear how these fractionations get transferred into signals that can be recovered from archives of plant material.Here, we will describe physical and biochemical mechanisms of hydrogen isotope fractionation in plants and compare their magnitudes. Based on observations for hydrogen isotope transfer in plants, we present a model for the extraction of H isotope signals from plant archives.Plant responses to increasing CO2 are critical for plant productivity and as climate feedbacks. As CO2 is the substrate for photosynthesis, plants should benefit from increasing CO2, but the magnitude of this “CO2 fertilization” disagrees with biomass estimates. Photorespiration is a side reaction of photosynthesis that reduces C assimilation in most vegetation, therefore its response under climate change is critical for the future C cycle. Photorespiration should be reduced by increasing CO2 yet exacerbated by rising T, but its response is not well captured in models, adding large uncertainty to C cycle predictions.To retrieve ecophysiological signals from plant archives, we use manipulation experiments to develop proxies for plant C fluxes, based on intramolecular abundance variation of 2H and 13C, detected by NMR. We then retrieve these proxies from archives such as tree-ring series, to derive metabolic responses over long time scales, and to improve global vegetation models.Here we will describe progress in tracking isotopomer signals from controlled experiments to plant archives, and results on long-term trends of photorespiration in response to increasing atmospheric CO2 for two globally important ecosystems. In Sphagnum species, we link trends in photorespiration to the C sink of boreal peatlands. In the tropical tree species Toona ciliata, we describe long-term trends in photosynthetic efficiency.As intrinsic quantities, isotope data are well suited to report on metabolic shifts, but not about fluxes in absolute numbers. Therefore we use isotopomer data as input for the LPJ-GUESS Ecosystem Model, to translate isotopomer-derived changes in photorespiration into trends in ecosystem C fluxes.References:Augusti A. et al (2008) Chem. Geol. 252, 1-8, doi 10.1016/j.chemgeo.2008.01.011Ehlers I. et al (2015) PNAS 112, 15585-15590 doi 10.1073/pnas.1504493112Walker AP. et al (2021) New Phytol 229, 2413-2445 doi 10.1111/nph.16866Serk H. et al (2021) Scientific Reports 11, 24517 doi 10.1038/s41598-021-02953-1Zwartsenberg SA. et al (2025) New Phytol in press.
Why it matches plant phenotyping methods植物アーカイブから光合成・光呼吸などの生理状態を推定する同位体・NMRプロキシとモデルを開発・適用しており、植物表現型の取得・抽出が中心である。
abstractTo retrieve ecophysiological signals from plant archives, we use manipulation experiments to develop proxies for plant C fluxes, based on intramolecular abundance variation of 2H and 13C, detected by NMR.
A bstract The Farquhar-von-Caemmerer-Berry (FvCB) model is the most widely-used mechanistic model of C 3 net CO 2 assimilation, and it plays a significant role in plant physiology, ecology, climate science, and Earth system modeling. As use of the model has grown, multiple variants have appeared across publications. Although many of these are commonly used, there has not been a detailed investigation of existing variants and their impacts on results and interpretations. Here we summarize the types of variants and their prevalence in the literature, and we present a comprehensive comparison of differences between them. A key finding is that a common variant that uses the minimum of assimilation rates rather than the minimum of carboxylation rates, which we call the “min- A variant,” makes different predictions than the original “min- W variant,” yet appears in approximately half of highly-cited publications and software tools that use the FvCB model. Another concern is that although leaf biochemistry restricts the range of CO 2 partial pressures where limitations due to triose phosphate utilization (TPU) can occur, this restriction is commonly omitted from the model’s equations. Among other potential issues, these variations can introduce errors exceeding 20% when estimating photosynthetic parameter values from CO 2 response curves. It is therefore important to be aware of this source of error when fitting the model, to avoid using the min- A variant, and to include the biochemically-derived CO 2 threshold for TPU limitations. C entral T heme of the M anuscript The Farquhar-von-Caemmerer-Berry model of CO 2 assimilation plays a key role in plant research, but many publications use variants of the model that differ from the original and can potentially introduce errors in photosynthetic parameter estimates. N ovel R esults , I deas, or M ethods Using a literature survey, FvCB model variants are categorized, and some are found to make contradictory predictions. Comparisons against A - C i curves show that the “min- W variant” exhibits the best performance, especially at low CO 2 concentrations.
Why it matches plant phenotyping methodsFvCBモデルの変種を比較し、CO2応答曲線から光合成パラメータを推定する手法の性能と誤差を検証しており、植物生理形質の推定方法が中心である。
abstractComparisons against A - C i curves show that the “min- W variant” exhibits the best performance, especially at low CO 2 concentrations.
High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit light capture and overall yield potential. In response, some maize plants naturally reorient their canopies to optimize light capture, a process known as canopy reorientation. Understanding this adaptive response and its impact on light capture is crucial for maximizing agricultural yield potential. This study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown maize with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception. In particular, using 3D point clouds derived from field data, virtual fields for a diverse set of maize genotypes were constructed and validated against field PAR measurements. Using this framework, we present detailed analyses of the impact of canopy orientations, plant and row spacings, and planting row directions on PAR interception throughout a typical growing season. Our findings highlight significant variations in light interception efficiency across different planting densities and canopy orientations. By elucidating the relationship between canopy architecture and light capture, this study offers valuable guidance for optimizing maize breeding and cultivation strategies across diverse agricultural settings.
Why it matches plant phenotyping methods圃場トウモロコシの3D再構成とPARモデルを統合し、圃場測定で検証した再利用可能な表現型取得・解析フレームワークが研究の中心である。
abstractThis study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown maize with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500–640 nm and 740–1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R2) of 0.70 and a low mean bias error (MBE) of 0.04 mg g−1. The Cubist model performed even better, demonstrating an R2 of 0.77 and an exceptionally low MBE of 0.01 mg g−1. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.
Why it matches plant phenotyping methods葉のクロロフィル含量という植物形質を、Vis/NIR分光とケモメトリクスで非破壊推定する手法を開発・検証しており、表現型取得が中心である。
abstractThis study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Vegetative indices (VIs) are widely used in high-throughput phenotyping (HTP) for the assessment of plant growth conditions; however, a range of VIs among diverse soybeans is still an unexplored research area. For this reason, we investigated a range of four major VIs: normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), anthocyanin reflectance index (ARI), and change to carotenoid reflectance index (CRI) in diverse soybean accessions. Furthermore, we ensured the correct positioning of the region of interest (ROI) on the soybean leaf and clarified the effect of choosing different ROI sizes. We also developed a Python algorithm for ROI selection and automatic VIs calculation. According to our results, each VI showed diverse ranges (NDVI: 0.60-0.84, PRI: -0.03 to 0.05, ARI: -0.84 to 0.85, CRI: 2.78-9.78) in two different growth stages. The size of pixels in ROI selection did not show any significant difference. In contrast, the shaded part and the petiole part had significant differences compared with the non-shaded and tip, side, and center of the leaf, respectively. In the case of the Python algorithm, algorithm-derived VIs showed a high correlation with the ENVI software-derived value: NDVI -0.97, PRI -0.96, ARI -0.98, and CRI -0.99. Moreover, the average error was detected to be less than 2.5% in all these VIs than in ENVI.
Why it matches plant phenotyping methods大豆葉のハイパースペクトル画像から植生指数を抽出するROI自動選択・計算法を開発し、既存ソフトウェアとの相関および誤差で検証しており、植物表現型取得法が中心である。
abstractWe also developed a Python algorithm for ROI selection and automatic VIs calculation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based on the parameter optimization components of the popular AI framework PyTorch. The primary novelty of the software lies in its computational efficiency, robustness of parameter estimation, and flexibility in handling different types of response curves and sub-model functional forms. PhoTorch can fit both steady-state and non-steady-state gas exchange data with high efficiency and accuracy. Its flexibility allows for optional fitting of temperature and light response parameters, and can simultaneously fit light response curves and standard $$A/C_i$$ curves. These features are not available within presently available $$A/C_i$$ curve fitting packages. Results illustrated the robustness and efficiency of PhoTorch in fitting $$A/C_i$$ curves with high variability and some level of artifacts and noise. PhoTorch is more than four times faster than benchmark software, which may be relevant when processing many non-steady-state $$A/C_i$$ curves with hundreds of data points per curve. PhoTorch provides researchers from various fields with a reliable and efficient tool for analyzing photosynthetic data. The Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch .
Why it matches plant phenotyping methods植物の光合成ガス交換データから生理形質を推定する専用ソフトウェアの開発であり、表現型取得・解析手法が中心である。
abstractThis work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based on the parameter optimization components of the popular AI framework PyTorch.
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.
Why it matches plant phenotyping methods植物の3D構造と蛍光機能情報を取得・統合する画像計測手法の開発が中心であり、植物病害の非侵襲的フェノタイピングに該当します。
abstractWe show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion.
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 match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2025Computers and Electronics in Agriculture.
Reconstructing 3D architecture of cucumber populations for multi-scale phenotypic analysis poses significant challenges in greenhouse crop research. Cucumber canopy architecture directly impacts light interception and the plant growth conditions. Terrestrial Laser Scanning (TLS) was employed to capture the 3D point cloud of cucumber plants at various growth stages, named as real plant canopy (RPC). A novel method, CP-FEC-RG, combining Fast Euclidean Clustering with Region Growing algorithm, was developed to segment cucumber plants and extract phenotypic traits both at plant and leaf scales. The virtual plant canopies (VPCs), namely VPC-H, VPC-M and VPC-L were constructed representing high, medium, and low growth potentials based on the data collected via TLS. A radiative transfer model was adopted to compare the radiation interception capabilities of both RPC and VPCs. An average recall rate of 92.2% was achieved for leaf segmentation. Growth differences were observed among the segmented individual plants and leaves, with coefficients of variations for phenotypic traits ranging from 0.13 to 0.48 for individual plants and from 0.21 to 0.54 for leaves. For daily cumulative light interception, VPC-L showed a reduction of 17.1% compared to RPC, whereas VPC-M and VPC-H exhibited increases of 18.2% and 30.1%, respectively. These findings highlight the importance of using the RPC for the accurate calculations of light interception and provide a solid foundation for applying TLS in the 3D phenotypic analysis of crops in solar greenhouses.
Why it matches plant phenotyping methodsTLSによる3D形状取得、植物・葉の分割、表現型形質抽出手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractA novel method, CP-FEC-RG, combining Fast Euclidean Clustering with Region Growing algorithm, was developed to segment cucumber plants and extract phenotypic traits both at plant and leaf scales.
Stomatal conductance (g s ) quantifies the rate of exchange of carbon dioxide for photosynthesis and water vapor for transpiration between plant leaves and the atmosphere. g s is usually measured by handheld devices like porometers , and readings are manually taken in the field, which is time-consuming and labor-intensive. In this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling. The experiment was conducted in a research field equipped with an HTP platform in 2020 and 2021 involving maize, sorghum, soybean, sunflower , and winter wheat . Weather variables including dew point temperature, wind speed , air temperature, solar radiation, and relative humidity were collected by an onsite weather station . Plot-level canopy temperature, soil temperature , and seven vegetation indices were acquired using a thermal infrared camera, a multispectral camera, and a visible near-infrared spectrometer integrated on the HTP platform. Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for g s , and model performance was evaluated by Coefficient of Determination (R 2 ) and Root Mean Squared Error (RMSE). The result showed that RFR and SVR outperformed PLSR in g s modeling. The RFR model achieved R 2 of 0.63 and RMSE of 0.16 mol m −2 ·s −1 with the combination of phenotyping data and weather data. It outperformed the model using only the weather data (R 2 =0.35 and RMSE=0.21 mol m −2 ·s −1 ), or the model using only the phenotyping data (R 2 =0.46 and RMSE=0.19 mol m −2 ·s −1 ). This result suggested that high-throughput plant phenotyping data effectively complement weather data in estimating g s rapidly and non-destructively through ML. With the wide adoption of HTP technologies in aerial and ground-based platforms, this research provides a practical framework to estimate g s at large scale for crop breeding and irrigation management .
Why it matches plant phenotyping methodsHTPセンサーデータと機械学習を用いて、植物の生理形質である気孔コンダクタンスを大規模・非破壊推定する方法が研究の中心であり、モデル性能も評価している。
abstractIn this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling.
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.
Why it matches plant phenotyping methods3D放射伝達モデルと蛍光モデルを用いて、作物の光合成系レベルの蛍光量子効率を推定する手法を開発し、地上・航空観測で検証・適用している。植物生理状態の取得手法が研究の中心である。
abstractOur proposed method for scaling TOC SIF down to photosystems' (PSI and PSII) level uses a three-dimensional (3D) modeling approach
• Integral difference model best fits the spatiotemporal variation of FIPAR in canopy. • Photometric sensor could monitor the spatiotemporal variation of crop canopy and PAR. • Monitoring the spatiotemporal variability of PAR in canopy represents crop growth. • The spatiotemporal variation of FIPAR is positively correlated with growth traits. Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (PH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for PH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with PH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity.
Why it matches plant phenotyping methods光量子センサーでキャノピーFIPARを取得し、時空間モデルで作物成長形質を推定する測定技術の開発・検証が研究の中心である。
abstractThe photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth.
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.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-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.
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 match · UnverifiedEurope PMC · checked 15 Sept 2026
In drier zones pearl millet faces moderate to severe drought stress at seedling stage leading to mortality and poor plant stand. An effective and rapid drought screening protocol as well as tolerant genotypes are required to address the issue. Hydroponics provide an uniform stress environment which we utilized in the study to create dehydration stress in pearl millet seedlings through root dehydration. Stress duration for 7 days @ 6 h/day differentiated the tolerant and susceptible genotypes. Pearl millet minicore collection of 207 genotypes were divided in to four groups based on seedlings traits measured under stress. Phenotypic response of seedlings to dehydration stress measured through drought score showed moderate correlation with quantum yield and fresh shoot weight. In all, 32 genotypes showed tolerant reaction to seedling drought stress. Both tolerant and susceptible genotypes identified under hydroponics performed at par under soil-pot conditions. Genotypes IP 3642, IP 20995, IP 10665, IP 1556, IP 2322, IP 8562, IP 18579, IP 8472, IP 5711, IP 277, IP 11268 showed better seedling growth parameters under rapid dehydration through hydroponics as well in progressive soil drying under pot conditions.
Why it matches plant phenotyping methodsハイドロポニクスによる幼苗脱水ストレスの迅速な耐 drought スクリーニングプロトコルを構築し、土壌ポット条件でも検証しており、表現型取得法が研究の中心である。
abstractAn effective and rapid drought screening protocol as well as tolerant genotypes are required to address the issue.
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.
Why it matches plant phenotyping methods綿花葉のクロロフィル蛍光からリン栄養状態を推定する生理フェノタイピング手法を開発・検証しており、PLSRモデルの検証も中心的に行っている。
abstractit is necessary to develop methods for early assessment of P status in the LSCB
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.
Why it matches plant phenotyping methods乾燥耐性を評価する標準化された生理フェノタイピング系を構築し、複数の形質をスクリーニングして予測指標・判定閾値を検証しているため、方法が研究の中心である。
abstractWe 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.
Nitrogen (N) is a vital plant element, affecting plant physiological processes, carbon and water fluxes and ultimately crop yields. However, N uptake by crops can vary over fine spatiotemporal scales, and optimising the application of N-fertiliser to maximise crop performance is challenging. To investigate the potential of spatially mapping the impact of N fertiliser application on crop physiological performance and yield, we leverage both optical and thermal data sampled from drone platforms and ground-level leaf measurements, across a range of different N, Sulphur (S) and sucrose treatments in winter wheat. Using leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression (PLSR; R2= 0.93, P < 0.001). Leaf photosynthetic capacity (Vcmax) exhibited a strong linear relationship with leaf chlorophyll (R2 = 0.77; P < 0.001). Using drone-acquired MERIS terrestrial chlorophyll index (MTCI) values as a proxy for leaf chlorophyll (R2 = 0.76; P < 0.001), Vcmax was spatially mapped at the centimetre-scale. Thermal drone and ground measurements demonstrated that N application leads to cooler leaf temperatures, which led to a strong relationship with ground-measured leaf stomatal conductance (R2= 0.6; P < 0.01). Final grain yield was most accurately predicted by optical reflectance (MTCI, R2 = 0.94; P < 0.001). Precise retrieval of leaf-level crop performance indicators from drones establishes significant potential for optimising fertiliser application, to reduce environmental costs and improve yields.
Why it matches plant phenotyping methodsドローンの光学・熱画像と回帰モデルを用いて、葉クロロフィル、Vcmax、気孔コンダクタンス、収量などの植物形質を空間推定・検証しており、形質取得手法が研究の中心である。
abstractUsing leaf level hyperspectral reflectance data, leaf chlorophyll content was accurately modelled across fertiliser treatments via partial least squares regression
Background The chlorophyll content has a strong influence on plant photosynthesis and crop growth and is a key factor for understanding the functioning of farming systems. Therefore, the accurate estimation of chlorophyll content (Cab) is important in precision agriculture. In this study, the three-dimensional radiative transfer model (3DRTM) was used to calculate the radiative transfer and simulate the canopy hyperspectral image of a rice field. Then, a physically based joint inversion model was developed using an iterative optimization approach with penalty function and a priori information constraints to estimate chlorophyll content efficiently and accurately from the hyperspectral curve of a rice canopy. Results The inversion model demonstrates that the sparrow search algorithm (SSA) can estimate rice Cab, providing relatively satisfactory Cab estimation outcomes. In addition, the inversion of the SSA method with or without carotenoids content (Car) constraints was compared, and compared to the inversion of Cab without Car constraints [coefficient of determination (R 2 ) = 0.690, root mean square error (RMSE) = 7.677 µg/cm 2 )], the SSA with constraints was more accurate (R 2 = 0.812, RMSE = 5.413 µg/cm 2 ). Conclusions The Large-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes (LESS) exhibited higher accuracy in estimating the rice Cab compared to the 1DRTM PROSAIL model, which is constituted by coupling the Leaf Optical Properties Spectra (PROSPECT) model and the Scattering by Arbitrarily Inclined Leaves (SAIL) model. The 3DRTM is conducive to precisely estimating Cab from the hyperspectral data of the rice canopy, thereby holding great potential for precise nutrient management in rice cultivation.
Why it matches plant phenotyping methodsイネ群落のハイパースペクトル画像から葉緑素含量を推定する3D放射伝達モデルと逆解析手法を開発・比較しており、植物形質取得が研究の中心である。
abstracta physically based joint inversion model was developed using an iterative optimization approach with penalty function and a priori information constraints to estimate chlorophyll content efficiently and accurately from the hyperspectral curve of a rice canopy.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
The amount of absorbed light is one of the main factors governing plant photosynthesis, and ultimately, the gross primary production (GPP) of vegetation. Since canopy chlorophyll (Chl) content defines the amount of light that can be absorbed (thus the amount of energy available for photosynthesis), it is representative of the status of the photosynthetic apparatus and directly relates with vegetation productivity. The non-invasive assessment of these traits is the foundation of proximal and remote sensing and of high-throughput phenotyping of plants. The goal of this study is to explore: (i) the response of GPP to the absorption coefficient of Chl derived from canopy reflectance (i.e., assessed in situ) across the PAR and red-edge spectral regions in two plant species with contrasting biochemistry, structural properties, and photosynthetic pathway; (ii) the efficiency of contrasting plants in absorbing radiation and converting it into photosynthetic carbon uptake. The spectral composition of light absorbed by vegetation and the contribution of each spectral range to GPP were quantified. The highest responses of GPP to the Chl absorption coefficient occurred in the red-edge and green spectral regions. More notably, in contrasting plant species the GPP responses in the visible and red-edge spectral regions were almost identical and close to the quantum yield of CO 2 fixation. This potentially opens a novel avenue for the remote assessment of the quantum yield of photosynthesis. The uncertainty of the relationship between GPP and Chl absorption coefficient and its impact on the estimation of photosynthetic rates was also quantified.
Why it matches plant phenotyping methodsキャノピー反射スペクトルからクロロフィル吸収係数を非侵襲推定し、GPP・光合成能力との関係と不確実性を評価する測定手法が研究の中心であるため、植物フェノタイピング手法の応用・評価として採用する。
abstractThe non-invasive assessment of these traits is the foundation of proximal and remote sensing and of high-throughput phenotyping of plants.
Although photosynthetic response to light has been extensively studied at the single-leaf level, little is known about the response at the whole-plant level. The present study aims to reveal the differences in the photosynthetic response to light under steady and non-steady states between the single leaf and whole plant in Arabidopsis thaliana and to investigate the mechanisms underlying these differences with respect to leaf aging. First, we developed an open system for gas exchange measurement of the whole plant of Arabidopsis. It enabled the photosynthetic response to dynamic environmental changes to be directly compared between the single leaf and whole plant. The photosynthetic response to the fluctuating light did not differ significantly between the single leaf and whole plant. This result is partly confirmed by the fact that the leaves at different ages showed no difference in the photosynthetic induction after a step change in light. On the other hand, light response analysis for steady-state photosynthesis showed a higher apparent quantum yield in the whole plant than in the single leaf. This difference might be attributed to the difference in the efficiency of light absorption and/or utilization of absorbed light among the leaves at different ages.
Why it matches plant phenotyping methods全植物の光合成応答を測定・比較する開放型ガス交換システムを開発し、その測定法を用いて単葉と全植物の生理形質を評価しているため、植物フェノタイピング手法が研究の中心である。
abstractFirst, we developed an open system for gas exchange measurement of the whole plant of Arabidopsis.
Quantifying the effect of factors controlling CO 2 assimilation is crucial for understanding plant functions and developing strategies to improve productivity. Methods exist in numerous variants and produce various indicators, such as limitations, contributions, and sensitivity, often causing confusion. Simplifications and common mistakes lead to overrating the importance of diffusion-whether across stomata or the mesophyll. This work develops a consistent set of definitions that integrates all previous methods, offering a generalised framework for quantifying restrictions. Ten worked examples are provided in a free downloadable spreadsheet, demonstrating the simplicity and applicability to a wide range of questions.
Why it matches plant phenotyping methods植物のCO2同化に関する光合成制限を定量化するための定義・統合フレームワークを開発しており、光合成状態の推定手法が研究の中心である。
abstractThis work develops a consistent set of definitions that integrates all previous methods, offering a generalised framework for quantifying restrictions.
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.
Why it matches plant phenotyping methods植物の光合成状態を測定する高時間分解クロロフィル蛍光法を、農薬による植物損傷の評価・環境バイオモニタリング手法として検証しており、表現型取得が中心です。
abstractDamage to plants at the photosynthetic apparatus level was evaluated by measuring the high temporal resolution variable chlorophyll fluorescence (OJIP test).
ATR-Fourier transform infrared spectroscopy was used to determine the carbon and nitrogen content in the leaves of herbaceous forest plant species and functional traits associated with the leaf economic spectrum (one of the two-dimensional global spectrum of plant form and function), and monitoring plant physiological status under elevated temperature conditions. The content of carbon and nitrogen determined by traditional methods validated the accuracy of ATR-FTIR method. It was also shown that in the case of forest herbs, the ATR-FTIR method is an efficient tool for determining functional traits (such as specific leaf area (SLA) and leaf dry matter content (LDMC)) related to the leaf economics spectrum, and to diagnose the photophysiological state of plants after changes of temperature (changes of day/night temperature from 21/13 °C to 25/17 °C). Measuring the areas of three absorption bands of the ATR-FTIR spectra related to amides I and II (between 1700 cm -1 and 1500 cm -1 ), carbohydrates (cellulose and hemicellulose; between 1200 cm -1 - 850 cm -1 ) and amide III (between 1290 cm -1 and 1190 cm- 1 ) allowed for determination of all analysed chemical and functional properties of leaves. Based on selected absorption bands accurately estimated C and N content, with coefficients of correlation (r) of 0.88 for C and 0.84 for N. SLA and LDMC were also predicted, with r values of 0.88 and -0.91, respectively. Moreover, ATR-FTIR proved to be a rapid, non-destructive tool for monitoring the plant physiological status, as demonstrated by the significant correlation (r = 0.99) between the chlorophyll fluorescence performance index (PI) and ATR-FTIR data. ATR-FTIR has been demonstrated as an efficient tool for simultaneous quantification of leaf carbon and nitrogen content, economic functional traits, and physiological status of forest herb plants.
Why it matches plant phenotyping methodsATR-FTIRを用いて葉の化学的・機能的形質および生理状態を非破壊推定し、従来法やクロロフィル蛍光との相関で精度検証しており、植物表現型取得法が中心である。
abstractThe content of carbon and nitrogen determined by traditional methods validated the accuracy of ATR-FTIR method.
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).
Why it matches plant phenotyping methods低コストの蛍光・NDVIセンサーを用いた圃場での作物表現型取得と品種評価が研究の中心であり、光合成性能や蛍光形質の測定法を実質的に適用している。
titleIn-Field Phenotyping Using the Low-Cost and Open Access Fluorescence PhotosynQ Multispeq Sensor Together with NDVI: A Case Study with Durum Wheat
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 13 Sept 2026
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.
Why it matches plant phenotyping methods分光法とポロフルオロメトリーによる葉の形態・生理形質の高スループット推定を開発・評価し、予測精度と頑健性を検証しているため、植物フェノタイピング手法が中心である。
abstractThis study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits
Hyperspectral remote sensing (RS) has demonstrated to be useful for estimating vegetation photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and the electron transport rate (Jmax). However, the spectral ranges used by RS models for predicting photosynthetic traits vary, and their predictive performance across different crop varieties is poor. This study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability. Leaf-level measurements linked high-throughput spectral reflectance observations with photosynthetic traits obtained via a portable photosynthesis system. Results revealed strong correlations between the green and red edge bands and photosynthetic traits. Among newly developed vegetation indices, the structure insensitive pigment index [SIPI (850,691,476) ] and SIPI (850, 699, 579) effectively predicted Vcmax and Jmax, respectively. In contrast, partial least squares regression (PLSR) modeling combined with reflectance from 400 to 1000 nm outperformed in estimating photosynthetic traits of tea chrysanthemum and exhibited excellent performance in a multi-variety validation dataset, indicating applicability across different varieties. Our findings suggest that increasing the modeling dataset's variability enhances the universality of RS estimation models for photosynthetic traits, providing a valuable tool for breeders to efficiently collect photosynthetic trait information.
Why it matches plant phenotyping methods茶キクの光合成形質を高スループット葉面ハイパースペクトル反射から推定するモデルを開発し、品種横断データで性能検証しているため、形質取得法が研究の中心である。
abstractThis study aimed to investigate whether augmenting the modeling dataset's variability through various nitrogen experiments on tea chrysanthemum could improve RS-based photosynthetic trait models' applicability.
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.
Why it matches plant phenotyping methodsDARTとFluspect-CXを統合し、作物の光合成系レベルの蛍光量子効率を推定するセンシング・解析手法を開発し、地上および航空観測で検証・適用している。植物生理状態の取得が研究の中心である。
abstractHere, 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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
• UAV-derived canopy model quantified radiation availability of intercropped soybean. • Shadow fraction method was developed to calculate direct, diffuse radiation and RUE. • RUE of intercropped soybean was higher than that in monoculture. • Fraction of diffuse in intercropping was slightly lower than that in monoculture. • Other factors leading to the higher RUE of soybean in intercropping systems. Shading is an unavoidable phenomenon in strip intercropping systems for lower crops, which affects the amount and component of solar radiation, and thus the radiation use efficiency (RUE). The higher crop is usually treated as a homogeneous block instead of the actual canopy structure to calculate lower crop radiation availability (block-based method, BM), which underestimates the amount of light passing through gaps in the canopy. Here we proposed a new shadow fraction method (SFM) to separately quantify direct and diffuse radiation on lower crops. The SFM considered shadow fraction dynamic and view factor within a day, which was calculated based on UAV-derived canopy structural models. To test this method, UAV images and crop data were collected from a maize-soybean intercropping experiment with six planting configurations. For daily total radiation, as the width of the soybean strip decreased from 3.8 m to 1.6 m, the relative difference between BM and SFM increased from about 11.10% to 20.36%. Accordingly, the RUE of soybean calculated by the SFM was 0.2–0.3 g/MJ lower than the BM. Consistent with previous studies, the RUE of soybean in strip intercropping systems (1.36–1.61 g/MJ) calculated by the SFM was higher than that in monoculture (0.98 g/MJ). The higher RUE was usually attributed to the increasing fraction of diffuse in strip intercropping systems. However, SFM showed that the fraction of diffuse on intercropped soybean (ranged from 37.42% to 38.58%) was slightly lower than that in monoculture (39.48%), implying that other factors, such as light intensity and quality, may have an impact on soybean performance and warrant further investigation. The SFM was theoretically more accurate than BM as it considered the actual 3D canopy structure. This method can enhance the understanding of light distribution and use efficiency in intercropping systems, which can be integrated with crop growth models or functional structural plant models to optimize intercropping configurations for improved resource use efficiency.
Why it matches plant phenotyping methodsUAV由来の3Dキャノピー構造モデルを用いて、下層作物の光環境を推定する新しいshadow fraction法を開発・検証しており、植物キャノピー構造と放射利用効率の定量化が研究の中心である。
abstractHere we proposed a new shadow fraction method (SFM) to separately quantify direct and diffuse radiation on lower crops.
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with Rubisco carboxylation ( V cmax ) and electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world’s most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through steady-state methods ( r 2 =0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43-46% and 56-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to elevated temperatures.
Why it matches plant phenotyping methods作物の生理形質（V cmax、J max）を迅速に取得する動的同化法を開発・検証し、定常状態法との比較で妥当性を評価しているため、植物フェノタイピング手法が中心である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (gs), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (gs and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm2), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale.
Why it matches plant phenotyping methodsハイパースペクトル計測とPROSPECT-D逆解析による植物機能形質の推定、および水ストレス早期検出モデルの開発・評価が研究の中心である。
abstractleaf-level hyperspectral measurements were recorded over a ten-week period
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.
Why it matches plant phenotyping methods携帯型蛍光センシングと作物モデルを統合し、葉の光合成能力とバイオマスを推定する測定・解析ワークフローが研究の中心であるため、植物表現型計測法の応用として採用する。
abstractThis 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.
Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch. The primary novelty of the software lies in its computational efficiency, robustness of parameter estimation, and flexibility in handling different types of response curves and sub-model functional forms. PhoTorch can fit both steady-state and non-steady-state gas exchange data with high efficiency and accuracy. Its flexibility allows for optional fitting of temperature and light response parameters, and can simultaneously fit light response curves and standard A/Ci curves. These features are not available within presently available A/Ci curve fitting packages. Results illustrated the robustness and efficiency of PhoTorch in fitting A/Ci curves with high variability and some level of artifacts and noise. PhoTorch is more than four times faster than benchmark software, which may be relevant when processing many non-steady-state A/Ci curves with hundreds of data points per curve. PhoTorch provides researchers from various fields with a reliable and efficient tool for analyzing photosynthetic data. The Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch.
Why it matches plant phenotyping methods光合成ガス交換データから生理形質を推定するモデルフィッティングソフトウェアの開発・ベンチマークが中心であり、植物フェノタイピング手法に該当する。
abstractThis work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch.
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 Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch .Open asset ↗GEMINI-Breeding/photorchlines:1-57Plant phenotyping relevance match · UnverifiedbioRxiv · checked 13 Sept 2026
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.
Why it matches plant phenotyping methods単一細胞のクロロフィル蛍光イメージングと機械学習を組み合わせ、光ストレス応答などの植物生理形質を抽出・評価する分析フレームワークが研究の中心である。
abstractWe introduce a single-cell analysis framework that enables the study of intercellular heterogeneity of photosynthetic traits
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
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.
Why it matches plant phenotyping methods植物の蛍光スペクトル画像を分光器なしで推定する撮像・計算法を開発し、分光放射計との比較で信頼性を検証しており、植物表現型取得法が中心です。
abstractThis study proposes a method for estimating the spectral images of fluorescence spectral distributions emitted from plant grains and leaves without using a spectrometer.
Agriculture is the largest consumer of freshwater, accounting for approximately 70% of the total global usage. As the human population continues to grow, demand for water will be exacerbated by a changing climate and shifting temperature and precipitation regimes. Dynamically modelling crop physiological function will be crucial to optimising crop management strategies. In this study we synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat (Triticum aestivum) in growth chambers within a controlled environment experiment under water and/or nitrogen stress conditions. Results showed that plants which had first received nitrogen fertiliser and were subsequently droughted presented the lowest water fluxes, and the lowest leaf chlorophyll content and photosynthetic capacity (Vcmax) values. Partial least squares regression (PLSR) analysis of hyperspectral reflectance data revealed key wavelengths sensitive to six different plant traits and fluxes (including relative water content, leaf nitrogen, stomatal conductance), with strong correlations between measured and modelled values (R2 = 0.84; p
Why it matches plant phenotyping methodsハイパースペクトル・熱画像データとPLSRを用いて、複数の植物形質および水フラックスを推定する手法が研究の中心であり、測定値とモデル値の相関も評価しているため。
abstractwe synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat
Water use efficiency (WUE) relates two important processes of the plant atmosphere continuum namely net carbon assimilation (via photosynthesis) and water utilization (via evapotranspiration). Our desire to trade-off WUE between accurate measurement at leaf level (WUEL) and effective implementation at plant level (WUEP) demands accurate scaling relations. Conventional mid-day, fully expanded, single-leaf measurements of WUEL are found to be poorly correlated with WUEP, thus questioning the applicability of scaling relations. This research is aimed at obtaining optimal time-window and leaf canopy position to characterize and upscale WUEL for effective field level implementation. Leaf gas exchange parameters were monitored in a rainfed Cotton field at five canopy positions for one crop cycle, and further correlated with WUEP considering individual measurements as well as their spatial averages. Optimal time-window showing highest correlation with WUEP has occurred during 15:00 to 16:00 hours irrespective of canopy leaf position and growth stage. Deviation with mid-day measurements of WUEL low during boll bursting stage (7.38 ± 4.69 %) and high during germination and seedling emergence stage (17.27 ± 5.37 %). These changes are largely attributed to stomatal regulation of water vapour via unregulated water stress conditions. Scaling relations between WUEL and WUEP are linear with correlation strengths ranging from 0.52 (west bottom) to 0.80 (plant top). At leaf level, WUE is controlled by variations in photosynthetic photon flux density (ρ = 0.80) and vapour pressure deficit (ρ = 0.78), whereas at plant level, WUE is controlled by relative humidity (ρ = 0.77) and net solar radiation (ρ = 0.85). Our findings can help in developing alternate water management strategies to improve WUE in rainfed Cotton fields of tropical humid climate.
Why it matches plant phenotyping methods葉レベルWUEを植物レベルへ拡大するため、測定時間帯と葉冠位置を最適化し、相関関係を評価することが研究の中心であり、単なる生理測定の付随利用ではない。
abstractOur desire to trade-off WUE between accurate measurement at leaf level (WUEL) and effective implementation at plant level (WUEP) demands accurate scaling relations.
Carbon balances of croplands are often assessed using models that depend critically on accurate estimates of soil carbon inputs such as crop residues, dead roots, and root exudates. Here, we develop a method to estimate soil carbon inputs by combining satellite-based gross primary productivity (GPP) estimates with harvest yields extracted from agricultural statistics. We model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites. When tested with leave-one-site-out cross validation, the model predicted the yearly GPP with a root mean squared error equal to about 10 % of the mean. We furthermore show that the predicted cumulative GPP explains 60-70 % of the observed variability within a set of 135 aboveground biomass measurements collected on 40 agricultural fields. Finally, we apply the method to three Finnish regions and estimate the soil carbon inputs as the difference between the net primary productivity (NPP), assumed 50 % of the GPP, and the carbon removed in harvest. Compared to the allometric method used in the current national greenhouse gas inventory, our annual carbon inputs are within 5-30 % for wheat but 50-100 % higher for barley, 40-50 % higher for green fallows, and 100-160 % higher for forage grasses. These results highlight a discrepancy between the current national greenhouse gas inventory and carbon budgets derived from flux measurements at eddy covariance sites.
Why it matches plant phenotyping methodsSentinel-2のスペクトル指標から作物GPPを推定する手法を開発し、サイト除外交差検証と圃場バイオマスで検証しており、植物の生産性・バイオマス推定が中心的な技術要素です。
abstractWe model the daily GPP as a statistical regression on photosynthetically active radiation and the red edge chlorophyll index measured by the Sentinel-2 satellites and train the model using data from five eddy covariance flux measurement sites.
In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor- and time-intensive due to challenging environmental conditions and highly dynamic plant processes. To enable more detailed studies on leaf angles, we modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision. We demonstrate the system's accuracy and reliability, with minimal deviation from reference values. The method can be utilized by other researchers to gather data on leaf angles and other structural plant traits at regular intervals to access the dynamics of leaves, plants, and canopies. The system's low cost and adaptability can enhance the efficiency of crop monitoring in plant breeding and phenotyping experiments. Detailed documentation and code are available on GitHub.•An open-source farming robot is retrofitted to function as an automatic data collection platform•Hard to access leaf angles can be retrieved with high accuracy•Leaf angle dynamics can be observed with high temporal resolution.
Why it matches plant phenotyping methodsステレオビジョンを用いて葉角度を高精度・高頻度に測定するロボット基盤を開発・改良し、精度と信頼性を検証しているため、植物フェノタイピング手法が中心である。
abstractwe modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision.
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 · publicAll used codes and recorded data are available at: https://github.com/FrederikHennecke/PointCloudHarvest .Open asset ↗FrederikHennecke/PointCloudHarvestlines:218-236Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Review Navigating Challenges in Interpreting Plant Physiology Responses through Gas Exchange Results in Stressed Plants Diego A. Márquez *, Anna Gardner and Florian A. Busch School of Biosciences and Birmingham Institute of Forest Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK * Correspondence: d.a.marquez@bham.ac.uk Received: 14 November 2024; Revised: 20 December 2024; Accepted: 27 December 2024; Published: 13 January 2025 Abstract: This paper explores the challenges that arise when performing and interpreting leaf gas exchange measurements in plants subjected to abiotic stress. It highlights how factors such as cuticular fluxes, stomatal closure, and common assumptions about gas exchange can lead to errors, especially under stress conditions. Key phenomena such as substomatal cavity unsaturation and stomatal patchiness during water stress are discussed in detail, as they significantly complicate the calculation of gas exchange parameters under stress. The paper also addresses the importance of other factors, including steady-state conditions, the differences between adaxial and abaxial surface responses, and boundary layer effects, all of which play critical roles in influencing the accuracy of measurements. Important physiological indicators—such as intrinsic water-use efficiency, minimum leaf conductance, substomatal CO2 concentration, and mesophyll conductance—are analysed in the context of how stress-induced discrepancies in data often result from measurement artefacts rather than true physiological differences. To address these challenges, the paper outlines practical approaches to improving measurement accuracy, offering insights on standardising experimental conditions and minimising errors. By recognising these issues, gaps in current knowledge are identified, providing a comprehensive overview of the challenges in interpreting leaf gas exchange data under stress conditions and suggesting areas for further study.
Why it matches plant phenotyping methods植物の葉ガス交換測定における誤差要因、解釈上の課題、精度改善と標準化を中心に扱う方法論レビューであり、植物生理状態の取得・評価法が中核である。
abstractThis paper explores the challenges that arise when performing and interpreting leaf gas exchange measurements in plants subjected to abiotic stress.
The Mango leaf diseases significantly restrain mango output as they affect yield and tree health. Mango leaf sooty mould disease is one of the diseases which affects the tree’s photosynthesis and general vigor quite severely. This research study looks into the use of deep learning models like the Residual Network with 50 stacks and ResNext50 for assessing that severity classification of mango leaf sooty mould disease. The model evaluates severity based on the dataset of 25,000 images obtained from different mango fields, as per the study. The overall accuracy achieved is 94.61% for the ResNext50 architecture through layer-wise parameter analysis, performance metrics, and confusion matrices. Model comparisons in the field reveal advantages across and between models. This research not only proves the use of DL in disease management but also paves the way for more applications in farming use. Automated mango leaf disease assessment is always bright white.
Why it matches plant phenotyping methodsマンゴー葉の画像から病害の重症度を推定する深層学習手法を開発・評価しており、植物の病害状態を直接定量化するフェノタイピング手法が中心である。
abstractThis research study looks into the use of deep learning models like the Residual Network with 50 stacks and ResNext50 for assessing that severity classification of mango leaf sooty mould disease.
MaizeLeafPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
A deep understanding of ammonia (NH 3 ) emissions from cropland can promote efficient crop production. To date, little is known about leaf NH 3 emissions because of the lack of rapid detection methods. We developed a method for detecting leaf NH 3 emissions based on portable NH 3 sensors. The study aimed to (i) determine the performance of the method in detecting leaf NH 3 emissions; (ii) analyze the variation of leaf NH 3 emissions with foliar rank; and (iii) elucidate the relationships between leaf NH 3 emissions and other leaf parameters. Maize (Zea mays L.) was used as the tested plant. The results showed that the NH 3 sensors had good repeatability, accuracy, and selectivity in detecting NH 3 . The response time of the method ranged 7-22 s and the NH 3 reading ranged 0.078-0.463 μmol mol -1 . Leaf NH 3 emissions were observed mainly in daytime (negligible at night). Daytime leaf NH 3 emission rates ranged 0.347-1.725 μg N cm -2 d -1 . The middle leaves (near the ear) were the major contributor to plant NH 3 -N loss. There were significant linear relationships between leaf NH 3 emission rates and other nondestructively-measured leaf parameters [e.g., SPAD (soil and plant analyzer development, which reflects the relative concentration of leaf chlorophyll), stomatal conductance, transpiration rate, and net photosynthetic rate] (p 4 + ) concentration and leaf total N concentration (p 4 + concentration, leaf total N concentration, and leaf NH 3 emission rate. Overall, nondestructively-measured leaf NH 3 emission rates can partly reflect maize growth status and provide information for N management in maize production.
Why it matches plant phenotyping methods携帯型NH3センサーによる葉のNH3放出速度測定法を開発し、性能（再現性・精度・選択性・応答時間）を評価しており、植物生理状態の取得方法が研究の中心です。
abstractWe developed a method for detecting leaf NH 3 emissions based on portable NH 3 sensors.
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.
Why it matches plant phenotyping methods葉・群落の蛍光スペクトルを用いてSIF放射伝達モデルを検証・比較する手法が研究の中心であり、植物の光合成状態に関わる生理形質を測定する。
abstractHere, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra.
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.
Why it matches plant phenotyping methods塩耐性を非破壊・高スループットに評価するため、クロロフィル蛍光とハイパースペクトル計測を組み合わせた植物フェノタイピング手法が中心である。
abstractdeveloping a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO
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.
Why it matches plant phenotyping methods葉・シュートの光合成状態を長期・現場で取得する統合測定システムを開発・実証しており、植物生理フェノタイピング手法が中心である。
abstractIn this communication we provide a first-time demonstration of long-term, in situ and combined measurements of photosynthetic gas-exchange and ChlF.
ABSTRACT Currently, the breeding programs focus their efforts on identifying and developing tolerant genotypes to adverse conditions, such as drought and high temperatures. In this context, the physiological approach, which involves phenotyping several traits, is useful for breeding programs. Leaf photosynthetic traits have become one of the main objectives to be evaluated for breeders due to their relationship with improving grain yield and biomass production. Gas exchange ( Ge ) and chlorophyll “a” fluorescence ( Chf ) are the main tools to characterize the photosynthetic activity in real time at the leaf level. Consequently, several association studies using proximal and nonproximal sensing (e.g., RGB, thermography) have been developed. However, for the correct application of this breeding approach, it is essential to have a basic knowledge of both the physiological principles involved in the readings and the limitations of phenotyping due to the characteristics of the devices available on the market. This revision also covers other traits, such as the morphological and anatomical characteristics of leaves and roots, and the use of isotopes complementing Ge and Chf measurements.
Why it matches plant phenotyping methods植物フェノタイピング手法のレビューであり、光合成測定、蛍光、近接・非近接センシングの原理と限界を扱うため、方法論が中心です。
titleMorphophysiological Plant Phenotyping for the Development of Plant Breeding Under Drought and Heat Conditions: A Practical Approach
ABSTRACT Implementation of context‐specific solutions, including cultivation of varieties adapted to current and future climatic conditions, have been found to be effective in establishing resilient, climate‐smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesizing that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world's largest banana gene bank in terms of base temperature ( T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. One hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype‐specific T base were modelled. Growth responses of two genotypes were validated under greenhouse conditions, and gas exchange capacity measurements were made. The model confirmed genotype‐specific T base , with 30% of the accessions showing a T base below the reference of 14°C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low temperature sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, with this case study on banana, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate‐smart varieties.
Why it matches plant phenotyping methodsバナナ遺伝資源の温度応答を高スループットに定量化し、遺伝子型別の基底温度をモデル化・温室で検証しており、表現型取得法と検証が研究の中心です。
abstractOne hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation.
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.
Why it matches plant phenotyping methodsSIFの遠赤色/赤色比から葉クロロフィル含量を推定する理論・測定手法を開発し、放射伝達シミュレーションと複数植物種の実測で検証しており、植物形質取得が中心である。
abstractSun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC.
In this paper, we present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans. Our goal was to enhance the precision of radiative transfer models for modelling tree shading by using highly resolved 3D tree models. The approach was tested on a single apple tree (Malus domestica (Suckow) Borkh.) in a peri-urban setting and was validated by utilising an open-source radiative transfer model and comparing the simulation output with in-situ measurements of photosynthetically active radiation (PAR) as well as simulations utilizing turbid voxels of 0.2 m and 1 m edge length. The in-situ measurements of 60 PAR sensors showed a correlation coefficient (r) of 0.92 with the simulated light intensities for the reconstructed polygons which was higher than for the voxel-based approaches (0.2 m: r = 0.85, 1 m: r = 0.73). We were able to demonstrate that our approach effectively simulates light extinction through the canopy. This innovative method has the potential to easily provide detailed insights into high resolution radiation patterns within forests, which are connected to multiple ecosystem functions like species and habitat diversity.
Why it matches plant phenotyping methodsTLSデータから樹木の木部・樹皮・葉の3D形状を抽出する手法を開発し、PARシミュレーションとの比較で検証しており、植物形態の取得が中心的です。
abstractwe present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans.
Reproduction assets foundThe authors state that all study data (TLS-derived point clouds, PAR measurements) and the full R code for the leaf/wood polygon reconstruction are openly available in their GitHub repository, archived as Frey & Kröner 2024 (JulFrey/dotshadow, Zenodo DOI 10.5281/zenodo.14204435, cited in the references). The Zenodo URLCode · publicAll data relevant to the study and the full R code for the reconstruction of the leaves and woody
compartments can be found at our GitHub repository under open source license (Frey and Kröner
2024).Open asset ↗pdf-page:10 lines:1-56Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
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.
Why it matches plant phenotyping methods小麦の病害状態をSIF・ハイパースペクトル反射から推定する検出手法とモデルを開発・評価しており、植物表現型取得が中心である。
abstractTo 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.
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.
Why it matches plant phenotyping methods植物フェノミクス、HTP、AI、画像・センサーによる形質取得を主題とする概説であり、フェノタイピング手法のレビューとして中心的です。
abstractPlant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA).
Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.
Why it matches plant phenotyping methodsMODIS観測から植物群落のGPP（光合成）を推定するモデルを開発・補正し、交差検証と他モデル比較で性能評価しており、植物生理状態の取得手法が研究の中心である。
abstractwe incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress.
Droughts and heatwaves jeopardize terrestrial ecosystem services. The development of an open digital twin of the soil-plant system can help monitor and predict the impact of these extreme events on ecosystem functioning. We illustrate how our recently developed STEMMUS-SCOPE model—STEMMUS, Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil; SCOPE, Soil Canopy Observation of Photosynthesis and Energy fluxes—links soil-plant processes to novel satellite observables (e.g. solar-induced chlorophyll fluorescence), contributing to such a digital twin. This soil-plant digital twin allows a mechanistic window for tracking above- and below-ground ecophysiological processes with remote sensing observations. Following Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) principles, both for data and research software, we present the building blocks of the soil-plant digital twin. It emphasizes the importance of FAIR-enabling digital technologies to translate research needs and developments into reproducible and reusable data, software and knowledge.
Why it matches plant phenotyping methods土壌・植物系の生理状態を衛星観測と機構モデルで追跡するデジタルツインの構築が中心で、植物の生理・機能状態を推定する再利用可能な方法論・基盤を扱う。
abstractThe development of an open digital twin of the soil-plant system can help monitor and predict the impact of these extreme events on ecosystem functioning.