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.
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests 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.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.
Why it matches plant phenotyping methods継続的な植物センサー、ドローン画像、樹冠構造解析、果実表現型計測を統合し、NDVIや樹冠表面粗さなどのストレス指標を抽出する方法が研究の主要部分であるため。
abstractWe conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping
Raman / spectroscopyLeafTissuePhysiological trait estimationLeaf traitsWater status / transpiration
Phenotyping extensive populations remains a major constraint in tree breeding programmes, particularly due to the time-consuming and labour-intensive nature of conventional methods. Near infrared (NIR) spectroscopy, which is a high-throughput phenotyping method, offers an alternative solution, providing a rapid and cost-effective approach for assessing growth- and function-based traits on large numbers of trees. This study aimed to evaluate the potential of NIR spectroscopy-based models for predicting such traits in Larch. Specifically, delta carbon-13 ( δ 13 C), carbon (C), nitrogen (N), specific leaf area (SLA), leaf dry matter content (LDM), and phenolics on needles; the branch hydraulic trait (P 50 ), and lignin and hydroxyphenyl/guaiacyl (H/G) ratio on wood cores from an experimental study on Larix species were predicted using multivariate modelling, specifically, partial least squares regression. Reliable models were obtained for N content (R 2 training = 0.95, r 2 testing = 0.94), lignin (R 2 training = 0.95, r 2 testing = 0.94), and H/G ratio (R 2 training = 0.88, r 2 testing = 0.89), while moderate predictive performance was observed for C content (R 2 training = 0.79, r 2 testing = 0.79) and δ 13 C (R 2 training = 0.76, r 2 testing = 0.69). This methodological approach and its results encourage the transition from traditional laboratory methods to efficient, large-scale-based trait evaluation techniques in forestry.
Why it matches plant phenotyping methodsNIR分光とPLS回帰を用いて樹木の複数形質を大規模推定する手法を評価しており、表現型取得・推定法が研究の中心である。
abstractNear infrared (NIR) spectroscopy, which is a high-throughput phenotyping method, offers an alternative solution, providing a rapid and cost-effective approach for assessing growth- and function-based traits on large numbers of trees.
Global trait axes reveal overarching dimensions of plant functional variation. However, how these dimensions are spatially organized within and across forest types remains unclear. We combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping. Through site-training scenario, leaf-to-canopy scaling and spectral-domain modelling tests, we find that reliable canopy trait retrieval depends not only on trait and spectral coverage, but also on preserving trait-spectral relationships across sites and scales. Spectral predictions recovered observed multivariate covariation, summarizing crown variation into a leaf-economics dimension and two additional biochemical dimensions related to hydro-thermal regulation and defence/metabolism. Mapping these dimensions revealed distinct community-level trait organization alongside substantial species- and crown-level variation within forests. These findings link remotely sensed trait retrieval to environmental filtering and plant functional differentiation, providing a scalable framework for monitoring forest functional diversity.
Why it matches plant phenotyping methodsドローン分光画像と冠レベル形質測定を用いた植物形質の空間マッピング手法が中心で、スケーリングおよびスペクトルモデルの検証も行っている。
abstractWe combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
ABSTRACT Water-use efficiency (WUE), the ratio of accumulated plant biomass to water lost through transpiration has conventionally been determined using a destructive single-point measurement. Recent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies. Therefore, we compared digital biomass determined point clouds produced from multispectral camera scanners with destructive harvests across eight harvests using Samsun tobacco grown under both drought and high-water conditions. WUE efficiency, calculated using the digital biomass estimated from a point cloud and gravimetric water use determinations, were compared to destructive harvest determinations. The coefficient of variation (CV) showed there were no significant differences in digital and destructive measurements for either biomass or WUE. Indicating that digital measurements can be used in place of destructive measurements. Drought plants used significantly less water and were significantly smaller than high-water plants from Harvests 4 through 8. However, there were no significant differences in the ratio of evapotranspiration to leaf area or WUE, indicating that drought plants were simply smaller and used less water than the high-water plants. This work validates that estimating plant biomass from a digital point coupled with continuous gravimetric determination of water use provides a reliable nondestructive measure of WUE in high-throughput measurements across the full plant life cycle. PLAIN LANGUAGE SUMMARY We grew tobacco plants under either a drought or high-water treatment and harvested a portion of the plants every few days for a total of eight harvests. Throughout the experiment, we collected 3D images of the plants and continuously measured pot weight to track plant growth and water use across different developmental stages. Destructive biomass served as the gold-standard measurement. We then compared biomass and water-use estimates generated from the digital measurements with the destructive measurements. The digital approach provided accurate estimates of plant biomass and water use while requiring little hands-on labor and no plant destruction. These nondestructive methods could help plant breeders identify water-efficient plants earlier in the breeding process, accelerating the development of crops that use water more efficiently.
Why it matches plant phenotyping methods3D画像による非破壊バイオマス推定と連続的な重量測定からWUEを推定する手法を、破壊収穫と比較して検証しており、植物表現型取得法が中心である。
abstractRecent advances in high-throughput phenotyping now enable repeated, non-destructive estimation of biomass and WUE. However, these digital measurements must be statistically validated against conventional destructive methods to validate their use as reliable proxies.
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Raman / spectroscopyObject detectionPhysiological trait estimationWater status / transpiration
ABSTRACT Real‐time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional detection methods typically include destructive processes and suffer from response delays. Near‐infrared (NIR) luminescent metal halides offer a novel solution to this challenge, but they still face issues such as ultraviolet excitation and low photoelectric conversion efficiency. Herein, a luminescent material was synthesized based on the blue‐light‐excited lead‐free perovskite Cs 2 HfCl 6 :Te 4+ /Mo 4+ , in which energy transfer (ET) from Te 4+ to Mo 4+ enables highly efficient NIR luminescence in the 800–1200 nm wavelength range. Upon encapsulation with a commercial blue light chip, the fabricated NIR light‐emitting diode device achieved a photoelectric conversion efficiency of up to 16.1%. By utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content. This non‐destructive and precise NIR detection module provides a new solution for the real‐time monitoring of crop physiological status and holds broad application prospects in the fields of precision agriculture and plant science.
Why it matches plant phenotyping methods植物の含水量を非破壊・リアルタイムに推定するNIRセンシングモジュールと機械学習手法が研究の中心であり、植物生理状態の測定法を開発・検証している。
abstractBy utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content.
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
Localized soil-moisture deficits, that is, irregular sub-field patches where crops experience water stress well before visible wilting, are a leading cause of yield variability in row-crop agriculture. These zones are difficult to detect at the spatial resolution and revisit frequency required for timely irrigation response. This paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time. Rather than flying a fixed lawnmower survey, the platform is guided by an adaptive-sampling policy trained with Proximal Policy Optimization (PPO) that reallocates flight time and sensor dwell toward regions of emerging water stress as evidence accumulates mid-flight. We present the complete engineering pipeline: airframe and sensor design, a keyframe-based Visual SLAM front and back end that provides centimeter-scale geolocation without continuous reliance on Real-Time Kinematic (RTK) GNSS lock, a hyperspectral preprocessing and spectralindex chain (NDVI, NDRE, NDWI/NDMI) used to derive CWSI through a learned regression, the partially observable Markov Decision Process (POMDP) formulation and reward shaping used to train the sampling policy, and the fused system architecture tying these subsystems together. In simulated field trials over a 0.8-hectare test plot, the reinforcement-learning-guided policy achieved a 92% water-stress-zone detection rate versus 61% for a fixed-grid baseline, while reducing mission flight time by approximately 32%. We further report an ablation study isolating the contribution of SLAM-derived canopy structure to CWSI accuracy, a sensitivity analysis across field complexity, and a full error budget for the fused pipeline. We close with a discussion of validation limitations, broader scientific and agricultural impact, and a roadmap toward multi-UAV fleet deployment for whole-farm monitoring
Why it matches plant phenotyping methodsUAV、Visual SLAM、ハイパースペクトル画像、機械学習を統合し、作物の水ストレス状態を推定・地図化する技術パイプラインを開発・評価しており、植物表現型取得が中心である。
abstractThis paper presents a reinforcement-learningguided autonomous quadrotor unmanned aerial vehicle (UAV) platform that fuses onboard Visual Simultaneous Localization and Mapping (Visual SLAM) with a pushbroom hyperspectral imaging payload to construct georeferenced, canopy-registered maps of a Crop Water-Stress Index (CWSI) in near real time.
Abstract Visual assessments of growing forest nursery plants are time-consuming and often result in a lack of information at a physiological level. There exists a need for health screening in nurseries, that is fast and efficient, to improve overall health monitoring and nursery productivity. Rapid handheld sensors such as rapid thermal devices, leaf porometers and moisture meters, can provide regular information at a physiological level, that can improve the understanding of the impact of stress on young plant cuttings and their decline in health over time. This paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions. Furthermore, to assess whether thermal sensors could be used as an indicator, in conjunction with other variables such as soil water content or stomatal conductance, is needed operationally for fast screening during limited planting windows. Near Infra-Red Analysis (NiRA) data was collected to understand detailed plant functions at a finer reflectance level. A relationship was found where the increase in thermal signals reflects a depletion of water content, resulting in an eventual decline in stomatal conductance and, ultimately, plant mortality. Several algorithms were used in a preliminary test, using RapidMiner software, to discriminate between the four phases of plant health decline using physiological variables and NiRA data. Both Gradient Boosting Trees (GBT) and Deep Learning (DL) showed the best performances, achieving favourable accuracies of 96.8% and 91.2% without NiRA data, 84.6% and 88.2% with NiRA data, with shorter training times. Using thermal technology weighted amongst the highest of the best performing variables using GBT, the utility and accuracy showed good discrimination between the stages of plant decline and is encouraged for future research in this field.
Why it matches plant phenotyping methods植物のストレス段階を熱センサー、ポロメータ、含水率計、NiRAおよび機械学習で測定・識別する方法の有用性と信頼性を評価しており、表現型取得・判定手法が中心である。
abstractThis paper evaluates the utility and reliability of contemporary sensor technologies, to operationally monitor stress phases in juvenile forest plant cuttings during progressive moisture (dry-down) conditions.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Summary Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths. We present a framework combining hyperspectral data, inverse modeling, and network analysis to investigate population‐level variation in Streptanthus tortuosus . Using a common garden experiment with four populations, we apply supervised methods (partial least square discriminant analysis; ridge regression) to identify which spectral features differ among populations, and an unsupervised spectral network approach to characterize how wavelength correlations are organizationally structured within each population, where we treat coordination architecture itself as a population‐level phenotype that can vary with environment. The framework detects distinct, heritable spectral signatures across populations, population differences in anthocyanins, carotenoids, Chl, water content, and population‐specific network architectures. Thermally variable environments were associated with greater spectral modularity, demonstrating that trait coordination architecture varies with climate of origin. This approach addresses the phenotyping bottleneck in evolutionary ecology, providing a scalable, high‐throughput tool for characterizing genetically based population differences in both individual traits and their coordination, with broad applications for monitoring plant population responses to climate change.
Why it matches plant phenotyping methodsハイパースペクトル計測、逆モデリング、ネットワーク解析を統合し、葉の機能形質と形質協調構造を植物表現型として抽出する手法が研究の中心である。
abstractHyperspectral reflectance offers a powerful high‐throughput solution, simultaneously capturing leaf biochemistry, water content, and structural properties across hundreds of wavelengths.
BarleySeed / grainPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Background and aims Laser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination. Methods Malting barley (Hordeum vulgare subsp. distichum L. cv. Sinfonia) seeds were analysed using five coefficients-Generalised Differences (GD), Fujii, Lasca, Frequent Motion Image (FMI), and Moment of Inertia (MI)-to assess their ability to discriminate between treatments and tissue regions, and to track changes during imbibition. Whole and longitudinally cut seeds from two treatments (control [untreated] and autoclaved [heat-inactivated]) were analysed, focusing on embryo/endosperm activity ratios. LBSA was also evaluated as a function of imbibition time and seed moisture content. Key results Four coefficients (GD, Fujii, FMI, and MI) successfully differentiated control and autoclaved seeds, as well as embryo and endosperm regions in control seeds, revealing distinct activity patterns. In control seeds, LBSA increased with imbibition time and was well described by polynomial models (quadratic for Fujii and MI; cubic for GD and FMI). GD, Fujii, and FMI required a minimum seed moisture content of 25% to detect activity, while MI was responsive only above 31.5%. In contrast, Lasca was exclusively sensitive to hydration level, fitting a relaxation curve independent of treatment. Conclusions LBSA constitutes a robust, non-destructive methodology for monitoring early germination processes. By combining coefficients, it is possible to infer physiological traits such as embryo specificity, hydration thresholds, and dynamic metabolic reactivation. This positions LBSA not only as a diagnostic tool for seed viability but also as a physiologically informative proxy for studying germination and tissue-level dynamics.
Why it matches plant phenotyping methodsレーザーバイオスペックル画像法を用いて種子の生存性、含水率、発芽動態を測定・識別し、複数係数の性能評価とモデル化を行う手法中心の研究である。
abstractLaser Biospeckle Activity (LBSA), derived from laser-induced speckle variations in response to dynamic changes in living tissues, is a promising non-invasive technique for evaluating seed germination.
Abstract China's rice production and environmental sustainability are largely dependent on the cold black soil region in Northeast China, where precise water and nitrogen management is challenged by water scarcity and high carbon emissions. To overcome the limitations of conventional empirical management and improve the accuracy of evapotranspiration (ET) estimation in controlled-irrigation paddy fields, this study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model. To parameterize and validate this methodology, field data comprising four lower limit thresholds for controlled irrigation and four nitrogen fertilizer application rates were acquired from the Rice Research Site of Farm 856, Heilongjiang Province, China. This integrated approach was used to systematically evaluate rice growth, water consumption, resource use efficiency, and greenhouse gas emissions under different water-nitrogen treatments. Based on these evaluations, an irrigation optimization scheme was developed using daily crop evapotranspiration (ETc). The results indicated that water, nitrogen, and their interaction significantly affected rice yield, irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and global warming potential (GWP). Treatments W3N2 (80%+155 kg/ha N) and W3N3 (80%+200 kg/ha N) achieved the highest yields, 11,883.51 and 11,436.82 kg/ha, respectively, whereas W2N1 (70%+110 kg/ha N) exhibited the best comprehensive performance, with a TCQ value of 0.65. Among the tested vegetation indices, the normalized difference vegetation index (NDVI) showed the strongest correlation with the basal crop coefficient, with an R²of 0.85. The NDVI -crop water stress index ( CWSI ) coupled model achieved the highest ET c estimation accuracy (R 2 = 0.89, RMSE = 0.39 mm/day), reducing the RMSE by 10.3% compared to the traditional, Multi-objective optimization revealed obvious trade-offs among high yield, water saving, high nitrogen efficiency, and low emissions. Scenario S5 was identified as the optimal solution, with an irrigation amount of 669.94 mm, a nitrogen rate of 117.48 kg/ha, a yield of 11,473.43 kg/ha, and the highest coordination degree of 0.86. These results demonstrate that coupling UAV multispectral and thermal infrared imagery with the FAO-56 model can effectively improve ETc estimation and provide reliable data support for water-nitrogen multi-objective optimization in cold-region rice production.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とFAO-56を結合し、イネの蒸発散量を推定する手法を開発・検証しており、ETc推定精度も定量評価しているため、単なる灌漑試験ではなく植物状態の計測手法が中心です。
abstractthis study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Within the context of climate change, quinoa ( Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg -1 ). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems.
Why it matches plant phenotyping methodsリモートセンシングと多時点の植 phenotyping を統合し、35の植生指数の感度比較によって水ストレス関連形質を抽出する方法適用が、研究の主要な技術的要素として明示されています。
abstractWe combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively.
Field / plotLeafStem / branchPhysiological trait estimationGrowth / time-series analysisStress response / toleranceWater status / transpiration
Live fuel moisture content is a key determinant of live fuel flammability, yet its destructive and discontinuous measurement limits high-temporal-resolution monitoring. This study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions. From February to July 2025, leaf and trunk electrical potentials were monitored weekly in Salvia rosmarinus individuals from a Mediterranean shrubland, while LFMC, essential oil yield, fatty-acid fraction, and laboratory-based flammability metrics-ignition time, combustion duration, and flame height-were assessed bi-weekly. Leaf electrical potential was strongly associated with LFMC (R 2 = 0.64, p < 0.001), decreasing as plants underwent seasonal drought-induced dehydration. Periods of high temperature and low rainfall reduced both LFMC and electrical potential, coinciding with shorter ignition times, which declined to approximately 20-30 s during the driest period. Based on the observed shifts in ignition time, combustion duration, and flame height, three empirical LFMC response zones were identified, with leaf electrical potential closely tracking transitions in plant hydration and flammability. These results suggest that plant electrophysiology may provide a promising non-invasive indicator of live fuel water status and seasonal flammability dynamics, with potential applications in wildfire risk monitoring when combined with conventional LFMC, meteorological, and remote-sensing approaches.
Why it matches plant phenotyping methods葉の電気的電位をLFMC（水分状態）および可燃性関連形質の非破壊・連続的な指標として評価しており、植物状態の取得方法の検証が研究の中心である。
abstractThis study evaluated whether leaf electrical potential can serve as a non-invasive proxy for LFMC and flammability-related traits under natural drought conditions.
Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C4 carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment.
Why it matches plant phenotyping methodsソルガムの干ばつ適応に関するセンシング型フェノタイピング手法を体系的にレビューし、形質推定の精度・移植性・検証、およびモデルと機械学習の統合を扱うため、方法論が中心である。
abstractThis systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement.
Accurate assessment of crop water status is critical for precision irrigation and sustainable water management in agriculture. This study develops a UAV-based thermal infrared inversion framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations. The proposed approach integrates multi-frame image mosaicking, threshold-based canopy extraction, and a gray-temperature calibration model to generate spatially continuous canopy temperature maps. Crop water stress was quantified using the Crop Water Stress Index (CWSI), and its reliability was further evaluated by analyzing its relationship with stomatal conductance. The framework further estimates soil moisture status and irrigation requirements based on a threshold-based irrigation strategy. The results show that the linear gray-temperature calibration model achieved a maximum absolute error of less than 0.3 °C and that the calculated CWSI and estimated irrigation requirement were strongly correlated with measured stomatal conductance, with R 2 up to 0.91. The proposed method provides a practical technical workflow from UAV thermal imagery acquisition to canopy temperature retrieval and quantitative irrigation decision-making, demonstrating its potential for precision irrigation management in tea plantations.
Why it matches plant phenotyping methodsUAV熱画像から茶園の樹冠温度と水ストレスを推定する取得・抽出・較正手法を開発し、気孔コンダクタンスとの関係で検証しており、植物状態の計測が中心である。
abstractThis study develops a UAV-based thermal infrared inversion framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations.
Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions.
Why it matches plant phenotyping methodsRGB画像、赤外線サーモグラフィー、VNIR–SWIRハイパースペクトルを統合した高スループット表現型解析ワークフローが中心的に記述され、複数の植物形質・状態を定量化している。
abstractA high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence.
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
LeafStress / disease detectionWater status / transpiration
We report the design and assembly of geometrically and compositionally complex dielectric metasurfaces, termed nanocrystal-adaptive polymer composite optical metasurfaces (NCOMs), architected from nanocrystal (NC) building blocks and stimuli-responsive polymers to enable high figure-of-merit optical leaf sensors. These NCOM sensors are fabricated at scale by direct nanoimprint lithography into UV-curable TiO2 NC inks to structure optical metasurfaces in the geometry of surface relief gratings atop waveguides that support high reflectivity, narrowband guided-mode resonances. A room-temperature ligand exchange process creates a nanoporous TiO2 scaffold that is subsequently infiltrated with a stimuli-responsive polymer while retaining the metasurface geometry and optical quality. As a proof of concept, we demonstrate optical humidity sensors by incorporating a moisture-responsive biopolymer chitosan into the nanoporous TiO2 metasurfaces, resulting in a 430% improvement in sensitivity compared to conventional refractive-index sensors where the polymer is adjacent to the metasurface. The NCOM geometry is engineered to position its resonance at wavelengths above 750 nm, enabling detection with standard VIS-NIR Si photodetectors while maintaining broadband transparency across the photosynthetically active 400-700 nm wavelength range for unobstructive monitoring of crop health. NCOM sensors are mounted face-down on leaves, and the highly reflective resonances are readily distinguished from the leaf background and respond dynamically to leaf water stress, enabling passive, battery-free monitoring of leaf surface humidity.
Why it matches plant phenotyping methods葉面湿度と水ストレスを非接触・受動的にモニタリングする光学センサーの設計、製造、感度向上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。
abstractenable high figure-of-merit optical leaf sensors
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
Accurate monitoring of cotton plant moisture content (PMC) is crucial for guiding irrigation practices. To address the limited capacity of single-source remote sensing data to characterize the water status of cotton plants, as well as the lack of quantitative reference values for suitable PMC levels at different growth stages, this study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data. Furthermore, the suitable reference levels of PMC at different growth stages were investigated according to the response relationship between PMC and yield at each growth stage. Five soil moisture gradients were established, and at each growth stage, fresh and dry weights of cotton shoots were measured to calculate the PMC. A UAV platform equipped with multiple sensors was used to collect visible-light (RGB), multispectral (MS), and thermal infrared (TIR) images of the cotton canopy. Three feature selection methods were employed to identify moisture-sensitive parameters: Pearson correlation analysis, principal component analysis (PCA) for dimensionality reduction, and recursive feature elimination (RFE). Using the selected parameters, four machine learning algorithms, AdaBoost, random forest (RF), CatBoost, and k-nearest neighbors (KNN), were applied to construct and validate PMC estimation models. The suitable PMC levels at different growth stages were identified based on the response relationship between measured PMC and yield under different water gradients. The results showed that the RFE feature selection method identified eight water-sensitive parameters, and the CatBoost model integrating multimodal data performed best, with R² and RMSE reaching 0.807 and 0.033%, respectively, on the test set, providing a reliable method for high-resolution spatial mapping of field-scale PMC. On this basis, the response of yield to PMC was analyzed, revealing that when PMC was maintained at 83.8%, 85.9%, 79.3%, 78.0%, and 67.7% at the bud, initial flowering, peak flowering, peak boll-setting, and boll opening stages, respectively, the theoretical maximum yield of 6579–6667 kg/hm² could be achieved. This study realized high-precision remote sensing monitoring of PMC and further explored the appropriate moisture content thresholds for different growth stages, providing a quantitative reference for precision water regulation in cotton fields.
Why it matches plant phenotyping methodsUAVのマルチモーダル画像と機械学習により、綿植物の水分含量を推定・検証する手法が研究の中心であり、植物状態の高解像度マッピングにも応用している。
abstractthis study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data
Accurate prediction of foxtail millet yield is essential for effective field management and high-throughput breeding. Despite advances in UAV-based yield prediction for major crops, existing studies predominantly rely on single-temporal features (SFs) extracted at noon, overlooking significant diurnal dynamic signals that characterize crop responses to water stress. To address this research gap, we propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy under different irrigation regimes. During the flowering and grain-filling stages, UAV images were acquired across eight time slots (T1–T8) within a single day to capture the complete diurnal trajectory of canopy physiological responses. SFs were extracted at each time slot, and CFs were derived through summation, averaging, and range operations across multiple slots. A systematic four-step workflow was developed to determine the optimal UAV flight frequency and timing by balancing prediction accuracy with operational costs. Three ensemble learning algorithms (Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost)) were evaluated using multiple feature sets incorporating SFs, CFs, and their integration. Results demonstrated that CFs more comprehensively captured dynamic crop responses to water stress than SFs. Canopy features from afternoon combinations generally exhibited stronger yield correlations than morning combinations. The [T5, T8] combination was identified as optimal, providing a practical balance between prediction accuracy and operational cost. Model comparison revealed that RF exhibited greater robustness across different water treatments, whereas AdaBoost achieved higher accuracy on the test set. Feature importance analysis confirmed the dominance of CFs, with ∑VSWI ranking first across both models and growth stages. This study provides a systematic framework for utilizing diurnal dynamic signals in crop yield prediction, offering new methodological insights for precision agriculture and high-throughput phenotyping of foxtail millet and other dryland crops.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から作物特徴量を抽出し、収量という植物形質を推定する手法と、撮影頻度・時刻を最適化するワークフローが研究の中心であるため。
abstractwe propose a novel approach utilizing diurnal cross-temporal features (CFs) derived from UAV-based multispectral and thermal imagery to enhance yield prediction accuracy
Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.
Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。
abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.
GrapevineLaboratory / benchtopStem / branchPhysiological trait estimationWater status / transpiration
Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C4D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments.
Why it matches plant phenotyping methods植物の水分状態を直接推定する非侵襲センサーと解析手法の開発・検証が研究の中心であり、明確な植物フェノタイプ測定に該当する。
abstractThe industrial C4D sensing system is adaptively modified into a specialized Plant-C4D sensor with an array-based design for batch signal acquisition.
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.
Field / plotLeafWhole plant / canopy / plot / fieldObject detectionPhysiological trait estimationGrowth / time-series analysisLeaf traitsWater status / transpiration
Abstract Understanding the vulnerability of plants to more severe and frequent drought events and developing adaptive management strategies requires robust methods for quantifying long‐term changes in plant water stress (PWS). Most data‐driven explorations of long‐term trends in PWS have focused on alterations in canopy structure (e.g., leaf area index) or canopy structure‐dependent variables (e.g., gross primary productivity and evapotranspiration). This is largely because long‐term trends in canopy structure are relatively easy to detect from satellite observations. However, a focus on structural responses limits our ability to detect physiological stress due to challenges in isolating it from the effects of structural greening. Consequently, this difficulty hampers a comprehensive examination of long‐term PWS in the context of global greening trends. To address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades. Combining site‐level and satellite observations at the half‐degree resolution across the globe, we found that accounting for greening‐related changes substantially alters the sign of long‐term PWS trends inferred from traditional approaches. Specifically, our study reveals a significant increase in PWS that is only detectable when accounting for structural greening trends. When greening trends are not accounted for, global PWS appears to have decreased over time. Overall, our results highlight the need to integrate structural dynamics and greening into PWS detection. Such an integration of observations and land models will improve our understanding of plant‐water‐energy interactions.
Why it matches plant phenotyping methods植物の生理的な水ストレスを定量化する新しいプロセスベース指標を開発し、衛星・地上観測で検証・適用しており、表現型測定法が研究の中心である。
abstractTo address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades.
Field / plotWhole plant / canopy / plot / fieldStress / disease detectionPlant / canopy temperatureWater status / transpiration
Efficient irrigation management requires complementary information on atmospheric demand, soil conditions, and crop water stress. This study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework. The system combines LoRa communication, ESP32-based sensor nodes, soil and meteorological sensing, FAO-56 reference evapotranspiration (ET0), and canopy-temperature-based Crop Water Stress Index (CWSI). Irrigation decisions rely on the complementary use of ET0, in situ soil measurements, and CWSI rather than on a single indicator. A hybrid time-, event-, and query-driven acquisition strategy was implemented to adapt node activity and limit communication overhead. The system was deployed under outdoor conditions in Oujda, Morocco, demonstrating integrated sensing, wireless data transmission, crop-stress monitoring, and automated irrigation control. Energy characterization further showed distinct consumption profiles across sensing, communication, actuation, and low-power operating states, supporting the use of duty cycling to limit active node operation. The results demonstrate the feasibility of integrating environmental, soil, and crop-level information within a low-power IoT framework for adaptive irrigation management.
Why it matches plant phenotyping methods低消費電力IoT基盤の開発・実証が中心で、作物の水ストレスをCWSIで取得・監視する植物フェノタイピング要素が技術的に組み込まれている。
abstractThis study presents a low-power Internet of Things (IoT)-based irrigation system that integrates these components within a unified monitoring and control framework.
The use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat, taking into account varietal specificity and agrotechnical factors, has been studied. The test site was the field experience in the forest-steppe zone of the Novosibirsk Priobye. In the experiment, spring wheat of the Suenga and Novosibirsk 41 varieties was cultivated using intensive agricultural technology. For the analysis, data obtained using the DJI Phantom 4 Multispectral Phantom unmanned aerial vehicle during the crop growing period in 2023–2025 were used. Vegetation index values were calculated using five spectral channels: blue (B, 450 ± 16 nm), green (G, 560 ± 16), red (R, 650 ± 16), red edge (RE, 730 ± 16) and near-infrared (NIR, 840 ± 26 nm). For the analysis of informational importance, the following indices were used as predictors of crop yield: NDVI, NDWI, GNDVI, LAI, CVI, GCI, and ChlRE. For assessing the informativeness of the indices, independent methods were used: the F-statistic of one-way regression (ANOVA F-test), evaluation of mutual information (Mutual Information, MI), and feature importance of the random forest algorithm (Random Forest, RF). Each of the scores was normalized in the range [0; 1] using the min-max normalization method, after which a combined score was calculated as a weighted sum. For the Suenga variety, the stable predictors regardless of the experimental variants were CVI (tillering) and ChlRE (stem elongation and heading), while for Novosibirsk 41, the set of informative predictors significant ly depended on the combination of plant protection and fertilizer systems. It was found that chlorophyll content indices (GCI, ChlRE) increased the predictive relationship with yield under fertilization, while the water status index (NDWI) lost informativeness when fertilizers were applied.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、複数の統計・機械学習手法を統合して小麦収量予測における指標の有用性を評価しており、植物形質推定ワークフローが中心です。
abstractThe use of a combined assessment of the informational significance of vegetation indices for predicting the yield of spring wheat
Above ground crop traits provide an early indication of a plant's capacity to tolerate stress, and are important for breeding programs aimed at improving stress tolerance. In this work, we present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time under control, drought, and saline conditions. We used daily sideview imaging of individual plants, followed by segmentation of the panicle, leaf and stem using the deep learning U-Net++ segmentation model. The resulting segmentations were used in regression models to estimate leaf area, fresh and dry biomass, and leaf dry weight. The regression models showed high predictive accuracy. Using these estimates, we could calculate specific leaf area and leaf weight ratio. In addition, radiation use efficiency for above-ground biomass production was calculated, providing an independent physiological check on the consistency of these predictions. Finally, using automated measurements of plant transpiration we were able to determine daily averages of whole plant stomatal conductance. The results show that image-derived morphological traits can be used to accurately estimate biomass-related traits and to derive physiologically meaningful indicators of plant performance over time. This method provides a framework for non-destructive monitoring of quinoa responses to drought and salinity.
Why it matches plant phenotyping methods画像取得、深層学習セグメンテーション、回帰による植物形質推定を中核とする高スループット表現型解析手法であり、ストレス実験での単なるルーチン測定ではない。
abstractwe present a high-throughput methodology to study morphological and physiological traits of individual quinoa plants over time
Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (K c ) and evaluated their operational performance for irrigation scheduling. K c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with K c , with higher calibration accuracy for the UAV model (R 2 = 0.9414) than for the satellite model (R 2 = 0.8278). The UAV-based model applied 23-30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha -1 in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger K c -NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality.
Why it matches plant phenotyping methods衛星・UAV画像からNDVIを用いて作物係数を推定する手法を開発し、別年・圃場条件で検証しており、植物群落状態の取得・推定が研究の中心である。
abstractK c -NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season
PotatoField / plotGrowth / time-series analysisGrowth / development / phenologyWater status / transpiration
Real-time measurement of belowground tuber growth has not been conducted in field crops. Here, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss. Over the 13 days preceding harvest, tuber thickness increased by 0.90 mm, corresponding to a daily gain of 1.33 g, or a 4.8% increase. The greatest diurnal fluctuation was 0.453 mm, corresponding to a transpirational water loss of 9.4 ml, or 3% of the tuber’s water content. Daily transpiration showed a positive correlation with air temperature and vapor pressure deficit. This sensor will enable more precise input control and higher temporal resolution than current methods for below-ground crops, supporting improved crop management, yield prediction, and harvest decisions.
Why it matches plant phenotyping methods埋没ジャガイモ塊茎の成長・厚さ変動・水分損失をひずみゲージでリアルタイム測定する手法が研究の中心であり、植物器官形質の取得法として適格。
abstractHere, a strain-gauge sensor was used to monitor potato tuber growth and estimate mean daily tuber water loss.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerを用いた根系画像解析が研究の中心で、根長・径・体積・表面積・分枝などの植物形質をデジタル抽出しているため、実質的な植物フェノタイピング応用研究である。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
The assessment of water stress levels in plants should be essential part of precise irrigation management, and a good and quick method is useful in plant phenotyping. There are many options for this task, but the effectiveness varies between crop species and environments. Apparently open field applications face the most difficulties. This study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components. Data of the abovementioned variables was collected during three consecutive growing seasons in processing tomato cultivated under different water supply regimes to provide data with varying water stress levels. Then the relation of the measured variables to 226 VIs was tested created according to the formulas collected in the Index DataBase (IDB Project, indexdatabas.de ). New VIs were also developed derived from the most important variables of the ML algorithms, customised to tomato water stress assessment. Standard normal variate and its combination with Savitzky-Golay first derivative were used for pre-processing the spectra and principal component regression (PCR), partial least squares regression (PLSR), elastic net (ENET), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGB) algorithms were tested. The newly developed indices outperformed the existing formulas, except in the case of β-carotene. The most reliable index was developed for RWC estimation; that was the difference of the reflectance on the 986 and 701 nm wavelengths. The ENET and SVR algorithms produced the best models depending on the pre-processing method. The blue, near-infrared (NIR) and green regions, respectively, were the most important regarding all models according to the variable importance analysis. The model with the best metrics was developed for chlorophyll-a (R 2 =0.82, nRMSE=11%, RPIQ=2.41), followed by RWC (R 2 =0.72, nRMSE=14%, RPIQ=2.53).
Why it matches plant phenotyping methodsハイパースペクトル反射データと多変量・機械学習モデルを用いて、トマトの水ストレス関連生理形質を推定し、新規指標も開発・評価しているため、表現型取得・推定手法が中心である。
abstractThis study aimed to test a large number of vegetation indices (VIs) and multivariate models based on hyperspectral reflectance data (325-1075 nm) regarding their correlation and prediction abilities to leaf stomatal conductance, relative water content (RWC), and detailed chlorophyll, and carotenoid components.
WheatGrowth chamberRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Description: Background: Water scarcity severely threatens wheat (Triticum aestivum L.) production in arid and semi-arid regions like Pakistan, requiring the development of climate-resilient crop varieties. Root System Architecture (RSA) is critical for drought tolerance, yet evaluating these "hidden half" traits has traditionally been limited by destructive, time-consuming field methods. Objective: This study comparatively evaluates the RSA and drought adaptability of four prominent wheat cultivars—Akbar-19, Fakhar-e-Bhakkar-19, Dilkash-19, and CN3—under varying water stress conditions. Methodology: The experiment was conducted in a controlled rhizobox setup at the speed breeding facility of CSI-NARC, Islamabad. Four cultivars were exposed to three irrigation levels ($100\%$, $75\%$, and $50\%$ field capacity). Digital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency. Key Findings: The analysis revealed distinct genotypic strategies for drought adaptation. CN-3 (G2) emerged as highly promising for water-limited environments, demonstrating the highest relative water content (RWC) stability ($71.27\%$), thickest root profiles ($7.95\text{ mm}$), and largest root volume ($1,773.2\text{ mm}^3$). Dilkash-19 (G3) showed strong structural stability with a consistent root length ($160.33\text{ mm}$), while Fakhar-E-Bhakkar-19 (G4) excelled in root surface area ($2,145.8\text{ mm}^2$) and root tip density ($958.99\text{ tips}$), signaling high potential for nutrient foraging. Conversely, Akbar-19 (G1) displayed lower adaptability due to lower RWC and limited root volume. Significance: This research bridges the gap between digital phenotyping platforms and traditional breeding practices. It identifies vital genetic donors like CN3 and Dilkash-19 for breeding programs targeting drought tolerance, offering practical pathways to sustain wheat productivity and strengthen food security under changing climatic conditions.
Why it matches plant phenotyping methodsRhizo Vision Explorerによるデジタル根系表現型計測が研究の主要な方法として明示され、根長・径・体積・表面積・分枝などの植物形質を抽出しているため。
abstractDigital root phenotyping was performed using the open-source software Rhizo Vision Explorer to evaluate 2D scanned images for traits including total root length, root diameter, volume, surface area, and branching frequency.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Purpose The variability in tolerance to water stress among cowpea genotypes requires fast and accurate phenotyping methods. The integration of infrared thermography with artificial intelligence is emerging as a robust solution for large-scale, non-invasive monitoring. Thus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images. Methods Ten genotypes (five varieties: Corujinha – G1, Paulistinha – G2, Sempre Verde – G3, Pintado – G4, and Rabo de Tatu – G5) and the cultivars BRS Novaera – G6, BRS Pajeú – G7, IPA 206 – G8, BRS Tapaihum – G9, and BRS Miranda – G10) were subjected to four water regimes (25%, 50%, 75%, and 100% of ETc). Thermographic images were collected at the V3 and R2 stages and processed using Deep Learning architectures (InceptionV3, SqueezeNet, VGG16, and VGG19) to extract features (vectorization). The k-NN, Decision Tree, Random Forest, SVM, Neural Network, and AdaBoost algorithms were trained to classify stress levels and genotypes. Results The vegetative stage (V3) proved more effective for diagnosis than the reproductive stage (R2), exhibiting more stable thermal signatures. The SVM algorithm, combined with the VGG16 vectorizer, achieved the best performance, achieving an accuracy greater than 0.910 in classifying water regimes. The landrace varieties exhibited thermal patterns distinct from those of the improved cultivars, enabling high-precision genotypic identification. Conclusions The proposed approach demonstrates that infrared thermography, combined with machine learning models, is an effective tool for high-throughput digital phenotyping, optimizing the selection of drought-tolerant materials and irrigation management in precision agriculture.
Why it matches plant phenotyping methods赤外線サーモグラフィ画像から水ストレス状態と遺伝型を抽出する機械学習手法を開発・評価しており、植物フェノタイピングが研究の中心である。
abstractThus, the objective was to train models to identify genotypes and diagnose water stress in cowpea using artificial intelligence algorithms to process infrared thermographic images.
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.
The selection of genotypes adapted to water stress requires experimental facilities that allow environmental control without compromising physiological and yield relevance. The objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe, which comprised sixty-two high-volume prismatic lysimeters arranged in rows 1.2 m long and spaced 0.6 m apart. Soil water dynamics were monitored weekly using a weighting system. To validate PlaFe, two soybean genotypes were exposed to two water scenarios for forty days from R2 + 7d, during two growing seasons. Two irrigation treatments were applied: irrigation to keep soil water content over 60–70 % of field capacity (EH0), and irrigation equivalent to 35 % of that applied in EH0 (EH1). Water consumption, crop biomass, and pod number were determined at maturity. On average, water stress reduced both biomass and pod numbers by 40 %. However, reproductive efficiency varied among genotypes. Canopy temperature increased by 0.56 °C as daily water consumption decreased, demonstrating its potential to assess drought. These results demonstrate PlaFe’s potential for the accurate evaluation of crop response and adaptation to diverse water scenarios without compromising the complex plant-environment interactions inherent to field conditions.
Why it matches plant phenotyping methodsPlaFeという屋外半制御型フェノタイピングプラットフォームを設計・検証しており、植物の水消費、バイオマス、莢数、群落温度などの表現型評価が研究の中心である。
abstractThe objective of this study was to design and validate an outdoor phenotyping semi-controlled platform, PlaFe
Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization. Rather than maximizing geometric segmentation accuracy, thresholds are selected to maximize correlations between retained points and yield‐related traits, outperforming manual filtering, reducing user effort, and standardizing decisions. Across multispectral 3D point clouds, Bayesian optimization recovered index‐specific threshold ranges that yielded strong in‐sample correlations with grain yield ( r = 0.72), bean number ( r = 0.61), pod number ( r = 0.53), and straw biomass ( r = 0.72). Peak associations occurred at harvest for straw biomass, at 41 days before harvest (DBH) for seed yield, 33 DBH for bean number, and 34 DBH for pod number. Across the season, greenness‐based indices and broadband brightness metrics consistently showed stronger links with seed yield than pigment ratio or water status indices. For straw biomass and pod number, pigment ratio indices showed consistently lower correlations. Targeting trait‐relevant canopy signals via Bayesian optimization enables reliable, nondestructive assessment of relationships between spectral signals and yield‐related traits in faba bean. By optimizing thresholds to maximize trait correlations rather than geometric accuracy, the workflow can support earlier, more cost‐efficient identification of high‐performing genotypes under drought stress and contribute to strengthening high‐throughput phenotyping in breeding programs. This enables faster identification of canopy signals most relevant to target traits.
Why it matches plant phenotyping methods植物の3D点群画像から表現型を抽出するセグメンテーション手法を開発し、ベイズ最適化による閾値選択と性能評価を行っており、フェノタイピング手法が研究の中心である。
abstractWe developed a color‐based filtering workflow for faba bean ( Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband brightness via Bayesian optimization.
Accurate estimation of evapotranspiration (ET) is critical for irrigation management in water-scarce regions such as the Middle East and North Africa (MENA). This study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer, deployed simultaneously over an irrigated late-season potato field (1.8 ha) in the Beqaa Valley, Lebanon. Satellite NDVI observations indicate that the BLS–EC overlap period (13 October–27 November 2021) sampled the crop from peak canopy (NDVI ≈ 0.85–0.90) through the onset of senescence (NDVI ≈ 0.79). The BLS (Scintec BLS900) operated along a 140 m path. The EC system showed incomplete daytime energy-balance closure, with a regression slope of ≈0.69 and a seasonal Bowen-ratio-preserving correction factor of CF = 1.24 (a ~19% closure deficit) was used. Across the matched period, daily H from the BLS was strongly correlated with EC (r ≈ 0.82) but systematically lower, with a regression slope of ≈0.63 that persisted across timescales; this scale-invariant amplitude compression reflects the path-averaged, similarity-based nature of the scintillometer retrieval rather than the EC closure deficit, which instead governs the mean bias. BLS-derived daily ET showed a systematic positive bias relative to uncorrected EC (mean bias error, MBE = +0.30 mm d−1; +16% cumulative). Applying the Bowen-ratio-preserving correction (CF = 1.24) to EC reduced this to MBE = −0.14 mm d−1 (−6%), and the residual-to-LE correction yielded MBE = −0.15 mm d−1 (−6.4%); the latter comparison is only partly independent, as both methods share the same Rn and G. The Bowen-ratio-preserving method is therefore recommended for this dataset. Overall, the BLS captured the temporal variability of crop water use well, but residual-based ET estimates require careful treatment of the energy-balance-closure gap and are sensitive to the high BLS gap fraction (61.6% of 15 min records over the overlap, exceeding 90% at night). Once EC is closure-corrected to serve as the reference, the BLS offers a cost-effective alternative for field-scale ET monitoring in the MENA region, subject to the conditional agreement documented here.
Why it matches plant phenotyping methodsジャガイモ圃場の作物蒸発散量（ET）という生理・水利用状態を対象に、ECとBLSを比較検証し、補正法や測定誤差も評価している。センサー測定法の技術的妥当性が中心であり、単なる routine measurement ではない。
abstractThis study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer
Reproduction assets foundThe paper's flux/ET datasets are only available on request from the corresponding author, so they do not qualify as public assets. However, the Supplementary Information file (available at the MDPI supplementary URL) explicitly contains experiment sensor documentation and field/canopy images (Figures S1–S4: study site,Supplement · publicmeasurements along the beam. Because these results derive from a single crop, season, and phenological window, their generalization awaits multi-site, multi-season replication spanning the full-canopy cycle—the priority for subsequent campaigns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s26144398/s1 , Figure S1: Study site and potato canopy—Beqaa Valley, Lebanon; Figure S2: Eddy covariance system—full tower view (peak canopy); Figure S3: EC sensor suite close-up and soil sensor installation; Figure S4: BLS900 scintillometer—transmitter, receiver, and meteorological station.
Author Contributions
Conceptualization, HOpen asset ↗lines:251-268Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Canopy water content (CWC) is an important indicator of crop water status **and** supports precision irrigation decision-making. Plot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined. Methods In this study, spring maize at the Shiyanghe site was monitored using UAV-based multispectral and thermal infrared imagery. Mean, percentile, and dispersion features were extracted from effective canopy pixels within each plot. RFECV feature selection, 50 repeated random train-test splits, paired statistical tests, simulated spatial aggregation, and four regression models were used to evaluate the stage- and scale-dependent contribution of these features. Results and discussion Water stress affected both overall spectral-thermal responses and within-plot signal distributions. Before tasseling, percentile and dispersion features were frequently selected and provided complementary information, especially for tree-based models and finer aggregation scales. After tasseling, mean features generally showed more stable performance, although some distributional features still contained CWC-related information. The supplementary Xinxiang site-internal analysis suggested that, under weak water-gradient and small-sample conditions, distributional features may be frequently selected but may not consistently improve prediction accuracy. Overall, the contribution of distributional features was growth-stage-, scale-, and model-dependent.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からトウモロコシ群落の水分含量を推定する特徴抽出・選択・回帰手法を中心に、反復分割や統計検定で技術的に評価しているため。
abstractPlot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined.
Heat stress limits plant productivity by disrupting transpiration, altering leaf microclimate, and activating metabolic pathways that increase volatile organic compound (VOC) emissions. VOC signatures, combined with leaf-level relative humidity (RH), provide early indicators of plant stress, but conventional analytical methods are costly, bulky, and unsuitable for continuous in situ monitoring. Here, a low-cost multimodal sensing platform based on laser-induced graphene (LIG) is reported for real-time, on-leaf detection of methanol, acetic acid, and RH under ambient conditions. The platform integrates Pt-modified LIG electrodes with PtNP/ZnONR@ZIF-8 for methanol, PtNP/ZnONR@ZIF-8/Sn3O4 for acetic acid and GO:PDMAA for RH sensing. After optimization, the sensors respectively achieved sensitivities of −75.83 Ω/log(ppm), −1.63 Ω/ppm, and −4,776.01 Ω/%RH with detection limits of 0.382 ppm, 0.318 ppm, and 1.53 %RH and more than 97% signal retention over 26 days. On-leaf measurements over 2 weeks showed methanol increasing from ~0.3–13.8 ppm to ~13.5–59.0 ppm, acetic acid from ~6.6–14.8 ppm to ~23.3–60.6 ppm, and RH decreasing from ~72.2–86.3% to ~52.5–65.1% under heat stress. These coupled chemical and microclimate changes provide direct, dynamic stress readouts during plant monitoring. By moving beyond single-analyte measurements, the proposed multimodal approach enables early stress detection, data-driven crop management, and next-generation precision agriculture applications.
Why it matches plant phenotyping methods植物の熱ストレス状態を葉上のVOCと相対湿度から連続測定するセンサー基盤を開発・性能評価しており、植物状態の取得方法が中心的である。
abstractHere, a low-cost multimodal sensing platform based on laser-induced graphene (LIG) is reported for real-time, on-leaf detection of methanol, acetic acid, and RH under ambient conditions.
Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of N=150 observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved R2=0.842 and RMSE=0.0705 on a normalized target scale, corresponding to a 7.05% error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached 91.1% accuracy (Cohen’s Kappa =0.91). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline (ΔR2=+0.052) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物の水関連形質・水状態を推定する枠組みを開発・検証しており、表現型取得と予測手法が研究の中心である。
abstractWe developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the complete dataset and source code (raw UAV multispectral imagery, Python scripts, IoT sensor logs, CROPWAT 8.0 files, and XGBoost model code) in a public Mendeley Data repository, which directly reproduces this paper's phenotyping measurements and analysis.Dataset · publicThe complete dataset and source code supporting this study are publicly available at Mendeley Data: https://data.mendeley.com/datasets/9xwdvzf3bf/1 (accessed on 20 May 2026). The repository includes: (1) raw multispectral UAV imagery with calibration panel captures; (2) Python scripts for DN-to-reflectance conversion and spectral index extraction; (3) IoT sensor logs (soil moisture, temperature, relative humidity); (4) CROPWAT 8.0 project files for FAO-56 soil water balance simulation; and (5) XGBoost model source code with hyperparameter optimization routines.Open asset ↗Mendeley Data · 9xwdvzf3bf/1lines:193-228Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging.In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions.The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B 0 homogeneity, RF attenuation as well as potential RF artefacts) were verified.Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物の解剖学的・機能的MRI計測を可能にする環境制御チャンバーとインボア拡張を開発し、性能および植物での機能・解剖学的計測を検証しており、フェノタイピング手法が中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
This study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops under nitrogen (N) fertilization treatments, irrigation (I), and their interaction (N × I). The U-Net model outperformed all other methods, achieving accuracies for crop nitrogen status of 65–99% in N, 84–100% in I, and 41–82% in N × I treatments, with variation due to different input data. Supervised machine learning also performed well, with Support Vector Machine achieving 53–87, 66–86, and 32–66% respectively, and Random Forest 61–96, 70–81, and 33–65%. Unsupervised K-means yielded the lowest accuracies (47–58, 9–65, and 8–34%), demonstrating necessity of substantial supervision to delineate crop nitrogen and water status. These findings were confirmed by repeated analyses of UAV imagery acquired later in the growing season with consistent results. Comparable classification performance was observed for crop water status and leaf area index at both time points. Despite being demonstrated in a single-field, single-crop framework, the results provide proof of concept for applying deep learning classifiers to detect subtle nitrogen and water stress under field conditions in precision agriculture. Future research could test diverse agroecosystems and growing seasons, alternative deep learning algorithms, and sensor data fusion to improve classification accuracies.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からジャガイモの窒素・水分状態およびLAIを推定する分類手法を比較・検証しており、植物状態の取得と手法性能評価が研究の中心である。
abstractThis study compared unsupervised and supervised machine learning, and deep learning (U-Net) classifiers on Unmanned Aerial Vehicle (UAV) multispectral imagery to identify nitrogen status in potato crops
MaizeField / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Understanding evapotranspiration (ET) partitioning into soil evaporation (E) and plant transpiration (T) is crucial for improving agricultural water use efficiency in water-scarce regions. The isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized. This study systematically compared these two methods using two consecutive years (2012-2013) of field isotopic observations in a summer maize field on the North China Plain, a core maize production area facing severe agricultural water scarcity. Stable isotope analysis showed that the local meteoric water line (LMWL) had a slope lower than the global meteoric water line. The 0-5 cm surface soil water evaporation lines had slopes of 5.84 (2012) and 8.06 (2013), confirming significant evaporative enrichment in the topsoil. Plant water isotopic composition closely resembled that of 40-100 cm deep soil water, indicating limited root uptake from the surface layer. IMB-estimated transpiration ratio (T/ET) exhibited distinct phenological patterns, increasing from 37 to 44% at jointing to a peak of 94-96% at filling, then declining to 84-85% at maturity. The two methods agreed well during filling to maturity (differences of 2-10%), but compared with the IMB method, AquaCrop substantially underestimated T/ET at jointing (0.9% vs. 43.8% in 2013) due to its canopy-cover-based transpiration algorithm. These findings identify the filling stage as the critical water demand period, providing a quantitative reference for precision irrigation management under similar climate and soil conditions.
Why it matches plant phenotyping methodsトウモロコシの蒸散比を対象に、同位体質量収支法とAquaCropモデルを比較・検証しており、植物の水利用状態を取得する測定手法の性能評価が中心である。
abstractThe isotope mass balance (IMB) method and AquaCrop model are two widely used approaches for ET partitioning, yet their comparative performance across different crop growth stages remains poorly characterized.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
RiceSeed / grainPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traitsWater status / transpiration
Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.
Why it matches plant phenotyping methods穀粒含水率の画像推定モデルと生理学的閾値を統合し、脱水動態や成熟期などの植物形質を高スループットに抽出・検証する枠組みが研究の中心である。
abstractwe propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration
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
Field / plotLaboratory / benchtopLeafPhysiological trait estimationSegmentationWater status / transpiration
Abstract Climate-change-driven drought intensification increasingly threatens forest ecosystems, highlighting an urgent need for accurate monitoring of forest water stress. Leaf water potential (Ψleaf) is a key integrative indicator, yet conventional measurements are destructive and unsuitable for large-scale or high-frequency monitoring. Hyperspectral remote sensing offers a promising alternative, but robust canopy-level Ψleaf estimation remains constrained by limited labeled data and heterogeneous environmental conditions. Here, we develop a cross-scale framework integrating supervised contrastive learning with deep transfer learning to translate robust leaf-scale pretraining into canopy-scale Ψleaf estimation from hyperspectral data in a Populus × euramericana ‘I-214’ plantation. Hyperspectral imagery was captured at the leaf scale under controlled laboratory conditions (n = 229) and at the canopy scale using a UAV-based platform (n = 200), together with paired Ψleaf measurements. Reflectance consistently increased with declining Ψleaf at both scales, supporting the feasibility of cross-scale modeling. At the leaf scale, physics-consistent spectral augmentation coupled with contrastive learning enhanced feature discrimination and predictive stability under small-sample conditions (R2 = 0.8030). Transfer learning via progressive fine-tuning enabled efficient scaling of the leaf-level pretrained model to canopy-level prediction despite structural and environmental complexity and restricted field data ranges, achieving R2 = 0.7605 and RMSE = 0.1056 MPa. Coupling with individual-tree crown segmentation further enabled spatially explicit mapping of canopy Ψleaf and plot-level forest water stress dynamics. These results demonstrate that combining contrastive representation learning with cross-scale transfer provides a practical pathway for physiological monitoring and scalable, climate-smart forest phenotyping in data-constrained forested environments.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と深層学習により、ポプラの葉の水ポテンシャルを推定する手法を開発・評価しており、植物生理形質の取得が研究の中心である。
abstractHere, we develop a cross-scale framework integrating supervised contrastive learning with deep transfer learning to translate robust leaf-scale pretraining into canopy-scale Ψleaf estimation from hyperspectral data
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.
Why it matches plant phenotyping methodsマルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。
abstractthis work develops a regional PMC estimation approach by combining multi-source remote sensing data.
Leaf water status is a key indicator for irrigation scheduling and early stress detection, but many spectroscopic prediction studies have mainly relied on near-infrared features. Here, practical prediction models were developed using mid-infrared (MIR) ATR-FTIR spectra of capsicum (Capsicum annuum L.) leaves collected under glasshouse conditions during a 10-day gradual dehydration period, alongside an irrigated control. Spectra (4000-450 cm⁻1) were measured with minimal sample preparation, and leaf water traits were quantified using fuel moisture content (FMC), equivalent water thickness (EWT), and specific leaf weight (SLW). Water-related MIR bands at 3370 and 1641 cm⁻1 showed the most consistent response to dehydration, and simple band ratios generally provided stronger predictions than single bands. The best ratios were A1641/A2159 for FMC (R2 = 0.81; RMSE = 12.80) and A3370/A2849 for EWT (R2 = 0.72; RMSE = 0.0034) and SLW (R2 = 0.62; RMSE = 6.95 × 10⁻4), while predicted-versus-measured performance yielded R2 values of 0.72 for FMC, 0.68 for EWT, and 0.52 for SLW. These results indicate that MIR ATR-FTIR spectroscopy, when coupled with selected band ratios, can provide a rapid, low-preparation laboratory-based approach for estimating capsicum leaf water traits under controlled dehydration, supporting plant-based water stress assessment under controlled conditions and providing a basis for further irrigation-related sensing studies. However, the models are preliminary and require validation with larger independent datasets and tightly standardised measurement conditions before operational use in irrigation management.
Why it matches plant phenotyping methodsMIR ATR-FTIRスペクトルと選択バンド比を用いて、葉の水分形質を推定するセンシング・予測手法の開発と性能評価が中心である。
abstractHere, practical prediction models were developed using mid-infrared (MIR) ATR-FTIR spectra of capsicum (Capsicum annuum L.) leaves
Field / plotWhole plant / canopy / plot / fieldObject detectionPhysiological trait estimationStress / disease detectionGrowth / development / phenologyPigment / colour / senescenceStress response / toleranceWater status / transpiration
Plant wearable sensors are emerging as flexible, non-invasive platforms for continuous assessment of plant physiological status and plant–environment interactions. This review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses. It summarises major sensing approaches, including capacitive, chemical, photodetector-based and piezoresistive sensors, with attention to their materials, fabrication strategies, operating principles and potential applications in plant health monitoring. Advances in flexible substrates, conductive materials, nanostructured sensing layers, biodegradable polymers and wireless communication have improved sensor compatibility with plant surfaces and enhanced the detection of physiological changes under field-relevant conditions. Integration with the Internet of Things, artificial intelligence, machine learning, cloud platforms and data analytics further supports continuous data acquisition and interpretation for precision crop management. These systems may contribute to early detection of biotic and abiotic stresses, enabling timely interventions and improved resource-use efficiency. However, broader adoption remains limited by sensor durability, environmental interference, power requirements, scalability, cost and the complexity of interpreting plant-derived signals. Continued interdisciplinary research is required to develop reliable, affordable, energy-efficient, biodegradable and multifunctional sensing platforms that support sustainable agricultural management under changing environmental conditions.
Why it matches plant phenotyping methods植物の生理状態・成長・クロロフィル・ストレス応答を測定するウェアラブルセンシング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が中核である。
abstractThis review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses.
Low-cost RGB imaging is accessible for phenotyping, but color varies with devices and illumination. We tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage. Leaves (n=80) from three growth-chamber experiments were imaged in a closed light-tent with an in-frame gray/white/black card, then corrected in Adobe Photoshop. Mean leaf RGB values (manual ROIs) were used to compute 15 RGB/CIELAB indices, which were screened against SPAD, specific leaf area (SLA), vein density, water content (WC), stomatal density, and stomatal size using Pearson r and second-order regression (adj. R², NRMSE). The strongest relationships were for SLA (h_ab; adj. R²=0.666), vein density (TGI; adj. R²=0.610), and SPAD (G; adj. R²=0.558). WC was moderately associated with c_ab (adj. R²=0.344), while stomatal traits were weakly explained, consistent with scale limits of top-down mean-color metrics. Standardized consumer RGB imaging can therefore support rapid first-pass screening of pigment- and structure-related leaf traits.
Why it matches plant phenotyping methods標準化スマートフォンRGB撮像と色補正・指数計算を用いて葉形質を推定し、SPAD、SLA、葉脈密度などとの関係を定量評価しているため、画像フェノタイピング手法の検証が中心である。
abstractWe tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage.
Xylem tissue enables efficient long-distance water transport but is a primary target for vascular pathogens. This study investigates how systemic invasion by Verticillium dahliae impairs the hydraulic function of pepper (Capsicum annuum) plants, focussing on xylem colonisation and its anatomical and physiological effects. Real-time sap flow was continuously monitored with custom-built ExoBeat sensors, while periodic stem water potential measurements allowed calculation of changes in stem hydraulic conductance as an additional indicator of xylem performance. Fungal colonisation was assessed by quantitative polymerase chain reaction, and vessel occlusions and embolised conduits were visualised using scanning electron microscopy and micro-computed tomography, complemented by direct hydraulic conductivity measurements. By 14 d post inoculation, V. dahliae had progressed from roots to aboveground tissues, coinciding with a marked decrease in sap flow, water potential, and soil-to-stem hydraulic conductance, alongside the onset of dwarfing. Direct fungal blockage and anatomical changes were the primary contributors to hydraulic dysfunction. Vessel occlusion by tyloses, gels, and air embolisms played a negligible role. This study reveals how V. dahliae progressively impairs pepper hydraulics through systemic xylem colonisation, highlighting the value of real-time sap flow monitoring. Our integrative, multidisciplinary approach offers a powerful framework to unravel the complexity of dynamic plant-fungal vascular interactions.
Why it matches plant phenotyping methodsカスタムセンサーによるリアルタイム・サップフロー測定を中心に、植物の水理機能・病原体による機能低下を定量化しており、単なる生物学的測定にとどまらない実質的なフェノタイピング手法の適用である。
abstractReal-time sap flow was continuously monitored with custom-built ExoBeat sensors
Abstract Climate change and increasing drought frequency are intensifying water stress risks in viticultural systems, particularly in semi-arid regions where water availability directly influences grapevine productivity and quality. This study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery. High-resolution orthomosaics (2.31 cm pixel⁻¹) were acquired with a DJI Mavic 3 Multispectral platform and used to generate Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Red Edge Index (NDRE) maps for both phenological periods. Water stress responses were evaluated through pixel-based change detection, descriptive statistics, spatial heterogeneity metrics (Local Mean and Local Standard Deviation), and Global Moran’s I spatial autocorrelation analysis. Results revealed measurable spectral differences between pre-harvest and post-harvest periods, indicating changes in canopy physiological activity associated with water stress and post-harvest vine responses. GNDVI exhibited a higher relative change (+ 10.49%) and stronger standardized response ratio, suggesting greater sensitivity to overall canopy vigor and photosynthetic activity. In contrast, NDRE showed the highest heterogeneity response (+ 64.97%), demonstrating superior capability for identifying localized stress variability and fine-scale physiological differences within the vineyard. Spatial heterogeneity analyses indicated increasing local variability after harvest, while consistently positive Moran’s I values (≈ 0.257) revealed that stress patterns remained spatially organized rather than randomly distributed. These findings suggest that vineyard water stress is controlled not only by vine physiology but also by persistent spatial factors such as soil conditions, micro-topography, and water availability. Overall, the combined use of GNDVI and NDRE provided complementary information for vineyard water stress assessment. The proposed framework demonstrates the potential of UAV-based multispectral monitoring for precision irrigation management, early stress detection, and climate-resilient viticulture in water-limited environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からGNDVI・NDREを抽出し、ブドウ樹の水ストレスと空間変動を評価する測定・解析フレームワークが研究の中心である。
abstractThis study investigated pre-harvest and post-harvest water stress dynamics in a commercial Ağın Beyazı vineyard located in Eastern Türkiye using UAV-based multispectral imagery.
Abstract In the era of climate change, drought is defined as one of the most severe natural catastrophes that affects the environment, crop growth, and water resources, leading to economic losses, migration, and risks to human life. Since 2019, Morocco has suffered one of the most severe droughts in its recording history, coinciding with a broader period of precipitation deficit across the Mediterranean basin, resulting in a significant reduction in reservoir storage levels and the suspension of irrigation provided by dams in some areas due to low or absent rainfall, making drought a serious challenge to natural resources in this country. This study focused on semi-arid regions, especially on the Tensift basin in Morocco, and was conducted between 2018 and 2024. The effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area. The analyses indicated that arboriculture cover decreased markedly, from 11.17% in 2020 to 7.40% in 2023. From the analyses, it was evident that the cover of this culture had significantly declined from 11.17% in 2020 to 7.40% in 2023. From 2018 to 2024, more than half of the area suffered from drought in the agriculture of varying intensity levels, from moderate to severe. The meteorology of the evaluations confirmed the above observations by showing that there was a considerable decline in precipitation levels from about 350 mm in 2019 to less than 50 mm in 2024, coupled with continuously negative SPI-6 indices. Moreover, it was established that there was a significant effect on tree crops such as olives and citrus. Degradation of land was at its worst during 2021 when it affected more than 3,000 hectares of olives and 2,250 hectares of citrus. Similarly, 80% of farmers engaged in the production of citrus registered a decline in yield from 37% to 41%. Consequently, this study provides new information concerning the need for comprehensive evaluation and monitoring of agricultural drought in Morocco, thereby emphasizing the importance of essential factors.
Why it matches plant phenotyping methodsリモートセンシングと複数の植生・水ストレス指数を中核に、オリーブ・柑橘樹の植生健康状態や干ばつ影響を評価しており、植物状態の抽出手法の実質的応用に該当する。
abstractThe effects of drought on arboriculture were analyzed thanks to remote sensing by using the normalized difference vegetation index (NDVI), while NDVI, TCI (temperature condition index), VCI (vegetation condition index), VHI (vegetation health index), and SPI (standardized precipitation index) were employed to assess and evaluate the health of the vegetation area, especially the arboriculture area, and the effective water stress conditions in our agricultural study area.
Why it matches plant phenotyping methods植物の水分状態を測定する古典的方法（重量法、プレッシャーチャンバー、サイクロメトリ等）を中心に原理・手順・検証用途をレビューしており、植物生理形質のフェノタイピング方法レビューに該当する。
abstractIn Part 1 of a two-part review, we provide insights into using leaf water content as a reliable proxy for assessing water status and synthesize classical, destructive methods for measuring plant water status, encompassing gravimetric techniques, Scholander pressure chamber and psychrometric techniques.
Why it matches plant phenotyping methods植物の水分状態を非破壊・遠隔センシングで測定する手法を体系的に比較・評価したレビューであり、植物フェノタイピング手法が中心です。
abstractThis second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations.
Chlorophyll content (represented by the Soil and Plant Analyzer Development (SPAD) value) and leaf moisture content (LMC) are two key physiological phenotypic traits during wheat growth, and their variations among different wheat varieties reflect crop growth, stress response, and breeding evaluation. Simultaneous identification of wheat varieties and prediction of SPAD and LMC are therefore important for precision crop monitoring. In this study, hyperspectral imaging was employed to acquire leaf spectral information from four wheat varieties. After spectral preprocessing and outlier screening, 684 valid samples were retained for model development and evaluation. Single-task and multi-task models were constructed for wheat variety classification, SPAD prediction, and LMC prediction using support vector machine (SVM), partial least squares (PLS), convolutional neural network (CNN), and multi-task CNN (MLT-CNN) algorithms. In the MLT-CNN, a shared one-dimensional spectral feature extraction module and three task-specific branches were designed, and equal weight strategy (EWS), adjustable regularization weighted strategy (ARWS), and uncertainty-based weighted strategy (UWS) were compared. The best single-task models achieved a test-set classification accuracy of 0.75, with correlation coefficients (r) of 0.83 for SPAD and 0.84 for LMC. The MLT-CNN with EWS achieved balanced test-set performance across the three tasks, with a classification accuracy of 0.69, an r value of 0.84 for SPAD, and an r value of 0.81 for LMC. Shapley additive explanations (SHAP)-based visualization was further performed for both single-task CNNs and MLT-CNN task branches to identify important wavelengths and interpret shared and task-specific spectral contributions. These results indicate that hyperspectral imaging combined with multi-task learning provides a feasible and interpretable spectroscopic strategy for integrated wheat leaf phenotyping.
Why it matches plant phenotyping methodsハイパースペクトル画像からSPAD値と葉含水量という植物生理形質を推定し、複数の機械学習モデルとマルチタスク構成を開発・評価した研究であり、フェノタイピング手法が中心である。
abstractIn this study, hyperspectral imaging was employed to acquire leaf spectral information from four wheat varieties.
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。
abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE
Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R2p = 0.86), OCDM (R2p = 0.86), and M (R2p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R2p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring.
Why it matches plant phenotyping methods低コスト multispectral センサーとANNによるオリーブ果実の品質形質推定システムを開発・外部検証しており、植物形質取得法が中心的である。
abstractwe propose a methodology using a custom-made, low-cost multispectral device.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
Field / plotGreenhouseGrowth chamberLeafPhysiological trait estimationGrowth / time-series analysisWater status / transpiration
O_LITranspiration plays a central role in plant water relations and strongly influences plant growth. Continuous monitoring is essential for understanding responses to environmental conditions and improving water management in both natural and agricultural systems. Gas-exchange techniques such as infrared gas analysers (IRGAs) and porometers are widely used but are challenging for long-term or large-scale monitoring. On the other hand, the FylloClip is a low-cost, leaf-mounted capacitance sensor developed previously to monitor transpiration by detecting condensation of water vapour near the leaf surface. Here, we evaluated the potential of the FylloClip for monitoring transpiration dynamics and assessed environmental conditions that may affect its performance. C_LIO_LIThe FylloClip was tested under growth chamber, greenhouse, and tropical field conditions. We evaluated how its capacitance measurements respond to rainfall, temperature and humidity, and compared FylloClip measurements with transpiration measured with an IRGA. C_LIO_LIThere was a strong correlation (r = 0.85) between FylloClip and IRGA data. Both systems captured similar diurnal transpiration patterns, with transpiration declining simultaneously under water deficit. Rainfall and very high relative humidity produced FylloClip signals that could be misinterpreted as high transpiration, although transpiration is negligible under these conditions. C_LIO_LIOur results revealed that FylloClips capture temporal patterns of transpiration with high accuracy and resolution, providing a reliable tool for long-term, large-scale monitoring of transpiration dynamics in ecophysiological studies and precision agriculture. C_LI
Why it matches plant phenotyping methods葉面センサーによる蒸散動態測定法を開発・評価し、IRGAとの比較検証および環境条件による性能評価を行っており、植物生理形質の取得が中心である。
abstractHere, we evaluated the potential of the FylloClip for monitoring transpiration dynamics and assessed environmental conditions that may affect its performance.
TissuePhysiological trait estimationStress response / toleranceWater status / transpiration
Drought-driven plant mortality is closely linked to xylem embolism. Building useful, reliable datasets of xylem vulnerability to embolism requires methods that are practical, fast, accurate, widely accessible and robust across growth forms. We tested and advanced the pneumatic method for constructing xylem vulnerability curves (VCs) across contrasting growth forms to improve inference of drought resilience. Using an automated pneumatron, VCs were constructed for three species representing a small woody shrub (Erica monsoniana), a large woody shrub (Protea repens), and a reed-like graminoid (Cannomois congesta). For graminoid culms, we compared three approaches for estimating xylem water potential (Ψ) and developed a non-invasive method that couples repeated relative water content (RWC) measurements with Ψ-RWC models to obtain high-temporal Ψ estimates. Percent air discharged (PAD)-Ψ relationships were well captured by sigmoid functions. Cannomois congesta showed the steepest curves and the least negative thresholds overall (P 50 = -2.91 ± 0.09 MPa), indicating early, rapid embolism progression, whereas Erica monsoniana was most resistant (P 12 = -5.91 ± 0.74 MPa; P 50 = -6.78 ± 0.76 MPa) with higher variability; Protea repens was intermediate. P 50 estimates were the most comparable with prior optical, pneumatic and centrifuge estimates, whereas P 12 and P 88 showed greater divergence. Ψ TLP was less variable between species, but ranked similarly (-1.49 ± 0.03, -1.53 ± 0.03, -1.59 ± 0.02 MPa for C. congesta, P. repens, and E. monsoniana, respectively). Such variation yielded systematically wider hydraulic safety margins for the three species. By demonstrating that the pneumatic method can generate reliable vulnerability curves across small and large woody shrubs and graminoids, this study broadens the comparative evaluation of xylem vulnerability across growth forms with contrasting anatomy. A practical advance is the use of repeated RWC measurements paired with Ψ-RWC relationships to improve Ψ resolution in graminoid culms while minimizing disturbance.
Why it matches plant phenotyping methods植物の木部キャビテーション脆弱性を測定する空気圧法を改良・比較検証し、反復RWC測定による非侵襲的な水ポテンシャル推定も開発しているため、植物生理形質の取得法が研究の中心です。
abstractWe tested and advanced the pneumatic method for constructing xylem vulnerability curves (VCs) across contrasting growth forms to improve inference of drought resilience.
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.
The high spatio-temporal resolution of UAV sensors requires robust analytical tools to classify subtle agroecosystem variations. This study compared unsupervised, supervised machine learning (ML), and deep learning (U‑Net) classifiers to identify nitrogen (N) and water (I) status, and their interaction (N×I) in potato crops using UAV multispectral imagery. The U‑Net model outperformed all other methods, achieving accuracies of 85% (N), 93% (I), and 70% (N×I). Supervised ML classifiers also performed well and Support Vector Machine achieved 71, 62, and 40% respectively, whereas Random Forest achieved 67, 61, and 40%. The unsupervised K‑means classifier yielded the lowest accuracies (41, 36, and 23%), demonstrating the necessity of substantial supervision to delineate crop N and water properties. These results were confirmed by repeated analysis on UAV imagery acquired later in the season. Deep learning classifiers should be adopted more widely in precision agriculture, as they offer new potential for optimizing N and irrigation co-management under field conditions with subtle spatial variation that is otherwise difficult to capture. Future research should test alternative deep learning algorithms and sensor data fusion to further improve classification accuracies.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からジャガイモの窒素・水分状態を推定し、複数の分類器を比較・反復検証しており、植物状態の取得・推定手法が研究の中心である。
abstractThis study compared unsupervised, supervised machine learning (ML), and deep learning (U‑Net) classifiers to identify nitrogen (N) and water (I) status, and their interaction (N×I) in potato crops using UAV multispectral imagery.
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.
Abstract Real-time detection of plant stress and timely delivery of protective biomolecules are essential for improving crop resilience under adverse environmental conditions. However, conventional plant monitoring systems typically rely on ambient measurements and passive treatment strategies that fail to enable targeted plant recovery based on their localized physiological conditions. As a result, current approaches largely operate as open-loop systems, where sensing and intervention are not directly integrated, limiting the ability to respond dynamically to plant stress. This study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system. The objective of this work was to develop a platform capable of monitoring transpiration driven humidity changes at the leaf surface and enabling controlled hormone delivery based on plant physiological responses. A temperature responsive hydrogel encapsulating SA was synthesized to achieve sustained biomolecule release while minimizing initial burst release. Salicylic acid release kinetics were evaluated using multiple mathematical models, with the Korsmeyer–Peppas model providing the best fit (R² = 0.9978), indicating that SA release was governed primarily by polymer relaxation and degradation mechanisms. Leaf-level relative humidity was continuously monitored on the abaxial surface under different treatment conditions. Plants treated with the hydrogel-based SA delivery system showed improved drought tolerance, with localized relative humidity increasing from approximately 20–30% in stressed plants to 60–70% after treatment, while untreated stressed plants did not show any noticeable recovery. This improvement was further supported by measurements of stomatal aperture, which showed a mean opening of 1.932 micrometers in treated plants, compared to 0.396 micrometers in untreated plants. SA treated seeds also demonstrated accelerated germination within 14 days. These findings demonstrate the potential of integrating plant wearable sensors with stimulus responsive biomaterials to establish closed-loop plant healthcare systems that couple physiological sensing with adaptive intervention.
Why it matches plant phenotyping methods葉面湿度を連続測定して植物の生理状態（蒸散・ストレス回復）を推定するセンサーと、応答型処置を統合した植物フェノタイピング／ケア基盤の開発が中心である。
abstractThis study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system.
MangoTobaccoMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
This study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer. The approach enhanced model performance by fusing spectral features with some auxiliary factors of the samples, such as the locality of growth (region), type of produce (cultivar), and sample temperature (temp). Spectral data were extracted using one-dimensional CNN to capture local spectral features, while auxiliary factors underwent sine-cosine or label encoding before being embedded into the same feature space as spectral data via a fully connected network. Ultimately, a transformer was employed to achieve global interaction and fusion between spectral features and auxiliary factors rather than merely concatenating different feature types. The fusion strategy was validated using the ultraviolet (UV)-visible (vis)-near-infrared (NIR) spectra of mango and tobacco data sets. Compared to single-modal models using spectra only, the multimodal model using spectra coupled with the auxiliary factors achieved improved prediction performance on both validation and test sets for the mango dry matter content (DMC). The RMSE decreased from 0.984 and 1.03 to 0.577 and 0.613, respectively. These results outperformed those of the other 11 machine learning models. SHAP analysis revealed that the CNN-transformer framework successfully captured the underlying relationships between auxiliary factors (region, temp, and cultivar) and spectral features near 960 nm (due to the O-H absorption signal) with DMC, with the former contributing more significantly to the model than the latter. Similar observations were obtained in the tobacco data set. The results demonstrated the advantages of the CNN-transformer multimodal model in overcoming the limitations of single-modal information, providing novel technical support for quantitative analysis.
Why it matches plant phenotyping methodsCNN-Transformerによるスペクトルと補助情報の融合モデルを開発・検証し、マンゴーの乾物含量という植物器官の形質を定量推定しているため、方法が中心的である。
abstractThis study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer.
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:216-267Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Unmanned aerial vehicle (UAV) based remote sensing has emerged as a disruptive technology for detecting crop water stress (CWS) in real time, precisely and at low cost offering significant advancements over conventional approaches. The study examined the red green blue (RGB), multispectral (MSP), hyperspectral (HSP), thermal image sensors integrated with UAVs, which offers a high-spatial and temporal resolution of physiological indicators such as chlorophyll content and canopy cover, canopy temperature, stomatal conductance. The study highlights that in spring maize, random forest (RF) models using UAV-derived MSP and thermal indices with leaf area index (LAI) performed well (R² > 0.575, root mean square error (RMSE)
Why it matches plant phenotyping methodsUAV搭載センサーによる作物の水ストレスや生理形質のモニタリング技術をレビューしており、表現型取得法が中心である。
titleRecent trends in crop water stress monitoring using remote sensing technologies: A review
Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。
abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).
Abstract The Malaysian jackfruit industry is increasingly threatened by “jackfruit-bronzing,” a disease caused by Pantoea stewartii subsp. stewartii , which manifests as yellowish-orange to reddish discoloration of the pulp while leaving the rind visually unaffected. The cv. ‘Tekam Yellow’ cultivar is particularly vulnerable, resulting in substantial postharvest losses. This study explores the feasibility of employing visible near infrared spectroscopy (Vis-NIRS) as a non-destructive method to predict internal bronzing through the estimation of rind or flesh firmness and rind, flesh or seed moisture content. Spectral reflectance data were acquired non-destructively from the rind surface of jackfruit, and the resulting spectra were used to predict rind firmness, and moisture content of rind, flesh, and seed tissues. Jackfruits at 10, 12, and 14 weeks after anthesis (WAA) were analyzed within the 500–950 nm wavelength range. Partial least squares regression (PLSR) models were developed and optimized using preprocessing techniques such as Savitzky–Golay smoothing, standard normal variate (SNV), and multiplicative scatter correction (MSC). The best-performing models yielded high determination coefficients for both calibration (Rc²) and validation (Rv²), reaching up to 0.99, with root mean square error of calibration (RMSEC) and validation (RMSEV) values as low as 0.67 N and 0.74% w.b., respectively. Destructive reference measurements were conducted in parallel and analyzed using ANOVA and Fisher’s protected least significant difference (FPLSD) test at p ≤ 0.05. Results demonstrated that Vis–NIRS applied through the rind surface provided reliable prediction of firmness and moisture-related attributes associated with internal bronzing disorder in jackfruit. The developed approach shows strong potential as a rapid and non-invasive technique for early bronzing detection and postharvest quality assessment in jackfruit.
Why it matches plant phenotyping methodsVis-NIRSによる非破壊的な植物器官の硬度・含水率推定と、内部障害の早期検出モデル開発・検証が研究の中心であるため。
abstractThis study explores the feasibility of employing visible near infrared spectroscopy (Vis-NIRS) as a non-destructive method to predict internal bronzing through the estimation of rind or flesh firmness and rind, flesh or seed moisture content.
CottonAerial / UAVMultispectral / hyperspectralLeafPhysiological trait estimationWater status / transpiration
Optimizing water and fertilizer management is crucial for improving cotton yield and quality. However, reliable and generalizable models for quickly and accurately estimating cotton canopy leaves water and nutritional status at a low cost throughout the entire growth stage are scarce. Therefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning. In the hyperspectral reconstruction module, the new hyperspectral reconstruction model (swinT-HSCNN) based on multispectral showing superior performance in reducing pixel-scale systematic errors and effectively captured spectral variations than HSCNN+ and MST++ model. In the PROSAIL module, the proposed Original-E2DCOS method demonstrated greater sensitivity to spectral response characteristics, especially for parameters and bands with low correlation values, and three bands (702 nm, 762 nm, and 938 nm) were selected as the sensitive bands corresponding to chlorophyll content (Cab) and equivalent water thickness (Cw) of cotton. The improved PROSAIL with hyperparameter optimization based on full spectrum and multispectral band shown better fitting performance than the model based on sensitive bands, and achieved high accuracy on simulated data, with R 2 values exceeding 0.98 for both Cab and Cw. Moreover, the new developed modular transfer learning retrieval model of cotton canopy water and nitrogen content through PROSAIL model and hyperspectral reconstruction with UAV multispectral imagery achieved good inversion accuracy with R 2 of 0.83, 0.85, RMSE of 0.0048, 0.0052, for LNC and EWT, respectively after verifying with actual experiment data. In summary, the proposed modular transfer learning retrieval model of cotton canopy water and nitrogen content integrates physical constraints into retrieval models, which enhancing their accuracy and generalization capability, and providing valuable technical support for precision agriculture in cotton production across different regions. • A modular transfer learning model integrates PROSAIL and hyperspectral reconstruction. • Achieves high accuracy for LNC and EWT estimation across diverse environments. • Combines UAV multispectral data with physical constraints for crop monitoring. • Reduces reliance on expensive hyperspectral sensors, ensuring cost-effectiveness. • Validated on multi-regional datasets, demonstrating scalability and generalizability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL、ハイパースペクトル再構成、転移学習を統合し、ワタの窒素・水分状態という植物形質を推定する手法を開発・実データで検証しており、フェノタイピング手法が中心である。
abstractTherefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.
Why it matches plant phenotyping methods植物の3D形態とハイパースペクトル情報を統合してSPAD・EWTを推定する手法を開発し、予測性能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。
abstractHere, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology.
Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-adapted germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.
Why it matches plant phenotyping methods多センサー表現型プラットフォームを用いた画像ベースのデジタル biomass 推定を検証し、植物形質評価への利用可能性を示しており、表現型取得法が中心的です。
abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.
Deficit irrigation (DI) is a crucial strategy for optimizing water use in arid and semi-arid agriculture, yet its success depends on accurately determining the crop yield response factor (K y ). This study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators. fused Landsat 7/8/9 and MODIS data within the Google Earth Engine platform to generate high-resolution daily Crop Water Stress Index (CWSI) maps for the 2023 season in southern Iran. The Random Forest (RF) algorithm was applied to correct biases in land surface temperature (LST), achieving high accuracy (RMSE < 1.0°C, nRMSE < 3%, rMBE ≈ 0%) before CWSI calculation. Ground measurements from 12 field points, including canopy temperature and yield, were used for calibration and validation. The satellite-based CWSI showed strong agreement with field data (RMSE = 0.05, nRMSE = 11%), with values ranging from 0.18 to 0.71 at dekadal scale. Using this CWSI, K y was computed at dekadal, monthly, and seasonal scales, revealing substantial spatiotemporal variability (0.2–1.63) and an average seasonal K y of 1.05. This value is lower than the FAO-66 default of 1.2, indicating that the standard coefficient may prompt over-irrigation without yield benefits. The analysis further identified early July as the period of peak water stress sensitivity, with K y values exceeding 1.82. This ML-enhanced, satellite-based approach provides a robust tool for deriving spatially explicit K y values, offering a significant advancement for precision irrigation planning and water resource management.
Why it matches plant phenotyping methods衛星データと機械学習で作物の水ストレス状態（CWSI）を推定し、地上測定で較正・検証する手法が中心であるため、植物生理状態のセンシング型フェノタイピングとして含める。
abstractThis study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators.
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
AppleField / plotRaman / spectroscopyFruitClassificationWater status / transpiration
Visible and near-infrared (Vis/NIR) spectroscopy has been widely applied in fruit quality detection due to its advantages of rapid efficiency, non-invasiveness, and suitability for detecting opaque samples. To address the issue of whether apple watercore occurs during the growth and maturation of apples, a portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore. The device employs the AIOX2000-13 spectrometer as the detection unit, with an STM32F103VET6 ARM-based processor as the main control chip, and integrates a 4G wireless communication module to establish a stable data transmission channel between the processor and the computer. This structure ensures the efficient and stable transmission of apple spectral data and detection results, thereby meeting the need for in-field nondestructive detection of apple watercore on apple trees. The system is based on a self-designed spectral data acquisition mechanism and uses a transmission detection method to collect spectral data from 500 'Fuji' apple samples in two directions. The spectral data were preprocessed using Standard Normal Variate (SNV), and the dataset was divided using the Spectral Projection based on X-Y distances (SPXY) algorithm. Important feature wavelengths related to apple watercore were extracted by combining the Uninformative Variable Elimination method with the Successive Projections Algorithm (UVE-SPA). Subsequently, a detection model, SNV-UVE-SPA-SVM, was constructed using a Support Vector Machine (SVM) optimized by the Honey Badger Algorithm (HBA), achieving a test set accuracy of 96%. After research and analysis, Direction 1 was identified as the optimal acquisition direction, and field verification was conducted on 50 apple samples, with a detection accuracy of 94%. The results show that the detection device has the advantages of portability, high efficiency, and suitability for in-field detection, making it suitable for the rapid in-field detection of apple watercore.
Why it matches plant phenotyping methodsリンゴの水心症という植物状態を対象に、可視・近赤外分光による携帯型非破壊検出装置と解析モデルを開発し、圃場検証まで実施しており、表現型取得手法が中心である。
abstracta portable on-line nondestructive detection device based on Vis/NIR spectroscopy was designed to achieve accurate detection of apple watercore.
Abstract Purpose Capturing rapid changes in water status is key to optimizing deficit irrigation in Mediterranean orchards, but thermal remote sensing is constrained by the availability of high-spatial-resolution data. This study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards. Methods An experiment was conducted in two commercial orchards in south-eastern Spain, where mild water stress was induced by withholding irrigation for four days. High-resolution hyperspectral and thermal imagery were acquired concurrently with stem water potential measurements (ψ stem ). Structural, pigment-related, and water-sensitive indices were evaluated at high (20–50 cm) and medium (30 m) spatial resolutions to analyze the effects of pixel size on stress detection. The Crop Water Stress Index (CWSI), derived from thermal imagery, served as a reference indicator. Results Those optical indices based on SWIR reflectance at 1240 nm, the Normalized Difference Water Index (NDWI₁₂₄₀) and the Simple Ratio Water Index (SRWI), showed the strongest sensitivity to ψ stem variability (R² = 0.63, p
Why it matches plant phenotyping methods桃樹の水ストレス状態を高解像度ハイパースペクトル・熱画像とスペクトル指標で推定し、茎水ポテンシャルを用いて検証しており、植物表現型取得手法が中心である。
abstractThis study assesses the ability of visible, near and shortwave infrared (VNIR/SWIR) indices to detect short-term water stress in peach orchards.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.
Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.
Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。
abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.
TissuePhysiological trait estimationWater status / transpiration
Process-based models that mechanistically represent water-carbon balances in the atmosphere-soil-plant continuum are an attractive tool for monitoring live fuel moisture content (LFMC) dynamics, a key variable when assessing fire danger. However, their application as operational tools to assess near-term wildfire danger at regional scale faces important challenges. Here, we explored key sources of prediction uncertainty in process-based modeling of LFMC. We applied the SurEau-ECOS model of plant hydraulics embedded within the MEDFATE modeling framework to assess how the accuracy of LFMC predictions was influenced by input data sources, by the availability of species-specific plant traits and by the level of mechanistic detail used to model water content of plant tissues. A lack of accurate data describing soil physical properties compromises the application of process-based models for predicting LFMC. Nonetheless, using global meteorological and vegetation data allows for successful regional-scale applications. Fully mechanistic approaches that model LFMC from plant water status using ecophysiological knowledge yield more accurate predictions. However, when reliable plant traits are lacking, semimechanistic approaches based on empirical equations offer a robust alternative. Overall, addressing the sources of uncertainty highlighted here could pave the way for developing operational tools to forecast near-term wildfire danger through process-based modeling of LFMC dynamics.
Why it matches plant phenotyping methods植物の生体燃料水分量（LFMC）という生理状態の推定モデルを対象に、入力データ、植物形質、機構的詳細度が予測精度へ与える影響と不確実性を評価しており、植物状態の取得・推定手法が中心である。
abstractHere, we explored key sources of prediction uncertainty in process-based modeling of LFMC.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the LFMC field data (Catalan and Reseau–Hydrique networks) and the analysis/figure code in a public GitHub repository, which directly reproduces this paper's phenotyping measurements (7203 LFMC values) and computational analysis. Supporting Information TablesSCode · publicof the ‘Severo Ochoa’ Centres of Excellence programme, Ref. CEX2023‐001340‐S, funded by MICIU/AEI/ https://doi.org/10.13039/501100011033 . Also it was supported by the Spanish Government project IMPROMED (grant no. PID2023‐152644NB‐I00).
Data availability
The data and code for analyses and figures are available through GitHub ( https://github.com/emf‐creaf/LFMC_FR_CAT ). Also, the data that support the findings of this study are available in the Supporting Information of this article, specifically in Tables S1–S3 .
References
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Second‐growth forests exhibit higher sensitivity to dry and wet years than long‐existing ones
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28 : 6Open asset ↗emf‐creaf/LFMC_FR_CATlines:253-664Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Stem / branchPhysiological trait estimationWater status / transpiration
Abstract. Recent studies have reported widespread presence of hydrogen isotope offset (HIO) between cryogenically-extracted plant stem and soil water, challenging the long-standing assumption that the isotopic composition of stem xylem water reliably represents that of its source water. Despite intensive researches on this topic over the past decade, it remains debated as to whether and/or to what extent HIO originates from extraction-related artifacts or from in situ isotope mixing/fractionation during water transport from soil to plants. Here, we used cryogenic vacuum distillation (CVD) to extract stem and soil water from eight species (trees, shrubs, and grasses) grown under two humidity regimes. We quantified species-specific HIO, tested its associations with ecophysiological and environmental variables, and conducted immersion-based rehydration experiments to assess CVD-induced biases. Across species, HIO ranged from −7.2‰ to 3.2‰: trees were consistently negative, whereas shrubs and grasses were near-zero to slightly positive. Rehydration experiments revealed CVD-induced δ2H biases in stem (−4.5‰) and soil water (−2.5‰). When these extraction-related biases in both stem and soil water were simultaneously corrected, species-level HIO (mean = 0.2‰) was no longer different from zero, and showed no significant correlations with ecophysiological or environmental variables. These results suggest that apparent HIO is largely driven by CVD-induced artifacts rather than ecophysiological/environmental processes that cause isotopic fractionation during water transport along the soil-xylem continuum. We conclude that simultaneously correcting CVD-induced biases in both stem and soil water is critical to avoid spurious HIO signals and to improve isotope-based estimation of plant water sources.
Why it matches plant phenotyping methods植物茎水・土壌水の同位体組成測定におけるCVD抽出バイアスを再水和実験で検証・補正しており、植物の水源推定に関わる測定法の技術的妥当性が中心である。
abstractWe quantified species-specific HIO, tested its associations with ecophysiological and environmental variables, and conducted immersion-based rehydration experiments to assess CVD-induced biases.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。
abstractIn this review, we argue that developing climate-resilient forests requires looking below the canopy.
Abstract Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging. In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions. The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B0 homogeneity, RF attenuation as well as potential RF artefacts) were verified. Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物のMRI計測を可能にする環境制御・MR互換チャンバーを開発し、性能および植物の解剖・通道機能計測で検証しており、フェノタイピング手法と基盤が研究の中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Unmanned aerial vehicle (UAV)-based phenotyping has been applied to assess potato traits, however, its use to identify canopy traits associated with tuber yield across diverse genotypes remains limited. The objective of this study was to evaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield and it´s agronomic components in a set of eight potato genotypes grown across two environments and two growing seasons. Despite higher seasonal rainfall in Chiloé, tuber yields were consistently greater in Osorno, underscoring that total precipitation alone is less important than its temporal distribution and effective crop water availability; this makes it necessary to supplement with irrigation during the period of highest demand. RGB-derived vegetation indices and canopy temperature successfully differentiated genotypes, although their discriminatory power varied according to developmental stage and environmental conditions, with intermediate to late growth stages generally providing the strongest genotype separation. Canopy temperature supplied complementary physiological information related to canopy water status, whereas RGB traits captured broader variation in canopy structure and greenness. These findings highlight the importance of integrating phenological stage and environmental context when interpreting remote sensing data, and demonstrate the strong potential of UAV-based HTP to support breeding and agronomic strategies aimed at improving drought resilience, yield stability, and selection efficiency in potato.
Why it matches plant phenotyping methodsUAVによるRGB・熱画像を用いた圃場フェノタイピングが中心で、ジャガイモのキャノピー形質を抽出・評価し、遺伝子型間比較や収量関連性を検討している。
abstractevaluate the use of UAV-based field phenotyping, integrating RGB and thermal imaging, to identify key canopy traits associated with tuber yield
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.
Abstract Background Faba bean is an important grain legume in temperate cropping systems because it provides protein-rich seed and contributes biological nitrogen fixation. However, its productivity is highly sensitive to drought, and breeding for improved drought performance is constrained by complex genotype by environment interactions and the difficulty of measuring relevant traits at scale. This study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean, and how predictive ability changes when information is used from single dates or cumulatively across the season. Results Predictive performance was strongly trait dependent and varied with predictor set and temporal strategy. Combined VI + 3D predictors generally produced the highest and most consistent predictive ability for major traits. Total grain yield reached 0.75 under cumulative VI + 3D prediction at 93 days after sowing (DAS 93), cumulative water uptake peaked at 0.80 at DAS 97, and total straw biomass reached 0.66 at DAS 104. In contrast, some component traits were predicted equally well or better by 3D information alone, including grain number with 0.70 and pod number with 0.55 under cumulative 3D prediction. Useful prediction windows also differed among traits, with broad late-season windows for major agronomic traits but narrower, more stage-specific windows for productive tillers, thousand kernel weight, and water-use efficiency. Conclusion Phenomic prediction under drought in faba bean was strongly shaped by trait type, predictor composition, and temporal design. Combined VI + 3D predictors were most effective for integrative traits, whereas several component traits were predicted equally well or better by 3D information alone. These findings highlight the potential of scanner-based multisensor phenotyping to support drought-related selection in faba bean breeding.
Why it matches plant phenotyping methodsスキャナー由来のスペクトル指標と3Dキャノピー形質を用いたマルチセンサー表現型解析・予測が研究の中心であり、乾燥ストレス下の収量、バイオマス、水利用などの植物形質を技術的に評価している。
abstractThis study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・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。
abstractCrop Height was extracted from oblique photogrammetry point cloud data.
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 · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Faba bean ( Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-resilient germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.
Why it matches plant phenotyping methods多センサー表現型プラットフォームを用いた画像由来バイオマスの抽出と生物量との検証が研究の中心であり、表現型取得・検証に該当する。
abstractIn this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions.
This study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops, using Cestrum nocturnum as a model species. A greenhouse experiment was conducted with plants grown in containers and irrigated with nutrient solutions at three electrical conductivity (EC) levels (2.0, 4.5, and 7.0 dS m - ¹). Plant responses were assessed through vegetative growth, visual quality, flowering intensity, continuous stem diameter variation (maximum daily stem shrinkage, MDS), cumulative evapotranspiration (ETa), and substrate bulk EC monitored with sensors. Increasing salinity reduced vegetative growth, particularly shoot biomass, while enhancing flowering intensity at 4.5 dS m - ¹, indicating a shift from vegetative to reproductive development. The moving average of MDS (avgMDS) responded to salinity, showing both increases and decreases depending on stress intensity, and, when expressed as signal intensity (SI: control/salinity), discriminated between stress levels, establishing alert (1.10) and critical (1.38) thresholds. Salinity decreased ETa by 35% and 65% at 4.5 and 7.0 dS m - ¹, respectively, and ETa-based SI defined alert (1.20) and critical (1.55) thresholds. The hourly moving average of bulk EC (avgECb) enabled continuous assessment of salinity dynamics, minimizing the influence of substrate moisture variability. The use of avgMDS, ETa, and avgECb enables the detection and interpretation of salinity stress by integrating plant physiological responses with substrate conditions, while the combined use of two or more indices improves the robustness of the assessment, providing a quantitative framework for salinity management in potted crops.
Why it matches plant phenotyping methods植物の生理応答とセンサー計測から塩ストレスを定量検出する指標を開発し、警戒・臨界閾値を設定して技術的に評価しているため、単なる生育測定ではない。
abstractThis study proposes salinity indices based on plant and substrate measurements to define reference thresholds for salinity management in potted crops
Field / plotMultimodalLeafWhole plant / canopy / plot / fieldClassificationObject detectionStress / disease detectionDisease symptoms / severityWater status / transpiration
Crop losses caused by disease, water stress, and delayed field intervention remain a major challenge for small and medium farmers. Conventional advisory systems often depend on manual inspection or cloud-only diagnosis, which can be slow in rural environments where connectivity is limited. This paper proposes a multimodal edge-intelligence framework that combines leaf-image analysis, soil-moisture sensing, weather context, and lightweight decision rules to provide early crop disease detection and irrigation advisory. The system uses a compact convolutional neural network for visual symptoms and a sensor-fusion module for environmental risk estimation. By running inference near the field, the framework reduces latency and protects farm data while still supporting periodic cloud synchronization. Simulated evaluation shows 91.8% disease classification accuracy, 16.4% water saving, and faster advisory delivery compared with image-only and rule-based baselines.
Why it matches plant phenotyping methods葉画像から作物の病徴・病害状態を推定するCNNとセンサ融合手法の開発・評価が中心であり、植物状態の表現型計測に該当する。灌漑助言部分も含むが、病害検出手法が明示的に評価されている。
titleMultimodal Edge Intelligence for Crop Disease Detection and Irrigation Advisory in Precision Agriculture
Artificial intelligence (AI)-enabled camera sensor systems are increasingly transforming precision agriculture by providing non-destructive, rapid, and scalable methods for monitoring crop health. Two of the most critical applications are the detection of crop water stress and the assessment of pesticide requirement through pest, disease, and symptom recognition. This literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support. The reviewed studies show that thermal and hyperspectral imaging are particularly effective for water stress detection, whereas RGB and multispectral systems are highly practical for identifying disease symptoms, pest infestation, and spray targets. The literature further indicates a shift from simple classification toward real-time decision support, multimodal fusion, explainable AI, and precision input application. This review discusses core sensing technologies, major algorithmic approaches, research findings from key studies, present limitations, and future research directions. Overall, AI camera sensor systems offer substantial potential for reducing water wastage, minimizing excessive pesticide use, and improving sustainable agricultural productivity.
Why it matches plant phenotyping methods作物の水ストレスや病害症状を画像・センサーから推定する手法を中心に整理したレビューであり、植物状態の取得・推定方法が中核です。
abstractThis literature review synthesizes published work on RGB, thermal, multispectral, and hyperspectral imaging integrated with machine learning and deep learning methods for agricultural decision support.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
TomatoGreenhouseStem / branchGrowth / time-series analysisWater status / transpiration
Traditional irrigation management for tomatoes in solar greenhouses relies heavily on empirical manual experience and single soil moisture indicators, often leading to irrigation scheduling that lacks crop-specific physiological evidence and results in suboptimal water-use efficiency. To address these challenges, this study developed an intelligent, plant-centric irrigation decision-making framework for greenhouse tomatoes in the arid region of Xinjiang. Central to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation. By monitoring the high-frequency dynamics of stem diameter (SD) and integrating soil moisture data, the physiological responsiveness of tomatoes to water stress was systematically analyzed. A hybrid predictive model, STL-LSTM, was constructed by coupling Seasonal-Trend decomposition using Loess (STL) with Long Short-Term Memory (LSTM) networks to forecast 24-h SD trends. Furthermore, an innovative dual-threshold irrigation mechanism was established, utilizing a physiological trigger (Maximum Daily Shrinkage, MDS > 70 μm) and a soil moisture constraint (Volumetric Water Content, VWC ≤ 17%). Results demonstrated that tomato SD exhibited distinct diurnal rhythms, with MDS and Daily Increment (DI) identified as highly sensitive indicators of plant water status. The proposed STL-LSTM model achieved superior predictive performance during the peak fruiting stage, with a coefficient of determination (R2) of 0.9184, representing an improvement of 14.8% and 27.56% over standalone LSTM and ARIMA models, respectively. The validation of the dual-threshold mechanism confirms its ability to balance real-time crop water demand with conservation requirements, effectively mitigating the risks of premature or delayed irrigation inherent in traditional methods. This research provides scientific rationale and technical support for the transition of greenhouse agriculture in arid regions towards precision irrigation and optimised water resource management.
Why it matches plant phenotyping methodsトマト茎径を植物の水分状態指標として高頻度センシングし、STL-LSTMによる予測と二重閾値の検証を行うことが中心であり、単なる灌漑実験ではない。
abstractCentral to this framework is the precise identification of irrigation timing—the most critical first step and a fundamental prerequisite for achieving true on-demand irrigation.
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。
abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.
Abstract Premise There is a knowledge gap regarding how foliar injury and restricted water uptake can be detected by measuring root dielectric response. This pot study nondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying. Methods Root dielectric properties were recorded on a minute scale in control and glyphosate‐treated maize, cucumber, and pea. Chlorophyll, stomatal conductance, and biomass measurements were taken to interpret the dielectric changes. Results Electrical capacitance and conductance varied diurnally due to the circadian regulation of water uptake and hydraulic conductance. Glyphosate application reduced capacitance, indicating the impeded root growth and activity caused by impaired amino acid synthesis, foliar damage, and restricted transpiration. The dissipation factor decreased in response to glyphosate due to impeded apoplastic water flow, suppressed root lignification, and hampered water absorption. The enhanced leaf and root hydraulic resistance caused by glyphosate was manifested in sharply reduced electrical conductance. Changes in the species’ dielectric response were consistent with physiological symptoms and biomass loss. Discussion Real‐time dielectric measurement proved suitable for the nondestructive monitoring of plant responses to foliar stress through altered root traits. This method could be employed to evaluate herbicide tolerance in crops and to develop and determine dosage of herbicide ingredients.
Why it matches plant phenotyping methods植物の根の誘電特性をリアルタイム・非破壊で測定し、ストレス応答や根形質を評価する方法が研究の中心であるため。
abstractnondestructively evaluated the efficiency of real‐time dielectric measurement to monitor the effects of glyphosate spraying
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisWater status / transpiration
Summary Plant water potential is a central integrator of plant water status, linking hydraulic function with physiological performance and ecosystem water dynamics across species and systems. This review is motivated by the need to capture these dynamics under rapidly changing environmental conditions, which are often missed by discrete measurements. We evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote‐sensing proxies. We discuss the principles, measurement mechanisms, practical constraints, and environmental sensitivities of each approach. Relative to traditional methods, such as pressure chambers, continuous measurements offer major advantages by resolving rapid variation in water status and strengthening inference on plant–soil–atmosphere interactions. These approaches are especially valuable under dynamic field conditions, where temporal variability in vapor pressure deficit, soil moisture, temperature, and radiation strongly shapes hydraulic behavior. We conclude that continuous monitoring has substantial potential to advance plant and ecosystem science, but wider application will depend on careful interpretation and greater harmonization across comparable methodologies. By synthesizing core principles, methodological challenges and best practices, this review provides a practical framework for researchers and practitioners applying continuous water potential measurements.
Why it matches plant phenotyping methods植物の水ポテンシャルという生理形質を連続測定するセンサー手法を比較・整理し、測定原理、制約、標準化、実践指針を扱う方法論レビューであり、フェノタイピング手法が中心である。
abstractWe evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote‐sensing proxies.
Field / plotWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationBiomass / plant weightGrowth / development / phenologyPlant / canopy heightWater status / transpiration
This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.
Why it matches plant phenotyping methodsGNSS-IRによる作物構造・水分の推定手法が研究の中心であり、植生高やバイオマス密度などの植物形質を地上測定と比較して技術検証している。
abstractThis study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture.
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)
Improving transpiration efficiency (TE) offers a mechanistic pathway to enhance yield under drought by modulating the balance between carbon assimilation and water loss. This study quantified genotypic variation in TE and dissected the physiological processes underlying this variation across a diverse panel of wheat genotypes. Following an initial experiment with six cultivars, 105 genetically diverse lines were evaluated under well-watered conditions across naturally fluctuating vapour pressure deficit (VPD). Transpiration was measured at 10-minute intervals using a high-throughput lysimeter platform and normalised daily at low VPD to minimise confounding effects arising from genotypic differences in canopy size. Variation in TE was strongly associated with reduced normalised transpiration rate at high VPD (TR norm-highVPD ). No relationship was detected with maximum photosynthetic capacity, indicating that TE differences were driven primarily by regulation of water loss rather than carbon gain. High-TE genotypes achieved either greater biomass for a given water use or equivalent biomass with reduced water use, consistent with conservative stomatal regulation under high evaporative demand. A complementary experiment conducted under low VPD revealed limited genotypic variation in intrinsic TE, suggesting that genetic control of TE is predominantly expressed under high atmospheric demand. The consistency of TE–TR norm-highVPD relationships across experiments highlights TR norm-highVPD as a robust and physiologically meaningful phenotyping target. Several high-TE genotypes outperformed modern cultivars, offering novel sources of allelic variation for transpiration regulation. Collectively, these results define a mechanistically grounded, scalable phenotyping framework to target VPD-responsive water-use traits and support breeding strategies aimed at improving drought resilience and water productivity.
Why it matches plant phenotyping methods高スループットライシメータで蒸散を定量し、VPD応答性の水利用形質を検証・評価するスケーラブルな表現型解析枠組みが研究の中心である。
abstractTranspiration was measured at 10-minute intervals using a high-throughput lysimeter platform
Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.
Why it matches plant phenotyping methodsトマトの電気生理シグナルから水ストレス状態を推定するセンシング・機械学習パイプラインを開発し、未学習データで性能検証しているため、植物フェノタイピング手法が中心である。
abstractDirect physiological sensing offers the potential to detect stress responses before visible symptoms appear.
Reproduction assets foundThe paper's electrophysiological time-series and soil moisture measurements from the water-stress tomato experiment are explicitly stated to be publicly available online via a Zenodo deposit (Buss et al. 2026a), referenced in both the Methods and Data availability sections.Dataset · publicAll recorded and
processed data are available online (Buss et al. 2026a).Open asset ↗pdf-page:5 lines:1-37Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
This study addresses the early detection of water stress in grapevines ( Vitis vinifera L. cv. Monastrell), a key challenge for precision irrigation. The main objective is to assess the feasibility of VIS-NIR hyperspectral imaging (400-1000 nm) to anticipate water stress, relating the spectral signal to stem water potential. This study was developed over two campaigns, in 2024 and 2025, using 18 potted plants. In 2024, eight vines were irrigated, and the remaining 10 were subjected to water-deprivation treatments, whilst in 2025, all plants were irrigated, but half at a control dose and the rest at a reduced dose equivalent to 33% of the control. Images were acquired over five dates in June 2024 and over seven in June 2025 using a Specim IQ camera; stem potential was also measured to provide a physiological reference. Individual time series were developed, calculating the Mahalanoubis distance in a PCA space. Results revealed a change window between 10 and 13 June, consistent with the divergence in water potential from 17 to 24 June. PCA highlighted spectral regions related to changes in pigments, nitrogen and water content as main indicators of water stress. We conclude that HSI is a promising tool for early water stress detection.
Why it matches plant phenotyping methodsブドウの水ストレス状態を近接ハイパースペクトル画像から推定・早期検出する方法が研究の中心であり、生理学的基準との比較も行っている。
abstractThe main objective is to assess the feasibility of VIS-NIR hyperspectral imaging (400-1000 nm) to anticipate water stress, relating the spectral signal to stem water potential.
Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R2 reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R2 values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.
Why it matches plant phenotyping methodsRGB画像指標とANNによる植物形質・収量の定量推定が研究の中心であり、予測精度も評価しているため、画像ベース形質推定の方法適用・検証に該当する。
abstractThis study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Plant 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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Roots play a pivotal role for plant performance, but they are difficult to access, which hampers quantitative measurements. Repeated imaging of rhizotrons, flat growth containers with a transparent side, has proven suitable to assess dynamics of root traits in indoor experiments. However, measuring hundreds of soil-grown plants with high temporal resolution remains a laborious challenge. We introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3. This platform was designed to image shoots and roots of individual plants simultaneously and derive digital proxy traits for biomass and growth. In addition, built-in weighing and watering stations deliver water use data for each rhizotron. To achieve the desired throughput (image all 896 plants once a day) a high degree of automatization and standardization was required. We realized a modular plant-to-sensor solution, using a fleet of automated guided vehicles (AGVs) to transport large rhizotrons (80x40x5 cm) to four measurement chambers for daily imaging, weighing, and watering. Simultaneous imaging of the root system with a high-resolution camera (116 μm per px) and the shoot from six different viewing angles allows to monitor plant growth with high spatial and temporal accuracy. First, we verified that moving plants to the measurement chambers did not significantly affect above- or belowground plant growth. Next, we measured phenotypic variation in root and shoot traits of 24 barley genotypes, parents of a nested association mapping population. Our analysis revealed that heritability of root traits such as root system depth and seminal root length was moderate to high (r 2 =0.52 and r 2 =0.93, respectively), enabling further assessment of increasing numbers of recombinant genotypes. The results demonstrate the suitability of GrowScreen-Rhizo 3 to phenotype a range of plant species characterized by various growth habits, including crop, niche, and wild plant species. We conclude that GrowScreen-Rhizo 3 will contribute significantly to the development of phenotyping pipelines for the identification of candidate genotypes with improved resource use efficiency and to pre-breeding processes of climate-resilient crops.
Why it matches plant phenotyping methods根とシュートを自動撮像し、バイオマス・成長などの形質を抽出する大規模フェノタイピング platform の開発・検証が中心である。
abstractWe introduce a novel whole-plant phenotyping platform with a capacity of almost 900 rhizotrons, which we named GrowScreen-Rhizo 3.
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationWater status / transpiration
Abstract. Monitoring forest canopy water is essential for understanding drought response and interception losses under climate change. At short timescales, canopy water is partitioned among internal plant water storage (Sp), rainfall interception (Si), and dew (Sd), which together regulate plant functioning, canopy evaporation, and precipitation partitioning, yet are rarely observed simultaneously. GNSS transmissometry (GNSS-T) has recently emerged as a low-cost, continuous, stand-scale method to observe L-band vegetation optical depth (VOD) from signal attenuation. However, interpreting GNSS-T VOD remains difficult because the signal integrates multiple water pools and is similarly affected by biomass, canopy structure, and measurement noise. Here, we applied GNSS-T in a mature Picea abies stand in Tharandt, Germany, during the 2024 growing season to separate canopy water storage into Si, Sd, and Sp. Rainfall interception was simulated with the multilayer Penman–Rutter model CanWat and used to calibrate the empirical VOD–water-storage relationship and to convert the GNSS-T noise floor into an equivalent storage detectability threshold. GNSS-T VOD tracked interception storage robustly and approximately linearly at both 30-min and event scales (R² = 0.63/0.79), and modeled Si explained VOD variability better than gross precipitation alone. The inferred attenuation coefficient b was physically consistent with the reported L-band values but varied seasonally, indicating that time-varying calibration is preferable to a fixed relationship. Dew-related wetting signals were distinguishable in VOD and yielded plausible mean nightly amounts, but short event duration and high noise caused unrealistic extremes and limited detectability. Diurnal changes in internal plant water storage were not directly detectable at sub-daily scales, indicating that realized variations in Sp remained below the GNSS-T noise floor during the study period which we could show using trait-based estimates of expected maximum plant water loss under non-stressed conditions. This storage-versus-noise framework provides a practical way to benchmark the hydraulic sensitivity of GNSS-T across sites that differ in biomass, hydraulic strategy, and climate, and further highlights noise reduction as a prerequisite for plant-hydraulic applications, especially in low-biomass ecosystems. This study further promotes GNSS-T VOD as a robust monitoring instrument for resolving sub-event interception storage, a potential avenue for constraining hydrological models.
Why it matches plant phenotyping methodsGNSS-T VODによる植物・林冠の水貯蔵量測定を中心に、校正、検出限界、ノイズ、感度を評価しており、植物の水状態を取得するセンシング手法の検証・適用研究である。
abstractGNSS transmissometry (GNSS-T) has recently emerged as a low-cost, continuous, stand-scale method to observe L-band vegetation optical depth (VOD) from signal attenuation.
Accurate and non-destructive prediction of lettuce quality traits is essential for variety identification, germplasm utilization, and intelligent breeding. However, existing approaches relying on handcrafted features or purely data-driven models face limitations under small-sample conditions, including constrained prediction accuracy, weak interpretability, and an increased risk of overfitting. To address these challenges, we propose a knowledge-guided feature tokenizer transformer (KG-FT-Transformer) framework for hyperspectral quality trait prediction and fingerprint analysis. This framework integrates domain prior knowledge with data-driven learning, significantly improving prediction accuracy while enhancing biological interpretability. The KG-FT-Transformer employs a Transformer-based architecture integrating multi-head attention (MHA) with a gated feed-forward network (GFFN), enabling nonlinear spectral modeling and rich feature interactions. We evaluated its performance on three key quality traits: relative chlorophyll content (SPAD), soluble solids content (SSC), and moisture content (MC). The model achieved R 2 values of 0.9534, 0.9185, and 0.9226, with corresponding residual predictive deviation (RPD) values of 4.63, 3.50, and 3.60, outperforming all baseline models and demonstrating stable and consistent prediction performance. Moreover, pixel-wise predictions were used to construct quality trait fingerprints through pseudo-color mapping, intuitively visualizing the spatial distribution and varietal specificity of traits. SPAD and SSC exhibited visually consistent central aggregation patterns, while MC revealed distinct spatial variations among cultivars. These fingerprint-based representations provide spatially informed, qualitative references that may assist the interpretation of DUS-related (Distinctness, Uniformity, Stability) trait characteristics. Overall, this study demonstrates the potential of integrating hyperspectral prediction with quality fingerprinting for non-destructive quality assessment and breeding-oriented analysis, and provides a complementary perspective for germplasm identification and crop improvement.
Why it matches plant phenotyping methods植物の品質形質をハイパースペクトル画像から非破壊推定するTransformer手法を開発・評価しており、形質取得と空間可視化が研究の中心である。
abstractwe propose a knowledge-guided feature tokenizer transformer (KG-FT-Transformer) framework for hyperspectral quality trait prediction and fingerprint analysis.
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
WheatField / plotRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat (Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and differ among cultivars remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance. Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in radial conductance increasing and axial conductance decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20-30%). By integrating field sampling with high-throughput image analysis and modeling, this study establishes an integrated phenotyping approach linking root anatomy to hydraulic function and uncovering anatomical traits relevant for water uptake. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments.
Why it matches plant phenotyping methods根の高スループット画像解析とモデル推定を統合した表現型解析手法が研究の中心であり、解剖学的形質と水理機能を定量化している。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to the GRANAR-MECHA model to estimate radial and axial hydraulic conductance.
LeafStem / branchPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyWater status / transpiration
We report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth. The system integrates two components: a leaf-mounted tattoo sensor for estimating vapor pressure deficit (VPD) and a kirigami-inspired strain sensor for tracking radial stem growth. Uniquely, the tattoo sensor serves a dual function: measuring temperature and humidity beneath the leaf surface while simultaneously harvesting power from ambient moisture via a vanadium pentoxide (V 2 O 5 ) nanosheet membrane. This moist-electric-generator (MEG) configuration enables energy-autonomous operation, delivering a power density of 0.1114 μW/cm 2 . The V 2 O 5 -based sensor exhibits high sensitivity to humidity (4.2 mV/% RH) and temperature (1.02%/°C), enabling accurate VPD estimation for over 10 days until leaf senescence. The eutectogel-based kirigami strain sensor, wrapped around the stem, offers a gauge factor of 1.5 and immunity to unrelated mechanical disturbances, allowing for continuous growth tracking for more than 20 days. Both sensors are fabricated via cleanroom-free, roll-to-roll compatible methods, underscoring their potential for large-scale agricultural deployment to monitor abiotic stress and improve crop management.
Why it matches plant phenotyping methods植物の水分状態（VPD）と茎の成長を連続測定するセンサー基盤を開発・性能評価しており、植物表現型の取得が中心的な技術貢献である。
abstractWe report a scalable, moisture-powered in-planta sensor platform for the continuous monitoring of plant hydration and growth.
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
This study showcases and validates a fully-printed, low-cost microneedles (MNs) device integrated with environmental sensors and NFC wireless readout for real-time monitoring of changes in plant total ionic conductivity. The Aerosol-Jet printed MNs patch enabled minimally invasive impedance measurements for the monitoring of leaf hydration and ion uptake. Inkjet-printed temperature and humidity sensors provided complementary environmental and leaf's microclimate data. Both sensing platforms were integrated in a cost-effective, easy-to-use wooden clip assembly, granting adhesion and reproducible MNs insertion. Dehydration and ions uptake tests demonstrated that the devices can detect ionic variations in different cellular compartments of the leaves, with distinct responses across plant species reflecting their physiological and anatomical differences. The NFC system validation confirmed that wireless, battery-free readout can be used to observe similar impedance trends with respect to the ones observed with conventional potentiostat measurements. Overall, the presented platform establishes a scalable approach toward simple, field-deployable plant monitoring systems, supporting future development of species-tailored and functionally enhanced sensors for precision agriculture.
Why it matches plant phenotyping methods植物の葉の水分状態・イオン吸収を測定する低侵襲センサーとNFC読出しプラットフォームの開発・検証が研究の中心であり、植物状態の表現型を直接取得している。
abstractThis study showcases and validates a fully-printed, low-cost microneedles (MNs) device integrated with environmental sensors and NFC wireless readout for real-time monitoring of changes in plant total ionic conductivity.
Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
Why it matches plant phenotyping methods植物形質をハイパースペクトル画像から推定する深層学習について、OOD条件での不確実性推定手法Dis_UNを開発・評価しており、表現型取得・推定手法が中心である。
abstractwe propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty
Reproduction assets foundThe paper's authors publicly released their uncertainty-analysis code (two GitHub repositories) and the study data (Hugging Face dataset) with explicit availability statements and URLs. The EnMAP and NEON hyperspectral scenes are third-party public data sources, not paper-specific deposits, and the supplement is not anCode · publicThe code for this study is available at: https://github.com/echerif18/Multi_trait_Uncertainty/ (last access: 8 March 2026).Open asset ↗echerif18/Multi_trait_Uncertaintylines:449-456Dataset · publicThe data used in this study are available on Hugging Face: https://doi.org/10.57967/hf/7838 (Cherif et al., 2026).Open asset ↗Hugging Face · 10.57967/hf/7838lines:457-483Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
MaizeRaman / spectroscopySeed / grainPhysiological trait estimationWater status / transpiration
Using spectroscopic technology for the accurate and non-destructive determination of moisture content (MC) in husk-on fresh corn ( Zea maize L. sinensis Kulesh) is crucial for optimizing harvesting periods, ensuring quality, and maintaining nutritional value. However, corn husks interfere with the propagation of incident photons within corn kernels, leading to acquired spectral signals that contain information unrelated to the kernels themselves, thereby decreasing the accuracy of moisture detection in the kernels. This study developed a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn. The developed system mitigates the interference of husks on the acquired spectral signals by collecting spectral information from multiple detection positions offset at specific distances from the incident light source. Meanwhile, three model building strategies based on deep learning frameworks, including feature-level fusion, data-level fusion, and decision-level fusion, were proposed and compared. Results showed that the decision-level fusion model with standard normal variate (SNV) preprocessing achieved the highest prediction accuracy, with a coefficient of determination (R 2 p ) of 0.897 and a root mean square error of prediction (RMSEP) of 4.13%. Furthermore, multichannel data relatively enhanced model performance, with the four-channel combination achieving the best performance. This study demonstrates the potential of deep learning and multichannel spectral data fusion in improving MC prediction accuracy, offering a practical solution for non-destructive moisture measurement in fresh corn.
Why it matches plant phenotyping methodsトウモロコシの水分含量という植物器官の状態を、非破壊分光計測と深層学習で推定する取得・解析手法を開発し、性能比較・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 a multichannel visible and near-infrared (Vis-NIR) spectral acquisition system based on spatially resolved diffuse reflectance technology for MC detection in husk-on fresh corn.
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
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analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
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Validation; Visualization; Writing – original draft; Writing – review and editing.
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D t v il ility Part of the datasets generated and analyzed during the current study are
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publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
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be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
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Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
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
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water management. We reviewed over 200 research articles and discussed the tools, techniques, and challenges associated with spatial and temporal phenotyping for assessing crop water stress, highlighting recent advances and emerging technologies. Emerging technologies such as artificial intelligence (AI) and Internet of Things systems are transforming crop water‐stress phenotyping by enabling real‐time monitoring and robust predictive models. Despite these advancements, challenges persist, including data gaps, platform limitations, and the need for scalable integration frameworks. The review examines key physiological and spectral indicators of crop water stress across multiple spatial and temporal scales using ground‐based sensors, unmanned aerial vehicles, and satellites. It further discusses multiscale phenotyping approaches and data fusion techniques to improve spatial resolution and prediction accuracy. Challenges in harmonizing spatial and temporal data are discussed, along with the need for interdisciplinary collaboration among the phenotyping, modeling, and agronomy domains. The review concludes by identifying future directions, including edge computing, high‐resolution imaging, and robust spatiotemporal phenotyping frameworks to enhance crop water‐stress assessment. By leveraging remote sensing, modeling, and AI, future phenotyping systems can improve water‐stress assessment, advance precision agriculture, and ensure resilience in water‐limited agroecosystems.
Why it matches plant phenotyping methods植物の水ストレス表現型評価に用いる空間・時間スケール、センサー、UAV、衛星、データ融合などの手法を中心に扱うレビューであり、対象範囲に明確に該当する。
abstractThis review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Accurate plant health monitoring relies on hyperspectral imagery to extract vegetation spectral signatures and compute vegetation indices (VIs), which are critical for phenotyping and crop condition assessment. However, the requirement for high spectral resolution significantly increases the cost and complexity of data acquisition. In this study, we proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data. The aim was to reduce the dependency on high-resolution spectral imagery without compromising prediction accuracy. The framework integrated correlation-based feature selection with four regression models to identify and utilize the most informative spectral bands from coarsely sampled data. The system was trained and validated using a data set consisting of 555 spectral signatures collected from olive leaves at five stages of dehydration, with spectral resolutions ranging from 1 to 100 nm. A total of 25 vegetation indices, commonly used in the estimation of water stress, chlorophyll, and nitrogen, were predicted on various sampling scales. Experimental results show that even with 100 nm spectral resolution, the proposed framework achieves high prediction accuracy, with coefficients of determination reaching 0.99 for RVSI, VOPT, and SPADI indices. These findings demonstrate that accurate vegetation index estimation is achievable with significantly fewer spectral bands, offering a cost-effective solution for large-scale plant health monitoring. This framework lays the groundwork for the development of low-cost, data-efficient remote sensing systems for precision agriculture, especially in crops such as olives, where health dynamics are sensitive to water and nutrient status.
Why it matches plant phenotyping methodsオリーブ葉のハイパースペクトルデータから植物状態に関わる植生指数を推定する、低コストな機械学習・スペクトル測定フレームワークの開発と検証が中心である。
abstractwe proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data
Reproduction assets foundThe paper's Data Availability statement points to a Figshare deposit (DOI 10.6084/m9.figshare.26950660.v2), which per the statement hosts the study's data — the 555 olive-leaf hyperspectral signatures and vegetation index measurements underlying the phenotyping analysis. This is a paper-specific, publicly accessible,直接Dataset · publicnm.
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Inclusivity in global research questionnaire.
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Acknowledgments
The authors thank the Advanced Center of Electric and Electronic Engineering - AC3E ANID. The authors acknowledge the support provided by Universidad Técnica Federico Santa María and the Direction of Post-Grade programs DDP.
Data Availability
https://doi.org/10.6084/m9.figshare.26950660.v2 .
Funding Statement
This work was funded by the ANID FB240002 basal center AC3E, and ANID national doctorate scholarship, folio N°21231129. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Ruiz-Carrasco B, Fernández-Lobato L, López-Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2lines:266-293Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldPhysiological trait estimationWater status / transpiration
The impact of climate change on vegetation dynamics and wildfire risk has been a subject of considerable research interest. Live fuel moisture content (LFMC) is a critical factor in assessing fire risk and influencing fire ignition and behaviour. Satellite remote sensing techniques provide information on LMFC dynamics, but spatial and temporal resolution hinder understanding in structurally complex forests with interconnected tree and shrub layers. Multi-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC. However, studies of LFMC in shrubs are scarce despite their key role in fire spread. In this study, we investigated the capacity of a dual-wavelength SALCA (Salford Advanced Laser Canopy Analyser) TLS (1063 and 1545 nm) and the single-wavelength Trimble X6 (1500 nm) to estimate LFMC in six Mediterranean forest plots (135 individual plants, 18 species). Analysis at different separate plots and individual-species levels identified key factors affecting LFMC prediction using TLS. At plot level, linking spectral indices and LFMC is challenging due to species diversity in crown structures, ages, sizes and leaf types. Our results suggest that detector heating by solar radiation could alter the sensor calibration and reduce model accuracy. Nevertheless, in some areas the multiple linear regression models achieved an R adj 2 up to 0.82 and an RMSE of 7.66%. At the species level, models showed stronger relationships with LFMC ( R adj 2 ranging from 0.43 to 0.88) and a relatively low RMSE (RMSE from 1.92 to 3.97%). Overall, univariate relationships between LFMC and individual-wavelength reflectance were not consistent across species or most plots. Considering these results, combining the capacity of dual TLS devices to estimate LFMC with the structural information that they provide, open a potential to improve field work sampling for wildfire risk assessment. Extending the research to cover a wider range of tree, shrub and herbaceous species in the future will advance our understanding of LFMC dynamics and contribute to more accurate fire behaviour modelling.
Why it matches plant phenotyping methodsTLSセンサーを用いて個体・種・プロットレベルの生植物含水率（LFMC）を推定し、モデル精度やセンサー校正の影響を評価しているため、植物状態の取得・推定方法が中心です。
abstractMulti-wavelength terrestrial laser scanning (TLS) sensors can measure the structural and spectral properties of forests and have demonstrated their potential for monitoring LFMC.
Smart irrigation management is essential for improving crop resilience under increasing drought frequency driven by climate change. Although satellite-based remote sensing provides valuable tools for monitoring crop water status at large spatial scales, its accuracy is often limited in mountainous and heterogeneous agricultural landscapes. In this study, we investigated drought-induced metabolic responses in potato ( Solanum tuberosum L.) to identify biochemical biomarkers that could complement satellite-based irrigation advisories in the mid-Himalayan region of India. A field experiment was conducted using a gradient of soil moisture regimes corresponding to moderate (50% field capacity), critical (25% field capacity), and extreme drought stress (5-8% field capacity). Satellite-derived evapotranspiration-based irrigation advisories were validated against in situ soil moisture measurements, revealing discrepancies attributed to the inability of satellite estimates to capture actual water loss under drought stress conditions, highlighting the need for additional ground-truth biomarkers across heterogeneous field conditions. To capture plant-level physiological responses, untargeted metabolite profiling of potato leaves was performed using gas chromatography–mass spectrometry (GC-MS). Approximately fifty metabolites belonging to amino acids, organic acids, sugars, and sugar alcohols were detected. Multivariate statistical analyses revealed distinct metabolic signatures associated with progressive drought stress. Notably, accumulation of proline, serine, isoleucine, sucrose, fructose, glucose, and polyols such as mannitol and myo-inositol reflected key metabolic reprogramming associated with osmoprotection, redox homeostasis, and energy metabolism under drought conditions. Collectively, this ensemble of stress-responsive metabolites represents a robust panel of drought stress biomarkers. As a proof of concept, proline was validated as a qualitative biomarker of plant water status through a rapid and cost-effective colorimetric biochemical assay, demonstrating its practical applicability for field-level irrigation management. These findings demonstrate that metabolomics-derived biomarkers can provide sensitive plant-level indicators of drought stress that complement satellite-based monitoring systems. The integration of biochemical diagnostics with remote sensing platforms offers a promising approach for improving drought detection and developing low-cost, field-deployable tools for smart irrigation advisories in heterogeneous agricultural landscapes. Graphical abstract
Why it matches plant phenotyping methods植物の水分状態・乾燥ストレスを示す代謝バイオマーカーを開発・検証し、迅速な比色 assay として実用化可能性を評価しており、単なる乾燥処理実験の routine 測定を超えて表現型取得法が中心的です。
abstractAs a proof of concept, proline was validated as a qualitative biomarker of plant water status through a rapid and cost-effective colorimetric biochemical assay, demonstrating its practical applicability for field-level irrigation management.
Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha−1. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions.
Why it matches plant phenotyping methodsハイパースペクトル反射と複数センサーを統合し、作物の水分状態・キャノピー構造・老化を推定して収量との関係を評価することが中心であり、植物表現型の実質的な方法適用に該当する。
abstractThis study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield
Abstract Soil salinization constrains agricultural productivity across approximately 950 million hectares worldwide. In the Yellow River irrigation district of Ningxia, secondary salinization severely depresses maize yields. Existing crop–water–salt models lack day-by-day bidirectional coupling between salt transport and crop growth, over-simplify salt stress representation, and amplify stress through multiplicative integration. To address these gaps, we developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function. Salt stress is transmitted via three physiological pathways, and the combined stress factor is computed using Liebig’s law of the minimum. The model was calibrated with 43 sampling points spanning low-to-high salinity gradients (1.49–6.79 g kg⁻¹) in Huinong District during 2024, and independently validated with 30 points (1.29–7.75 g kg⁻¹) in 2025. Calibration yielded R² = 0.883, RMSE = 0.744 t ha⁻¹, NRMSE = 10.96%, and NSE = 0.795; validation gave R² = 0.824, RMSE = 0.753 t ha⁻¹, NRMSE = 11.13%, and NSE = 0.810, confirming strong inter-annual parameter stability. Under high salinity (> 4 g kg⁻¹), simulated mean yield declined to 3.75 t ha⁻¹, a 53% reduction compared with low-salinity conditions. Compared with the standard WOFOST model (R² = 0.450, RMSE = 1.399 t ha⁻¹), the coupled model substantially improved accuracy.These results show that the coupled model can improve yield prediction under salinity stress and provide a useful tool for irrigation scheduling and water–salt management in salinized farmland.
Why it matches plant phenotyping methodsWOFOST–HYDRUS-1Dの結合モデルを開発し、トウモロコシの生育・塩ストレス・収量を推定する手法として独立検証しており、植物状態の推定手法が中心である。
abstractwe developed a fully coupled WOFOST–HYDRUS-1D model linking the Richards equation and convection–dispersion equation with crop photosynthesis, transpiration, and assimilate partitioning through a modified Maas–Hoffman function.
Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana®) with two planting densities (625 and 1250 trees ha−1) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling.
Why it matches plant phenotyping methodsUAVのマルチスペクトル・熱画像からキャノピー形状、被覆率、NDWI、CWSIを抽出し、作物係数との関係を較正・比較している。植物の水分状態やキャノピー特性の測定・推定が研究の中心であり、単なる灌漑試験の routine measurement ではない。
abstractMultispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI
Field / plotRaman / spectroscopyTissuePhysiological trait estimationWater status / transpiration
Common hydrogeological methods make use of natural gases as tracers to better understand the spatial and temporal evolution of groundwater flow, to constrain water residence time, and to reconstruct environmental conditions at recharge [1-3]. Noble gases can be used as complement of the stable water isotope tracers for understanding complex hydrological systems [4,5,6].We adapted these methods to in-situ measurements of gases in tree xylem sap to better understand the plant-mediated water and gas flux between the hydrosphere, the biosphere, and the atmosphere. Using a “miniRuedi” portable mass-spectrometer [7] and tailored semi-permeable membrane probes, the partial pressures of He, Ar, Kr, N2, O2, CO2, and CH4 were continuously monitored in-situ in the soil, the tree, and the atmosphere. Diurnal variations of CO2 and O2 were observed that reflected the tree physiological activities [8]. Since transpiration by plants is a major component of the hydrological cycle, such measurement techniques offer new opportunities to better understand plant water and CO₂ dynamics, within the soil-plant-atmosphere continuum.[1] Kipfer et al. (2002), Reviews in Mineralogy and Geochemistry, 47, 615–700; [2] Brennwald et al. (2013), Advances in Isotope Geochemistry – The Noble Gases as Geochemical Tracers, 123-153; [3] Brennwald et al. (2022), Frontiers in Water, 4, 107-115; [4] Althaus et al. (2009), Journal of Hydrology, 370, 64-72. [5] Schilling et al. (2019), Reviews of Geophysics, 57, 146-182. [6] Xu et al. (2017). Hydrogeology Journal, 25(7), 2015–2029; [7] Brennwald et al. (2016), ES&T, 50, 13455-1346; [8] Marion et al. (2024), Tree Physiology, tpae062.
Why it matches plant phenotyping methods植物の木部樹液中ガスを測定する携帯型質量分析計と膜プローブを適応し、植物生理活動や水・CO₂動態を連続的に取得する測定法が研究の中心である。
abstractWe adapted these methods to in-situ measurements of gases in tree xylem sap to better understand the plant-mediated water and gas flux between the hydrosphere, the biosphere, and the atmosphere.
WheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementStress / disease detectionGrowth / time-series analysisRoot system architectureWater status / transpiration
Soil structure creates spatial heterogeneity that shapes ecosystem functions, including water retention and root colonization. Chernozems – soils characterized by exceptionally stable aggregation resulting from millennia of root-soil co-evolution – offer a unique model to investigate how aggregate-scale pore architecture controls plant responses to drought. Using soil microcosms (4 × 10 cm, ~80 g soil) with aggregates from Native Steppe and Arable Chernozems, we established six experimental treatments (3 aggregate sizes × 2 soil types) with three replicates each. Root-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution. Imaging was synchronized with plant developmental stages – germination, first leaf, and third leaf stage at permanent wilting point – yielding a total of 54 soil tomograms for analysis.Preliminary processing of the data reveals distinct pore network architectures across aggregate size classes. Small aggregates exhibited low CT-visible porosity (24%) with high solid phase connectivity (6.60 mm⁻³), while medium aggregates showed moderate porosity (39%) with lower connectivity (0.64 mm⁻³), and large aggregates had the highest porosity (49%) but the lowest connectivity (0.51 mm⁻³). This structural gradient directly controlled root colonization: solid phase connectivity showed a strong negative correlation with root volume growth (r = −0.76), suggesting that matrix mechanical cohesion, rather than pore characteristics alone, limits root expansion. Medium aggregates – which naturally dominate in undisturbed steppe soils – provided optimal conditions for root development, with 90% greater root surface expansion compared to small aggregates. Root sphericity decreased 3–4 times more in medium aggregates (−0.14) than in small aggregates (−0.04), indicating greater architectural plasticity critical for water acquisition. Importantly, our preliminary results also show that medium aggregates provided the greatest drought resistance: plants in these microcosms reached the permanent wilting point latest, suggesting that this aggregate fraction optimizes both root development and water availability over time.These findings demonstrate that native Chernozem aggregate structure represents an optimized spatial configuration balancing root accessibility with water retention. The strong coupling between aggregate-scale heterogeneity and root response suggests that tillage-induced disruption of natural aggregate distributions may compromise this evolutionary optimization. Our approach – combining high-resolution CT with growth stage-synchronized imaging – offers a framework for quantifying how spatial heterogeneity translates into ecosystem-relevant soil functions. Data processing is ongoing, and final results will include expanded replication and additional root morphometric parameters.
Why it matches plant phenotyping methods高解像度X線CTを用いて根の体積成長、表面拡大、球形度などの形態形質を反復取得・定量する手法が研究の中心であり、植物フェノタイピングへの実質的応用に該当する。
abstractRoot-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution.
Field / plotStem / branchStomata / guard-cell complexPhysiological trait estimationGrowth / time-series analysisStomatal traitsStress response / toleranceWater status / transpiration
Droughts have emerged as the primary driver of forest disturbances across Europe in the 21st century, significantly impacting both tree growth dynamics and mortality rates. Tree species are differently affected under drought, and these differences are related to species-specific plant hydraulic traits that govern water storage, hydraulic conductivity, and stomatal regulation. However, quantifying variability in these hydraulic traits across sites, species, and time remains challenging, as site measurements have historically rarely been comprehensive enough to assess the evolution of plant hydraulic behavior under drought stress. New continuous, high temporal resolution observational plant hydraulic data paired with process-based plant hydraulic modelling opens an opportunity to address this gap, by providing a framework to test and quantify theories based on first principles across species and sites.In this study, we apply the terrestrial biosphere model QUINCY, augmented by a recently developed plant hydraulic architecture module, across three eddy covariance sites in Germany covering broadleaved forest species (Aplern, Hainich, and Hartheim). The model is parameterized for three common temperate tree species present at the aforementioned sites. We constrain QUINCY across these species and sites using 30-minute resolution stem water potential measurements collected during the summer and autumn of 2023. Our results show that two groups of model parameters explain most of the simulated plant water potentials: parameters controlling plant water uptake from soil (plant ability to extract water from soil and the root distribution), and parameters regulating stomatal sensitivity to pre-dawn leaf water potential. Across species, we find ash to be more drought resistant than beech and hornbeam, as it closes its stomata earlier than other species under similar levels of drought stress, and it is characterised by a higher hydraulic capacitance per unit stem volume. Our study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters, effectively reducing uncertainty in, and providing robust constraints on, modelled responses to drought.
Why it matches plant phenotyping methods植物の水理状態・水理形質を連続観測と拡張モデルで定量化する手法の適用が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractOur study demonstrates how integrating the new generation of in situ plant hydraulic observations into vegetation models can facilitate the quantification of species-specific hydraulic parameters
Climate change is intensifying soil moisture variability, atmospheric evaporative demand, and salinity intrusion in agricultural landscapes, creating new challenges for sustainable food production. Understanding how soil hydrology and plant physiological stress interact under these conditions is essential for designing resilient irrigation strategies. This study presents a hydro-physiological assessment of wheat and maize grown under controlled combinations of soil salinity and deficit irrigation, and introduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support in semi-arid farming systems of northern India.Field experiments (2023–2025) were conducted to measure canopy temperature, air temperature, relative humidity, vapor pressure deficit (VPD), and soil moisture under varying salinity (EC levels) and irrigation regimes. These data were used to develop whole-season and stage-specific ANN models capable of capturing non-linear interactions between soil hydrology, crop physiology, and atmospheric demand. The ANN-based CWSI successfully distinguished mild-to-severe stress transitions and detected early-stage water stress acceleration during periods of high VPD, indicating a propensity toward flash drought development under combined salinity–moisture constraints.Results show that salinity amplifies crop water stress by reducing effective root-zone moisture availability, leading to higher canopy–air temperature gradients and elevated CWSI values even under moderate irrigation. Stage-specific ANN models achieved strong performance (R² = 0.87–0.94), particularly during flowering and grain filling, where hydrological stress most affects yield. The framework demonstrates how data-driven CWSI modeling can translate complex soil–plant–atmosphere interactions into actionable irrigation insights for farmers.This work highlights a scalable approach to precision irrigation scheduling, enabling reduced water use without compromising crop health in regions vulnerable to hydrological extremes and sociohydrological pressures. By linking soil hydrology, irrigation management, and physiologically informed stress indicators, the study contributes to sustainable food production strategies in a global climate change context.
Why it matches plant phenotyping methodsANNによる作物水ストレス指標（CWSI）の開発と性能評価が中心で、キャノピー温度などから植物の生理的ストレス状態を推定している。
abstractintroduces an Artificial Neural Network (ANN) based Crop Water Stress Index (CWSI) model for real-time decision support
Field / plotRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architectureWater status / transpiration
Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labour-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access tubes and customized ingrowth core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 m, over 5 years. The less-invasive studies using ingrowth cores reached depths of 4.2 m. Nutrient tracer 15 N analysis showed marked differences in deep root activity among crop species. Time domain reflectometry sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analysing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根研究施設とAIによる根形質解析パイプラインの構築・有効性評価が中心的に記述されており、根密度などの植物形質を取得するフェノタイピング基盤に該当する。
abstractThe facility includes 144 6-metre-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationWater status / transpiration
Understanding crop water status and soil moisture dynamics in heterogeneous agricultural landscapes remains a major challenge for microwave remote sensing, especially when multiple crop types coexist within a single pixel. With the NISAR mission providing fully polarimetric L- and S-band SAR observations, there is a unique opportunity to evaluate its retrieval capability in complex mixed-cropping systems.In this study, we conduct an intensive field campaign across irrigated and rainfed plots in southern India to assess how NISAR L- and S-band backscatter responds to variations in vegetation water content (VWC) and surface soil moisture (SSM) under heterogeneous conditions. Each satellite-aligned pixel in the study region typically contains 4-5 crop types with distinct canopy structures and rooting characteristics. For selected NISAR acquisition dates, we measure VWC through destructive sampling of each crop species present within the pixel. Concurrently, surface soil moisture is measured using both handheld probes and permanently installed soil moisture sensors deployed across the heterogeneous fields to capture intra-pixel variability.By combining in-situ VWC, multi-depth soil moisture observations, and crop-wise metadata with co-located NISAR L- and S-band backscatter, we evaluate (i) the sensitivity of each band to mixed vegetation conditions, (ii) the ability to distinguish irrigated vs. rainfed water-use patterns, and (iii) the impact of intra-pixel crop diversity on retrieval accuracy. This work provides one of the first ground-based evaluations of NISAR performance in complex Indian agroecosystems and contributes toward developing improved retrieval approaches for crop water assessment and soil moisture estimation in heterogeneous landscapes.
Why it matches plant phenotyping methodsNISAR SARによる作物の植生水分量（VWC）および水分状態の推定性能を、現地測定と比較検証する研究であり、植物状態の取得・推定手法が中心的です。
abstractwe conduct an intensive field campaign across irrigated and rainfed plots in southern India to assess how NISAR L- and S-band backscatter responds to variations in vegetation water content (VWC) and surface soil moisture (SSM) under heterogeneous conditions.
Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ETlys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R2 = 0.85; RMSE = 0.26 mm d−1; MBE = 0.01 mm d−1) and moderate agreement for UAV–TSEB (R2 = 0.66; RMSE = 0.81 mm d−1; MBE = 1.01 mm d−1). Model intercomparison further showed consistent temporal dynamics of ET (R2 = 0.70; RMSE = 1.35 mm d−1) and robust partitioning of crop transpiration (R2 = 0.79; RMSE = 0.99 mm d−1) and soil evaporation (R2 = 0.76; RMSE = 1.03 mm d−1) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems.
Why it matches plant phenotyping methodsUAV熱・マルチスペクトル画像とTSEB/AquaCropによる作物キャノピーの蒸発散・蒸散・蒸発推定を、ライシメータで独立検証・比較しており、植物の生理状態計測手法が中心である。
abstractThis study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season.
Field / plotRaman / spectroscopyLeafGrowth / time-series analysisGrowth / development / phenologyPigment / colour / senescenceWater status / transpiration
Mitigation of the ongoing biodiversity crisis requires thorough understanding of species dynamics across scales. However, monitoring plant species and their functional traits is time-consuming and challenging to implement across larger spatial scales and through time. Spectroscopy is emerging as a promising tool for monitoring plant functional and taxonomic diversity within and between ecosystems. This relies on the presence of a functional and taxonomic signal in leaf optical properties, which in turn depends on the spectral similarity of species, intra- and interspecific trait variation, and the timing of the measurements. In order to address these relations in natural and semi-natural ecosystems in Denmark, we are compiling a spectral library of plants. An important methodological aspect of this work is to assess how leaf degradation, and the phenological stage of the plant, affect leaf optical properties. This was assessed by measuring leaf spectra from 350 – 2500 nm of four plant species, representing different plant functional types, from the same site over five months. We measured leaves at the time of sampling, and repeatedly after detachment from the plant to test how sampling strategy, potential leaf degradation after detachment and phenological stage influence leaf optical properties. Furthermore, we collected spectral and functional trait data of dominant plant species in 100 vegetation plots across the Store Åmose nature area in July and August. We present results of the spectral and functional differences among species and taxonomic levels, and the significance of leaf degradation and phenology on measured spectra. Leaf water and chorophyll content are expected to be the major drivers of spectral variation over time. However, subtle spectral signals may reflect other biochemical traits or leaf biophysical changes during the growing season. These dynamics are expected to depend on plant ecology, functional types, and environmental conditions such as wet and dry habitats. Our results will demonstrate the potential (and challenges) of using spectroscopy for taxonomic and functional identification of plants. We will provide insights into the role of leaf sampling strategy and phenology on spectral signals of plant species, which can inform the planning of future remote sensing and field campaigns.
Why it matches plant phenotyping methods葉の分光計測を用いた植物機能形質・分類情報の取得を中心に、葉の劣化、採取方法、フェノロジーが光学特性へ与える影響を評価しており、表現型取得手法の方法論的検討が実質的に含まれる。
abstractSpectroscopy is emerging as a promising tool for monitoring plant functional and taxonomic diversity within and between ecosystems.
Field / plotMultispectral / hyperspectralWhole plant / canopy / plot / fieldClassificationPhysiological trait estimationBiomass / plant weightWater status / transpiration
Burullus Lagoon, situated in the Nile Delta of Egypt, is a Ramsar-listed wetland of high ecological importance, particularly in relation to its floristic diversity. This study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring. Hyperspectral reflectance measurements were obtained for 41 plant species selected from a total of 63 floristically identified taxa, based on ecological dominance, spatial recurrence across sampling sites (≥3 stands), and suitability for reliable field spectral acquisition. Spectroscopic data were collected from 44 stands representing lagoon shores, islets, and open-water habitats using an ASD FieldSpec spectroradiometer covering the 350–2500 nm spectral range. A set of vegetation indices was applied to evaluate key biophysical and biochemical properties associated with plant vitality, water status, and biomass. The results indicate that the red and near-infrared regions provide the highest discriminatory capability among species, whereas the shortwave infrared region exhibits more limited discriminatory capability. Dominant taxa, including Phragmites australis and Atriplex halimus , displayed elevated near-infrared reflectance, consistent with differences in canopy structure and biochemical composition. Most species showed vegetation index responses broadly indicative of healthy physiological conditions, although interspecific variability suggests contrasting stress responses among taxa. Overall, the study demonstrates the applicability of field-based hyperspectral data for species-level discrimination in wetland environments and delivers a curated spectral library to support biodiversity conservation and long-term ecosystem management at Burullus Lagoon.
Why it matches plant phenotyping methods野外ハイパースペクトル計測とスペクトルライブラリ構築が研究の中心で、植物の活力、水分状態、バイオマスなどの状態推定に用いているため、単なる生態調査ではなく植物表現型計測への実質的応用に該当する。
abstractThis study presents a field-based hyperspectral characterization of wetland vegetation with the objective of establishing a reference spectral library to support biodiversity assessment and environmental monitoring.
Empirical and physical models are widely used for monitoring equivalent water thickness (EWT) to adjust plant moisture management. However, model transferability to different times and locations, and insufficient training data remain the two key challenges of field spectroscopy analysis. Therefore, this study aims to construct a hybrid model, which combines the physical models optimized by Wasserstein Generative Adversarial Nets (WGAN) and empirical models for performing hyperparameter searches (the process of finding optimal model settings) to monitor the peanut EWT. Specifically, we develop a large spectral dataset consisting of field-measured data which including 246 peanut varieties in five peanut farms across China and synthetic datasets generated from the physical models optimized by WGAN. Furthermore, the PWLEH was constructed by hyperparameter tuning and pre-training which using synthetic datasets, and then fine-tuned by modular training with field data of peanut canopy water content. Comparing the model constructed with field data (R² = 0.5618, mean squared error (MSE) = 0.0725) and PROSAIL (a widely used canopy radiative transfer model) (R² = 0.7105, MSE = 0.0473), PWLEH achieved high accuracy in predicting peanut water content (R² = 0.7650, MSE = 0.0519). Unlike pure data-driven approaches, the new hybrid model incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. This study demonstrates the potential of applying an optimized PROSAIL, hyperparameter search and modular training to improve the accuracy and transferability of the EWT prediction model, providing a new approach for sustainable agricultural management.
Why it matches plant phenotyping methods落花生のキャノピー分光データから等価含水厚（EWT）を推定するハイブリッドモデルを開発・評価しており、植物水分形質の取得・推定手法が中心である。
abstractTherefore, this study aims to construct a hybrid model, which combines the physical models optimized by Wasserstein Generative Adversarial Nets (WGAN) and empirical models for performing hyperparameter searches
TomatoStem / branchPhysiological trait estimationWater status / transpiration
Accurate sap flow measurements in small-diameter plant organs are essential for understanding water transport and source-sink dynamics, yet existing methods are limited by their temporal resolution, reduced sensitivity to low or reverse flow, and incompatibility with small organ dimensions. In this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs. Its performance was validated on tomato truss peduncles (Solanum lycopersicum L.). Zero-flow corrections accounting for ambient temperature and peduncle diameter ensured robust baseline adjustment, while gravimetric calibrations revealed a strong linear relationship between the sensor-measured temperature difference and sap flow rate up to 2 g h⁻¹, corresponding to a sap flux density of 5.8 10⁻³ cm³ cm⁻² s⁻¹. Whole-plant validation further demonstrated close agreement between ExoHeat-derived sap flow and gravimetric transpiration data. Anatomical imaging showed an asymmetrical distribution of xylem vessels in the tomato truss peduncle, underscoring the importance of correct sensor orientation. High-resolution measurements on ripening trusses successfully captured dynamic bidirectional flow patterns. The ExoHeat sensor thus provides a novel, high-temporal-resolution tool for accurate monitoring of sap flow in small-diameter organs, with promising applications in plant physiology, irrigation optimisation and stress detection.
Why it matches plant phenotyping methods小径植物器官の双方向樹液流を測定するセンサーを開発し、重力法による校正・検証を行った、植物生理状態の取得手法が中心の研究。
abstractIn this study, the ExoHeat sensor, a continuous-heating solution was developed for bidirectional sap flow measurements in small-diameter plant organs.
MaizeRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Crop adaptation to the mixture of environments that defines the target population of environments is the result of balanced resource allocation between roots, shoots, and reproductive organs. Root growth plays a critical role in the determination of this delicate balance. The responses of root growth and function to temperature can determine the strength of roots as sinks but also influence a crop's ability to uptake water and nutrients. Surprisingly, this behavior has not been studied in maize (Zea mays) since the middle of the last century, and the genetic determinants are unknown. Low temperatures recorded frequently in deep soil layers limit root growth and soil exploration and may constitute a bottleneck for increasing drought tolerance, nitrogen recovery, sequestration of carbon, and productivity in maize. We developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm. Here, we show that there is (i) genetic variation in root growth under low temperature below a previously set threshold of 10 °C and (ii) genotypic variation in water transport under low temperature. The trait set examined herein and the high-throughput phenotyping platform developed for its characterization provide a unique opportunity for removing a major bottleneck for crop improvement and adaptation to climate change.
Why it matches plant phenotyping methods根の成長と水輸送という植物形質を評価するためのハイスループット表現型解析システムの開発が中心的に記述されており、遺伝的変異の評価にも用いられているため。
abstractWe developed high-throughput phenotyping systems to investigate these responses and to examine genetic variability therein across diverse maize germplasm.
Reliable and intelligent retrieval of leaf traits from hyperspectral reflectance is crucial for assessing ecosystem functions, yet conventional approaches struggle with spectral complexity and nonlinearities. To address these challenges, we developed the Leaf Trait Retrieval Network (LTRN), a novel deep learning framework that integrates Kolmogorov–Arnold Network (KAN), Transformer, and Temporal Convolutional Networks (TCN) for end-to-end trait estimation. Model validation was carried out using a large spectral–trait database covering hundreds of plant species and four functional traits. Experimental results demonstrated that LTRN model outperforms state-of-the-art deep learning models, achieving R² values greater than 0.78 for estimating chlorophyll content (Chlₐ₊b), equivalent water thickness (EWT), carotenoid content (Ccₐᵣ), and leaf mass per area (LMA). Further analyses indicated that the LTRN model delivers stable estimation performance across spectral resolutions of 10–25 nm. Moreover, the model demonstrates strong stability across varying proportions of training samples. These findings underscore the robustness and stability of LTRN for large-scale vegetation trait retrieval, offering a valuable framework for advancing the intelligent estimation of other ecological parameters.
Why it matches plant phenotyping methods植物のハイパースペクトル反射から葉形質を推定する深層学習手法を開発し、複数形質・種を含むデータベースで性能と頑健性を検証しており、表現型取得・抽出法が中心である。
abstractwe developed the Leaf Trait Retrieval Network (LTRN), a novel deep learning framework that integrates Kolmogorov–Arnold Network (KAN), Transformer, and Temporal Convolutional Networks (TCN) for end-to-end trait estimation.
Timely, field-scale retrieval of crop biophysical variables is widely regarded as central to data-driven agronomy. In this study, a practical workflow was evaluated in which high-resolution unmanned aerial system (UAS) multispectral imagery was coupled with PROSAIL inversion to map rice canopy traits across three phenological stages. Multispectral and RGB acquisitions were processed, and indices sensitive to chlorophyll, water, and pigment dynamics (e.g., Normalized Difference Red-Edge Index (NDRE), Leaf Chlorophyll Index (LCI), Modified Chlorophyll Absorption Ratio Index (MCARI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Structure-Insensitive Pigment Index 2 (SIPI2), Triangular Greenness Index (TGI), and Visible Atmospherically Resistant Index (VARI)) were derived. Leaf and canopy parameters, leaf chlorophyll content (Cab), carotenoids (Car), leaf water content (Cw), dry matter (Cm), mesophyll structure (N), and leaf area index (LAI)—were retrieved via lookup-table (LUT) inversion of PROSAIL. Independent ground measurements were used for validation, and a same-date Sentinel-2 benchmark was performed (subject to cloud constraints). Consistent phenological trajectories were observed: NDRE/LCI and Cab/LAI were found to peak at maximum greenness, while SIPI2 was observed to rise during senescence alongside declining Cab and Cw. Stage-dependent errors were identified in PROSAIL RMSE maps, with the lowest and most homogeneous errors detected at peak canopy. Strong agreement with field data was obtained (R² > 0.98 for most variables at the first date). For Cab, R²/RMSE values of 0.996/1.555, 0.978/2.104, and 0.972/0.2 were recorded across the three dates, respectively. Lower accuracy was produced by Sentinel-2 at field scale (e.g., LAI R²/RMSE ≈ 0.81/0.7; Cab ≈ 0.78/6.5), although useful cross-sensor complementarity was indicated. An operational pathway to within-field mapping of rice biophysics is thereby offered by the “UAS multispectral + PROSAIL” pipeline. The results demonstrate high accuracy at field scale, with phenology-dependent retrievals outperforming Sentinel-2-based estimates, highlighting the potential of UAV-based approaches for precise crop monitoring. Enhanced robustness to phenological change and cloud-related gaps is achieved when red-edge and pigment-ratio indices are fused with physical inversion, and straightforward extensibility to other cereals and management contexts is suggested.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL逆解析を統合し、イネの生理・構造形質を推定して地上測定で検証するワークフローが研究の中心であり、実質的な植物フェノタイピング手法の適用・評価である。
abstracta practical workflow was evaluated in which high-resolution unmanned aerial system (UAS) multispectral imagery was coupled with PROSAIL inversion to map rice canopy traits across three phenological stages.
Water stress significantly threatens sugarcane production, particularly among smallholder farmers in South Africa, where spatially explicit assessments remain limited. This study aimed to improve the quantification of crop water stress by developing a machine learning (ML) model to predict the Normalised Difference Water Index (NDWI), a proxy for vegetation water content. An ML approach was adopted to capture complex, non-linear relationships between structural vegetation indices (SVIs) and NDWI. Sentinel-2 satellite data and UAV-acquired multispectral imagery were integrated, with the model trained using satellite-derived SVIs and NDWI, and then applied to UAV-derived SVIs to predict NDWI. The model achieved high predictive accuracy (R² = 0.95, RMSE = 0.03, MAE = 0.02) and effectively captured temporal variations in sugarcane water status, including post-rainfall stress recovery and increased water retention during early maturation—aligning with changes in leaf area index (LAI), chlorophyll content (CC), and Total Soil Water Profile (TSWP). NDWI also showed a positive correlation with actual evapotranspiration (ET a ; R² = 0.60) and a negative correlation with the Water Deficit Index (WDI; R² = 0.62), suggesting its potential to reflect crop water status under certain conditions. When interpreted in conjunction with in situ measurements of precipitation, TSWP, and WDI, the predicted NDWI provides valuable insights into crop water dynamics. This approach demonstrates the potential of ML-driven NDWI estimation to support site-specific irrigation scheduling, enhance resource use efficiency, and promote sustainable sugarcane cultivation. The findings contribute to climate-resilient water management practices tailored to the needs of smallholder systems in water-scarce regions. • Machine learning predicts multispectral UAV-derived NDWI using Sentinel-2 vegetation indices. • Predicted NDWI aligns with trends in soil water status, evapotranspiration and water deficit dynamics. • NDWI correlates positively with ET a and negatively with WDI, reflecting crop water stress levels. • These relationships capture shifts in crop water dynamics under varying environmental and meteorological conditions. • Model outputs can inform timely, site-specific irrigation strategies in rainfed sugarcane production systems.
Why it matches plant phenotyping methodsUAV・衛星マルチスペクトル画像からNDWIを推定し、作物の水ストレス状態を定量化する機械学習手法の開発と精度評価が中心であるため、植物フェノタイピング手法として含める。
abstractThis study aimed to improve the quantification of crop water stress by developing a machine learning (ML) model to predict the Normalised Difference Water Index (NDWI), a proxy for vegetation water content.
Efficient irrigation of horticultural crops under increasing water scarcity requires crop models that exploit high–resolution remote–sensing (RS) data. This study evaluated how unmanned aerial vehicle (UAV) multispectral and thermal observations improved AquaCrop-OSPy simulations of canopy cover (CC), actual evapotranspiration (ETₐ) and yield for irrigated broccoli under Mediterranean conditions. Broccoli was grown for two seasons in a 0.2 ha field in eastern Spain under two irrigation strategies: decision–support Irrigation Advisor (IA) versus farmer practice. A global sensitivity analysis (GSA) and two–stage calibration against Season 1 CC and yield identified canopy growth (CGC), harvest index (HIₒ) and transpiration phenology (GDDᵤₚ) as dominant controls; the calibrated model was validated in Season 2. UAV imagery provided CC via supervised classification and ETₐ via a two–source energy balance model (pyTSEB), which were assimilated into AquaCrop-OSPy on three dates using a hybrid observed–simulated scheme. Compared with lysimeter measurements, pyTSEB reproduced ETₐ with root–mean–square error (RMSE) 0.39 mm d⁻¹ and Nash–Sutcliffe efficiency (NSE) 0.93, whereas baseline AquaCrop-OSPy showed RMSE 1.24 mm d⁻¹ and NSE 0.27. Without assimilation, AquaCrop-OSPy reproduced mean yield but not subplot variability (RMSE 1.67 t ha⁻¹). Assimilating CC reduced yield RMSE by 8.9 %, ETₐ alone gave smaller gains, and joint CC + ETₐ assimilation achieved the lowest RMSE (1.47 t ha⁻¹, 11.9 % reduction). Across seasons, IA applied 20.6 % more water than farmer practice with no consistent yield or water–productivity benefits. These results, obtained within the limitations of this study, indicate that UAV-derived CC, complemented by ETₐ, modestly improves AquaCrop-OSPy yield predictions. Nevertheless, they should be interpreted as indicative rather than definitive and motivate further evaluations.
Why it matches plant phenotyping methodsUAV画像からブロッコリーの canopy cover と実蒸発散量を推定し、作物モデルへの同化性能を検証することが研究の中心であり、植物形質・状態の取得と技術評価に該当する。
abstractUAV imagery provided CC via supervised classification and ETₐ via a two–source energy balance model (pyTSEB), which were assimilated into AquaCrop-OSPy on three dates using a hybrid observed–simulated scheme.
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.
Sugarcane farming demands precise irrigation and vigilant health monitoring to maximize productivity, yet conventional approaches often fall short in efficiency and scalability. This paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time. Data is processed locally and relayed to a cloud server, enabling automated irrigation decisions informed by the Crop Water Stress Index (CWSI) and growth tracking through advanced image analysis. The system achieved a soil moisture measurement accuracy with a strong correlation (R² = 0.96) to gravimetric methods and a plant height measurement accuracy with a mean absolute error of 1.8 cm. Designed for energy independence, the system operates seamlessly in off-grid environments. Field results demonstrate key findings: 98.7% data transmission reliability, early stress detection 24-48 hours before visible symptoms, 15% water savings through precision irrigation, and continuous operation for 180+ days on battery backup. These outcomes position this solution as a practical advancement for modern, sustainable sugarcane cultivation.
Why it matches plant phenotyping methods植物の健康状態・温度・草丈をセンサーと画像解析で取得し、精度検証まで行うIoTフェノタイピング基盤が研究の中心であるため。
abstractThis paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time.
Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.
Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。
abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits
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 analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 .Open asset ↗NPEC‐NL/greenhouse_m5lines:68-83Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Laboratory / benchtopTissuePhysiological trait estimationWater status / transpiration
Measurement of xylem hydraulic conductance provides access to xylem hydraulic conductivity and vulnerability to cavitation, two key traits for assessing plant sensitivity to environmental stressors. We evaluated the performance of custom-built low-cost pressure drop flow meters through nearly 1200 measurements across devices, laboratories, reservoir heights (10, 25 and 45 cm, used to induce pressure head and drive water flux) and PEEK tubing of hydraulic contrasting resistances. Flow meters were interchangeable, with mean differences generally < 3.5% and never exceeding 5%, with 88.9% of comparative tests showing no significant difference. Under recommended conditions (25-45 cm pressure head, downstream-to-upstream pressure ratio ≈0.5), precision reached 1%-7% coefficient of variation. Accuracy, assessed against reference values obtained by water displacement, was also strong, with 68% of measurements deviating by < 5% from reference values and over 78% when measured at {greater than or equal to}25 cm. At 10 cm, performance declined because sensor deviations represented a larger fraction of pressure differential, and low-resistance PEEK tubing increased absolute but not relative error. Validated flow meters proved portable, affordable (≈2500 CAD), and reliable. Their low cost, open-source interface, and publicly available construction protocol make them accessible to laboratories with limited resources, enabling reproducible multi-laboratory studies of plant hydraulics and fostering international collaborations.
Why it matches plant phenotyping methods植物の木部油圧コンダクタンスという生理形質を測定する低コスト流量計を開発・検証し、精度・再現性・多施設間性能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。
abstractWe evaluated the performance of custom-built low-cost pressure drop flow meters through nearly 1200 measurements across devices, laboratories, reservoir heights (10, 25 and 45 cm, used to induce pressure head and drive water flux) and PEEK tubing of hydraulic contrasting resistances.
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementGrowth / development / phenologyWater status / transpiration
Root hairs are outgrowths of the epidermal cells of plant roots. They increase the root's exchange surface with the soil and provide it with good anchorage in the soil. Root hairs are an emblematic model of apical growth, a process also used by yeasts and hyphae to invade their environment. From a mechanical perspective, the root hair is considered as an elastic cylinder under pressure, closed by a dome that behaves like a yield fluid. We introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana. In the first setup, root hairs grow against an elastic obstacle until buckling. By measuring the critical buckling force, we determine the surface modulus and estimate the Young's modulus of the cell wall, which aligns with previous measurements. Using a 1D elasto-viscoplastic model of root hair growth, we assess the excess pressure beyond the yield threshold (the driver of tip growth) and estimate the axial stiffness of the root hair, reflecting its elastic resistance to compression. For the second protocol, we designed a setup where a single root hair grows against a cantilever with variable stiffness, a technique adapted from our earlier work on rigidity sensing by animal cells. This method provides an independent estimate of the root hair's axial stiffness, confirming our initial findings and suggesting that this stiffness primarily involves tip compression and depends mainly on turgor pressure, at least within the low deformation regime explored.
Why it matches plant phenotyping methods単一の生長中根毛の力学特性を測定する革新的な実験系とプロトコルを開発・相互検証しており、植物表現型の取得法が研究の中心である。
abstractWe introduce here two innovative mechanical setups and protocols to characterize the mechanical properties of single growing root hairs in Arabidopsis thaliana.
MaizeLeafClassificationStomatal traitsWater status / transpiration
Biospeckle imaging enables non-destructive observation of dynamic physiological activity in plant tissues; however, the relative sensitivity of different biospeckle activity maps to water stress and their implications for data-driven classification remain insufficiently understood. This study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance. Maize plants were subjected to three irrigation levels (0%, 50%, and 100%) over a 7-day experimental period. Stomatal conductance was measured as an independent physiological reference, and a microfluidic phantom experiment was conducted to verify the physical response behavior of the biospeckle imaging system. Temporal variations in biospeckle activity were statistically analyzed, followed by deep learning–based classification using representative two-dimensional convolutional neural network models. Statistical analysis revealed that biospeckle activity exhibited stress-dependent responses, with severe water stress (0%) being consistently distinguishable, whereas moderate and well-watered conditions (50% and 100%) showed partially overlapping patterns. These trends were consistent with stomatal conductance measurements. Deep learning models trained on different biospeckle activity maps achieved classification accuracies of up to 0.73 and macro-averaged F1 scores of 0.73, with notable differences in performance depending on the selected activity representation. These results suggest that while traditional statistical parameters show limited linearity, the proposed deep learning-based biospeckle analysis could serve as a useful tool for water stress classification. By capturing complex spatial-texture features, this study presents a potential data-driven approach for precision plant phenotyping.
Why it matches plant phenotyping methods植物の水ストレス状態を推定するバイオスペックル画像マッピングと深層学習分類を系統的に評価し、独立した生理指標およびファントム実験で検証しているため、フェノタイピング手法が中心です。
abstractThis study systematically evaluates multiple biospeckle activity mapping approaches for water stress analysis in maize (Zea mays L.) leaves and examines how their characteristics influence deep learning–based classification performance.
Faba bean ( Vicia faba L.) is a key crop for sustainable agriculture in temperate cropping systems due to its nitrogen-fixing ability and high protein content, but its productivity is increasingly threatened by drought stress driven by climate change. Precise phenotyping under semicontrolled conditions is crucial for understanding drought responses. High-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars. In this study, faba bean genotypes were grown in a precision phenotyping facility comprising 120-L containers filled with mineral soil to simulate field-like growth conditions. Each container was placed on a high-precision gravimetric scale to record water use in real time in relation to 3-D spectral image information. Precise measurement of genotype-specific transpiration behavior using gravimetric methods enabled detailed insights into the transpiration patterns of different genotypes in response to ambient temperature and humidity fluctuations throughout the day and night, and across the whole-life cycle. The results showed that total water use, water-use efficiency, and consequently yield were particularly influenced by specific transpiration parameters, such as the maximum transpiration rate and the vapor pressure deficit threshold at which stomatal conductance was declined. The results revealed genetically determined variation for transpiration responses to drought stress. Genotypes that reduced water loss earlier tended to achieve higher grain yields and use water more efficiently. The findings show that precise automated phenotyping can identify previously undiscovered genetic variation for breeding drought-tolerant faba bean varieties, which are crucial for ensuring productivity under increasingly water-limited conditions.
Why it matches plant phenotyping methods高精度重量計と3-Dスペクトル画像を統合した自動表現型解析施設で、遺伝子型別の蒸散・水利用形質を測定・解析しており、表現型取得手法が研究の中心です。
abstractHigh-throughput precision phenotyping enables efficient evaluation of many genotypes, revealing detailed water-use patterns as a basis for breeding productive, drought-resilient cultivars.
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.
BarleyMultispectral / hyperspectralSeed / grainPhysiological trait estimationWater status / transpiration
This study evaluated the effects of penetration depth, grain orientation, and placement angle on hyperspectral imaging (HSI) signal quality using short-wave infrared (SWIR) HSI (1000–2500 nm). Orientation-related spectral variability was observed, primarily due to groove direction and angular placement. Penetration assessment with lead sulfide (PbS) quantum dots (QDs) at 1200 nm and 1800 nm revealed that barley husks exhibited higher transmittance, while intact kernels showed limited light penetration. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) analyses, two key moisture sensitive wavelength regions (1153 nm and 1954 nm) were identified. Deep learning models, including 2D and 3D convolutional neural network (CNN), were developed and evaluated under several configurations. The 3D CNN showed the best performance when trained solely on HSI data with selected feature wavelengths. Using 540 barley kernels for training and 132 kernels for testing, the model achieved an R² of 0.98, an RMSE of 1.1763, and an MAE of 1.2592, demonstrating that spectral–spatial information alone can provide stable and accurate moisture prediction, and that further inclusion of geometric metadata (angle and orientation) provides limited benefit. The proposed XGBoost–SHAP–3D CNN framework offers an interpretable, efficient, and cost-effective solution for rapid moisture estimation in barley, demonstrating strong potential for intelligent grain quality monitoring.
Why it matches plant phenotyping methods大麦個粒の水分という植物器官の状態を、HSIとXGBoost–SHAP–3D CNNで推定する手法を開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractDeep learning models, including 2D and 3D convolutional neural network (CNN), were developed and evaluated under several configurations.
Thermal imaging is becoming a valuable tool for monitoring plant canopy temperature, which can serve as an indicator of crop water stress. However, specialized thermal sensors are often cost-prohibitive. This study explored strategies for supplementing crop water stress monitoring by generating synthetic thermal images from standard Red-Green-Blue (RGB) imagery captured using an unmanned aerial vehicle system (UAVs) equipped with a Zenmuse XT2 sensor and leveraging deep learning models. UAV-based RGB and thermal images were collected from 32 experimental plots of sweet corn and green beans over three growing seasons from 2020 to 2023. Each crop was subjected to one full and three deficit irrigation treatments, replicated four times. A total of 3,400 UAV images were collected over three seasons. Image processing was done in Pix4D software, and orthomosaic RGB and thermal maps were spatially aligned using ground control points (GCPs). The UAV RGB and thermal map data were split into 80 % and 20 % for training and testing, respectively. Two image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs. Image quality evaluation metrics, i.e., correlation coefficients (r), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), were used to evaluate the models’ performance. Crop water stress index (CWSI) values were also computed from measured and generated thermal imageries to assess practical applicability. Generated thermal canopy temperature outputs from the Pix2PixGAN model showed a strong correlation with the measured data using a thermal camera (r >0.95). Moreover, Pix2PixGAN resulted in lower MSE (5.63) and higher PSNR (42.98) than CycleGAN (MSE = 7.09, PSNR = 40.56), whereas CycleGAN had a slightly higher SSIM (0.44) than Pix2PixGAN (0.31). CWSI values derived from the generated thermal images reflected the expected gradients of water stress across irrigation treatments, with the highest CSWI observed from deficit irrigation treatments compared to the full irrigation. These results demonstrate that RGB-to-synthetic-thermal image translation using GAN models could be used to support crop water stress assessment and irrigation scheduling.
Why it matches plant phenotyping methodsRGB画像から合成熱画像を生成し、作物キャノピー温度と水ストレス指標を推定するGAN手法の開発・比較・性能評価が中心であり、植物状態の表現型取得に直接関係する。
abstractTwo image-to-image translation generative adversarial network (GAN) deep learning models, specifically Pix2PixGAN and CycleGAN, were used to generate synthetic thermal images from RGB inputs.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
LettuceTissuePhysiological trait estimationWater status / transpiration
With the rapid development of smart agriculture, the agricultural Internet of Things (Ag-IoT) has gradually established a monitoring system centered on distributed sensing, low-power communication, and intelligent control. However, current solutions underexplore the perceptible characteristics of communication signals, and therefore do not fully utilize their latent potential in environmental perception. This paper targets the demand for crop water monitoring and introduces an integrated sensing and communication (ISAC) approach. This method can achieve non-contact and continuous perception of crop water status by reusing the communication link without altering the existing hardware architecture and frequency band configuration. Taking leafy vegetables such as lettuce as the research object, a prototype system based on a 3 GHz communication link was built. A quantitative mapping model between the water content of plant tissues and the amplitude and phase disturbances they cause to electromagnetic waves was established. A joint optimization mechanism that considers both communication performance and sensing accuracy was proposed to achieve a coordinated configuration between communication quality (BER < 10⁻⁴, SNR ≈ 20 dB, EVM < 8 %) and sensing accuracy (MAE = 2.51 %, R² = 0.92). Experiments were conducted in controlled environments and production-like scenarios, demonstrating that the method can stably identify the water status of lettuce while ensuring communication quality of service (QoS). The proposed ISAC method is potentially compatible with existing Ag-IoT frequency bands and physical-layer infrastructures, assuming access to pilot/CSI and airtime control. Protocol-level integration with LoRa, Wi-Fi, and NB-IoT is defined as future work It provides a low-cost, high-integration, and easily scalable communication-driven solution for water monitoring in smart agriculture.
Why it matches plant phenotyping methodsレタスの水分状態という植物生理形質を、通信信号を再利用した非接触センシングで推定する方法を開発し、プロトタイプと精度検証を行っており、フェノタイピング手法が中心である。
abstractThis method can achieve non-contact and continuous perception of crop water status by reusing the communication link
WheatField / plotRGB / grayscaleThermalPanicle / ear / spikeSeed / grainPhysiological trait estimationSegmentationGrowth / development / phenologyWater status / transpiration
Grain filling plays a vital role in determining both the yield and quality of wheat. Therefore, timely and accurate monitoring of the grain filling course (GFC) is essential for assessing the feasibility of harvest timing optimization. Traditional methods based on field sampling are time-consuming and destructive. This study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery. Wheat ears were first segmented using a temperature-threshold approach, after which colour and temperature features were extracted. Grain water content (GWC) was then estimated using a Normalised Relative Ear Temperature (NRET) index, while days after anthesis (DAA) were retrieved using a piecewise linear model derived from ear colour features. Finally, a grain filling index (Kf) was developed using DAA corresponding to 25 % moisture content (DAA25%) to quantify the GFC. Results showed that thermal images acquired at 17:00 showed the greatest separability between ears and background canopy and the highest sensitivity to irrigation differences. Both NRET and DAA based models provided accurate GWC estimates (R² = 0.86 and 0.91; RMSE = 3.13 % and 4.21 %; rRMSE = 0.07 and 0.09, respectively). The Kf index effectively captured differences in GFC under different irrigation treatments and detected early maturity under water stress (p < 0.05). This study demonstrates the potential of combining thermal and RGB imagery for high-resolution, non-destructive monitoring of wheat grain filling and for supporting timely harvest management.
Why it matches plant phenotyping methodsRGB画像と熱赤外画像を統合し、穂の分割・特徴抽出から穀粒水分含量と登熟進行を推定する非破壊フェノタイピング手法が研究の中心である。
abstractThis study presents a non-destructive method for estimating the wheat grain filling course (GFC) by integrating ground-based RGB and thermal infrared imagery.
Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.
Why it matches plant phenotyping methods画像時系列とdense optical flowから植物のモルフォ・キネマティック形質を抽出し、水分状態を分類する手法の開発・検証が研究の中心であるため。
abstractwe present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants
CottonAerial / UAVLiDAR / point cloudMultispectral / hyperspectralThermalLeafPhysiological trait estimation2D/3D reconstructionImage / point-cloud registrationWater status / transpiration
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を統合し、綿花キャノピーの水分形質を3D推定・可視化する手法の開発と検証が中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
In this work, we present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring. The humidity sensor was fabricated using graphene/PEDOT:PSS hybrid ink deposited via drop-casting method on interdigitated electrodes (IDEs) screen printed on a eco-friendly paper substrate. Contact angle measurement, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) studies were performed to demonstrate hydrophilic nature, surface morphology and elemental analysis, respectively, of the sensing layer. The sensor exhibited excellent sensing performance in the measured relative humidity (%RH) range from 25% RH to 94% RH having a maximum % response of 226.5%. The sensor demonstrated a nearly linear response (adj. R² = 0.99) in the considered range with a slope observed as 3.21%/%RH. Multi-cyclic repeatability and reproducibility analysis further confirmed high reliability and consistent performance of the developed sensor. Furthermore, the capability of the sensor was successfully evaluated through capturing variations in plant physiological health status (under different environmental conditions, such as un-watered, water availability and solar irradiation) via monitoring microclimatic relative humidity (%RH) variations on plant (Epiremnun aureum) leaves. Through establishing the %RH values for healthy crops or plants under normal (well-watered) and stress conditions (un-watered or excessive solar irradiations), sensor seems to demonstrate strong potential for smart agriculture i.e., detecting early plant leaf stress.
Why it matches plant phenotyping methods植物葉のストレス状態を相対湿度センサーで取得するセンサー開発と性能評価が中心であり、植物生理状態の早期モニタリングへ実証適用している。
abstractwe present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring
Powdery mildew is a major disease affecting rubber tree yield. Rapid and accurate monitoring of this disease is crucial for plantation management. Previous studies have focused on spectral and spatial data for monitoring powdery mildew but have not adequately addressed the underlying physiological and biochemical alterations induced by the disease. Therefore, this study proposes a method combining spatial-spectral features and plant traits to monitor rubber tree powdery mildew. Unmanned Aerial Vehicles (UAVs) equipped with the ULTRIS X20P hyperspectral sensor (350–1000 nm) were used to capture hyperspectral imagery in two rubber plantations. Plant traits (PTs), including chlorophyll (Cab), carotenoids (Car), anthocyanins (Anth), leaf water content (Cw), and dry matter content (Cm), were inverted from UAV hyperspectral imagery using a radiative transfer model. Meanwhile, spectral and spatial information in the imagery were analyzed to extract vegetation indices (VIs), texture features (TFs), and color features (CFs) sensitive to the disease. Machine learning algorithms, including Partial Least Squares Regression (PLSR), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Regression (RF), were subsequently employed to create disease monitoring models based on these features. The results show that models based on VIs and TFs effectively monitor powdery mildew, with the inclusion of PTs significantly improving model performance. Models that integrate multiple features outperform those that depend on single features, especially the monitoring model integrating VIs, TFs, and PTs using the PLSR algorithm, which achieved an R² of 0.794 and an RMSE of 7.991. This study highlights the novelty of integrating spatial-spectral features with plant traits for monitoring rubber tree powdery mildew, offering a reference for precise disease monitoring through the use of UAV hyperspectral imagery.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から植物形質を推定し、空間・スペクトル特徴と統合して病害症状を監視する手法が研究の中心であるため。
abstractTherefore, this study proposes a method combining spatial-spectral features and plant traits to monitor rubber tree powdery mildew.
Improving nitrogen use efficiency (NUE) in commercial maize production remains a persistent challenge. A major barrier is the lack of simple, remote sensing–based decision-support frameworks that enable broad adoption of in-season, site-specific nitrogen (N) management. This study developed and evaluated a practical framework that contextualizes the Holland–Schepers sensor algorithm using PlanetScope (PS) satellite imagery to guide multiple in-season, variable-rate fertigation delivered through a flow-proportional injection system integrated with a center pivot system equipped with variable-rate irrigation. Field implementation was carried out during the 2023 and 2024 seasons across four N rates (0-N, Low-N, Fertigation, Full-N) and three irrigation treatments: full (BMP), deficit (50 %BMP), and rainfed. Normalized Difference Red Edge (NDRE)-derived sufficiency index (SI) values informed the amount, timing, and spatial distribution of N applications. In 2024, PS-guided fertigation achieved yields statistically comparable to Full-N while reducing total N input by 23 %. Significant improvements in NUE were observed, with Fertigation outperforming Full-N by 12 % in agronomic efficiency (AE) and 26 % in partial factor productivity of N (PFPN). Satellite-derived SI values were strongly correlated with UAV benchmarks from the MicaSense Altum and RedEdge-3 sensors (ρ = 0.82–0.95). However, PS consistently overestimated NDRE relative to UAV data, particularly under N-deficient conditions, underscoring the need for local calibration and bias correction. To improve diagnostic specificity, a biologically informed, rule-based stress-classification framework was developed to differentiate nitrogen stress from water stress using NDRE and soil water depletion (SWD) as diagnostic variables. Retrospective yield and management data were used to establish physiologically meaningful NDRE–SWD thresholds for stress diagnosis during the critical in-season fertigation window (V10–R2). Full-Yield plots achieved approximately 12,000 kg/ha at NDRE = 0.78 and SWD = 64 mm. The resulting NDRE–SWD–yield patterns highlight the feasibility of disentangling stress types under commercial field conditions. However, further validation across seasons and environments, along with integration of canopy water or temperature indices, is needed to improve water-stress detection and enable real-time decision-making. Overall, these results offer actionable guidance for implementing satellite-guided fertigation at commercial scale. The developed framework delivers scalable, data-driven N recommendations that enhance profitability and support environmentally responsible maize production. It also provides a reference for future research and extension programs aiming to turn satellite remote sensing into practical tools for site-specific N management.
Why it matches plant phenotyping methods衛星・UAVリモートセンシングによるNDRE指標を用いて植物の窒素・水ストレスを診断し、センサー間の検証とルールベース分類法の開発を行っているため、植物状態の取得・推定手法が中心的である。
abstractThis study developed and evaluated a practical framework that contextualizes the Holland–Schepers sensor algorithm using PlanetScope (PS) satellite imagery to guide multiple in-season, variable-rate fertigation
The study of annual growth rings in Vitis vinifera has recently emerged as a promising framework to explore long-term vine responses to climate and management. This review synthesizes current knowledge on grapevine wood anatomy, xylem functionality, and isotopic signatures, highlighting their role as archives of environmental and agronomic information. Grapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices. Stable isotope analyses further complement anatomical chronologies by integrating physiological responses to water availability. Together, these approaches offer valuable insights into the structural memory and plasticity of grapevine xylem, with direct implications for vineyard sustainability and climate adaptation. We also examine the impact of viticultural practices such as irrigation, pruning, grafting, and rootstock choice on xylem architecture, emphasizing how agronomic decisions leave long-lasting anatomical imprints in wood. Finally, we outline future research perspectives, including the integration of dendrochronological, isotopic, and high-resolution imaging techniques, to fully exploit grapevine chronologies as tools for understanding vine resilience and for guiding varietal selection, breeding, and precision viticulture under changing environmental conditions.
Why it matches plant phenotyping methodsブドウの年輪解剖、安定同位体、画像解析を用いて環境応答や木部機能などの植物形質・状態を評価する方法群をレビューしており、測定・解析手法が中心的である。
abstractGrapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
MRI / PETX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Strategic optimisation of Root System Architecture (RSA) represents a critical frontier for stabilising crop productivity amid increasingly unpredictable moisture-deficit regimes. Understanding key root traits underlying effective drought response is necessary to harness the genetic diversity associated with root growth patterns and environmental adaptations. Many functionally significant root architectural traits have been reported, and the mechanistic importance of some of the anatomical ideotypes, such as the increased metaxylem vessel diameter to reduce axial hydraulic resistance to maintain leaf water potential and change in root growth angle to promote geotropic deep-soil moisture foraging, are discussed in this review. Despite the identification of these characteristics, the knowledge gap in their integration into predictive breeding frameworks remains. This review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities. Dynamic root-soil interfaces can be spatio-temporally quantified in situ using non-destructive technologies such as X-ray computed tomography and MRI, which can detect developmental plasticity masked by destructive sampling. Artificial Intelligence (AI), especially Convolutional Neural Networks, enables automated extraction of high-dimensional topological parameters from complex digital rhizograms. Present review integrates recent advances in phenotyping with molecular regulatory mechanisms, bridging two traditionally disparate fields. By focusing on the DRO1/qSOR1 loci and ABA-auxin crosstalk, we establish critical connections between molecular regulation and field-scale architectural performance. The resulting multi-scale roadmap may help in targeted selection of climate-resilient cultivars to maximize resource use efficiency.
Why it matches plant phenotyping methods根系構造の非破壊・ハイスループット表現型解析技術を中心に、X線CT、MRI、AIによる根系形質抽出をレビューしており、植物フェノタイピング手法が中核です。
abstractThis review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities.
Crop production is often affected by the co-occurrence of heat and drought, which significantly impacts physiology, growth, development, and yield. Leaf wilting is a phenotype that can provide important insights into how plants respond to combined stressors, linking visible morphological changes to internal physiological changes and water transport. This review emphasizes the importance of leaf wilting as a visual indicator of stress under water-limited conditions. It discusses (i) the water transport processes within plants, (ii) canopy responses to heat and drought stress such as paraheliotropism, leaf rolling, and leaf wilting, (iii) wilting dynamics in plants, in response to heat and drought stress, and (iv) highlights the importance, opportunities, and challenges of emerging technologies, i.e., the potential of unmanned aerial vehicles and machine learning, to enable efficient and large-scale high-throughput phenotyping of heat and drought stress-induced leaf wilting in crop production. A deeper understanding of leaf wilting mechanisms under water-limited conditions is essential for optimizing the use of advanced technologies for crop stress monitoring, thereby improving crop resilience and global food security.
Why it matches plant phenotyping methods葉の萎れという植物形質を対象に、UAVや機械学習による大規模ハイスループット表現型計測の可能性と課題をレビューしており、フェノタイピング手法が中心的に扱われている。
abstractThis review emphasizes the importance of leaf wilting as a visual indicator of stress under water-limited conditions.
Evapotranspiration (ET) is a key component of the hydrological cycle and is critical for determining crop water requirements. Accurate ET estimation is essential for improving irrigation efficiency, particularly under increasing water scarcity and climate variability. Conventional approaches such as the soil water balance, empirical formulations, the FAO Penman-Monteith method, eddy covariance flux towers, lysimeters, and scintillometers each have limitations related to spatial representativeness, accuracy, or operational cost. Unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors offer a high spatial resolution and cost-effective alternative for field-scale assessment of surface energy balance components and ET. In this study, a field experiment was conducted on maize during rabi season of 2022-23 under two irrigation regimes based on depletion of available soil moisture (20% DASM and 40% DASM). UAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes. Spatiotemporal analysis showed that the 20% DASM treatment (400 mm) resulted in a 1.7 °C lower land surface temperature, a 16.5% higher NDVI, and an 11% increase in daily ETa compared with the 40% DASM treatment (316 mm), which experienced water stress and a 20% reduction in seasonal ETa. The UAV-based METRIC estimates of daily ETa showed strong agreement with that of Penman-Monteith (PM) combination approach (R² = 0.84; RMSE = 0.22 mm day⁻¹; MAPE = 6.1%), with a slight underestimation of seasonal ETa (-7%). Agreement with the soil water balance method ranged from - 3% to + 3%, demonstrating the capability of the approach to capture irrigation-induced variability in ETa and surface energy fluxes. Overall, the results highlight the potential of UAV-based METRIC for spatiotemporal assessment of crop evapotranspiration and surface energy dynamics to support precision irrigation management.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とMETRICモデルにより、トウモロコシの蒸発散量・表面エネルギーフラックスを取得し、複数手法との一致性を検証している。植物キャノピーの生理状態の定量が研究の中心であり、単なる灌漑試験のルーチン測定ではない。
abstractUAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes.
Abstract Field experiments are complex to interpret due to interactions between genotypes, environment, plant development and cultivation practices. This complexity challenges the accurate phenotyping of individual plant traits over the season. Here, we quantified the primary sources of seasonal variation in stomatal conductance (gs) across 15 grapevine cultivar–rootstock combinations within a large-scale phenotyping platform, comprising over 6000 observations. Environment-related traits and date of measurement accounted for up to 76% of the variance, potentially obscuring cultivar–rootstock effects. Therefore, we integrated machine learning, spatiotemporal normalization of the gs response, and the use of mixed models to disentangle the influences of environmental factors, plant material and crop performance related traits. After spatio-temporal normalization, cultivar and cultivar–rootstock interactions explained over 25% of the variation in gs, and Grenache exhibited the most conservative water-use behavior resulting in high water-use efficiency. Specific rootstock–scion combinations also exhibited smaller, but still significant, differences in gs and water-use efficiency, highlighting the specificity arising from the interaction within each rootstock–scion combination. The high variability in gs indicates that accurate quantification of rootstock–scion contributions to key traits in field studies is complex and requires accounting for spatial heterogeneity driven by the environment.
Why it matches plant phenotyping methods大規模な圃場フェノタイピングで測定した気孔コンダクタンスを対象に、機械学習、時空間正規化、混合モデルを統合して環境変動と遺伝的要因を分離する手法が中心である。
abstractwe integrated machine learning, spatiotemporal normalization of the gs response, and the use of mixed models to disentangle the influences of environmental factors, plant material and crop performance related traits.
Field / plotWhole plant / canopy / plot / fieldSegmentationGrowth / development / phenologyStress response / toleranceWater status / transpiration
Abstract Water scarcity is a major constraint to expanding green spaces in arid and semi-arid regions. In urban landscapes, irrigation for ground cover plants represents one of the largest portions of outdoor water use; therefore, improving its efficiency is essential for balancing water demand and reducing water stress at both city and basin scales. However, lawns and green areas provide important aesthetic, social, and psychological benefits to residents, creating a challenge between conserving water resources and maintaining urban greenery. To address this challenge, a field experiment was conducted to evaluate alternative ground cover species, including Phyla , Frankenia , Oxalis , and Bermudagrass, and to assess the role of vermicompost in improving plant performance under different irrigation regimes. Treatments included three irrigation levels (100%, 75%, and 50% field capacity) and two soil conditions (with and without vermicompost). To ensure objective plant evolution while reducing labor-intensive field work and enabling whole-plant scale monitoring, Fractional Vegetation Cover (FVC) was estimated using classical image processing and advanced deep learning models. Deep learning achieved high segmentation accuracy (~ 91%) and showed strong robustness to environmental variability, outperforming traditional methods. Results showed that vermicompost significantly enhanced growth performance and relative water content under both full and deficit irrigation. Oxalis and Frankenia were more sensitive to water stress, while Bermudagrass and Phyla demonstrated greater drought tolerance. Overall, the findings suggest that drought-resilient ground covers, supported by AI-based monitoring tools, can reduce outdoor water consumption, improve resilience to water stress, and provide a sustainable alternative to high-water-demand plants in urban landscapes.
Why it matches plant phenotyping methodsFVCという植物状態を古典的画像処理と深層学習で推定し、セグメンテーション精度と環境変動への頑健性を比較評価しており、表現型取得・抽出法が実験の中心的要素である。
abstractFractional Vegetation Cover (FVC) was estimated using classical image processing and advanced deep learning models.
CottonMultispectral / hyperspectralSeed / grainPhysiological trait estimationWater status / transpiration
Rapid, non-destructive and accurate prediction of cotton seed moisture content is important for assessing seed vigour and improving storage capacity. In this study, a prediction approach for cotton seed moisture content was developed based on machine learning (ML) and hyperspectral imaging. Using the cultivar Jinken 1161 as the experimental material, spectral data in the range of 935-1720 nm were acquired. Outliers were removed using the Isolation Forest algorithm, and the samples were divided into calibration and prediction sets using the spectral-physicochemical value coordinate algorithm. The raw spectra were pre-processed using four methods, including Savitzky-Golay (SG) smoothing and standard normal variate transformation, before constructing traditional ML models [partial least square regression and multiple linear regression (MLR)] and deep learning (DL) models [convolutional neural network and long short-term memory network]. To reduce data redundancy and improve computational efficiency, feature wavelengths related to moisture content were selected using the successive projection algorithm and the least absolute shrinkage and selection operator (LASSO). Comparative analysis of different algorithmic combinations identified the optimal model, which was subsequently applied to hyperspectral images for pixel-wise prediction. This application enabled visualisation of the spatial distribution of moisture within individual cotton seeds. The results showed that, given the current sample size, ML models outperformed DL models. The SG-LASSO-MLR model achieved the best performance, with a prediction correlation coefficient (R2 p), root mean square error of prediction and residual predictive deviation of 0.9557, 0.908 and 4.77, respectively. These findings provide a feasible and effective technical solution for rapid and non-destructive detection of cotton seed moisture content. These outcomes offer valuable insights for seed quality evaluation and intelligent crop monitoring.
Why it matches plant phenotyping methods綿実の水分含量という植物器官の状態を、ハイパースペクトル画像と機械学習で非破壊推定する手法を開発・比較・検証しており、表現型取得が中心です。
abstracta prediction approach for cotton seed moisture content was developed based on machine learning (ML) and hyperspectral imaging.
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.
Abstract Purpose Taro (Colocasia esculenta (L)) , a neglected and underutilized crop species (NUS), holds great potential as a future smart crop that can thrive under climate variability and change, hence sustaining food security. While taro exhibits tolerance to drought conditions, variations in physiological attributes such as leaf temperature that rises under water stress and the associated stomatal closure that is initiated to conserve water, compromise crop productivity and overall yield. Therefore, monitoring taro crop physiological indicators of water status allows for the implementation of timely interventions and targeted adaption strategies to mitigate the effects of water deficit on taro crop productivity. Methods Unmanned Aerial Vehicles (UAV), integrated with high-resolution thermal sensors, provide valuable platform for generating near-real-time spatially explicit information suitable for assessing taro crop water status physiological indicators at farm scale. Hence, this study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops. Results Findings showed that the multi-modal variable method achieves higher estimation accuracies in comparison to a single-modal technique, achieving R 2 values greater than 0.91 and rRSME values less than 14.15% of equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and chlorophyll content. Additionally, the results illustrated that the thermal wavebands and derived thermal indices are the most influential variables in estimating stomatal conductance and leaf temperature, yielding R 2 of 0.96 and 0.95, respectively. Conclusion These research findings underscore the applicability of UAV-acquired thermal remote sensing in providing rapid and robust spatially explicit information on smallholder taro crop water status for ensuring crop productivity and developing early warning systems of water stress. These findings serve as a stepping stone towards advancing agricultural monitoring frameworks and integrating NUS, such as taro, into traditional farming.
Why it matches plant phenotyping methodsUAV熱・マルチスペクトルデータと深層学習により、タロイモの水分状態、生理形質、クロロフィルなどを推定し、精度も評価しているため、植物フェノタイピング手法の応用・技術評価が中心である。
abstractthis study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops.
BACKGROUND: Durum wheat (Triticum durum L.) productivity is strongly limited by salinity stress, particularly during early growth stages, due to disruptions in growth, water relations, and nutrient uptake. Seaweed extracts (SWEs), especially those derived from Ascophyllum nodosum, are widely used as biostimulants to enhance stress tolerance; however, their effects on durum wheat under salinity remain insufficiently characterized. In parallel, artificial neural networks (ANNs) provide effective tools for modeling complex plant responses to environmental stress. RESULTS: Salinity significantly reduced growth and physiological parameters, including biomass, chlorophyll content, and relative water content. SWE applications (2 and 4 g L⁻¹) effectively mitigated these negative effects. Biochemical traits such as proline accumulation, total phenolic content, and total antioxidant capacity were markedly enhanced under salinity. SWE treatments also improved macro- and micronutrient uptake in roots and shoots. ANN models successfully predicted multiple plant traits with high accuracy (R² > 0.90 for several key parameters). These models were implemented in a web-based R Shiny application to enable real-time prediction of plant responses. CONCLUSIONS : SWE application alleviates salinity-induced stress in durum wheat by improving growth, antioxidant capacity, and nutrient acquisition. The integration of ANN modeling with experimental data provides a reliable and practical approach for predicting plant responses, supporting artificial intelligence-assisted strategies for sustainable wheat production under saline conditions.
Why it matches plant phenotyping methods塩ストレス・生物刺激剤実験を背景とするが、ANNによる複数の植物生理・生化学・栄養形質の予測とWebアプリ実装が題名および結果の中心であり、再利用可能な計算的形質推定ワークフローに該当する。
titleArtificial neural network-based estimation of physiological, biochemical, and nutrient parameters in durum wheat under NaCl and biostimulant treatments.
TomatoGreenhouseLeafPhysiological trait estimationWater status / transpiration
Introduction Accurate crop water status monitoring is crucial for optimized irrigation in controlled environments, but traditional approaches relying on damaging measurements or sporadic sampling frequently restrict real-time evaluation. Methods This study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery. Leaf turgor dynamics analysis enabled the characterization of the LPCP probe's output parameter (P p ) and its environmental drivers, and the development of predictive machine learning models. Results The results indicated that diurnal patterns of P p in drip-irrigated tomato and celery exhibited two distinct states: State I (unimodal) and State II (troughed), corresponding to moisture conditions with no or mild stress, and severe stress, respectively. The soil water content (SWC) thresholds for State I were set at SWC > 20% (tomato) and SWC > 19% (celery), whereas those for State II were set at SWC p was positively associated with solar radiation but negatively associated with SWC (in tomato) and wind speed (in celery). For State II, the associations between P p and environmental parameters were less than those in State I. Interestingly, compared to full irrigation, non-full irrigation treatments not only showed a higher proportion of State II but also resulted in an increase in both P p,max and P p,min by 15.39%-138.39% in tomato and 3.44%-94.02% in celery. These analytical results yielded four model parameter combinations based on the inclusion of SWC and the management of distinct P p states. The prediction model that integrated Combination 4 (substate P p prediction based on meteorological factors and SWC) with the random forest approach exhibited the highest accuracy (R 2 = 0.995, MSE = 2.419, RMSE = 1.540, and MAE = 0.531), with SWC identified as its key feature parameter. Discussion These findings provide a scientific foundation for optimizing the precision irrigation of greenhouse vegetables in drip systems.
Why it matches plant phenotyping methodsLPCPプローブによる葉の膨圧動態（水分状態）の非破壊測定と、機械学習による予測モデル開発が研究の中心であり、植物生理状態を抽出するフェノタイピング手法に該当する。
abstractThis study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery.
Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R2 of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R2 of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R2 across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application.
Why it matches plant phenotyping methods作物キャノピーの分光反射から植物含水量を推定する新規三帯域指数と機械学習モデルを開発・比較しており、植物生理状態の取得手法が研究の中心である。
abstractThis study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW.
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Plant 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
In citrus cultivation practice, regular monitoring of leaf leaf relative water content (RWC) can effectively guide water management, thereby improving fruit quality and yield. When applying hyperspectral technology to citrus leaf moisture monitoring, the precise quantification of RWC still needs to address issues such as data noise and algorithm adaptability. The noise interference and spectral aliasing in RWC sensitive bands lead to a decrease in the accuracy of moisture inversion in hyperspectral data, and the combined sensitive bands of chlorophyll (LCC) in citrus leaves can affect its estimation accuracy. In order to explore the optimal prediction model for RWC of citrus leaves and accurately control irrigation to improve citrus quality and yield, this study is based on 401-2400 nm spectral data and extracts noise robust features through continuous wavelet transform (CWT) multi-scale decomposition. A high-precision estimation model for citrus leaf RWC is established, and the potential of CWT in RWC quantitative inversion is systematically evaluated. This study is based on the multi-scale analysis characteristics of CWT to probe the time-frequency characteristic patterns associated with RWC and LCC in citrus leaf spectra. Pearson correlation analysis is used to evaluate the effectiveness of features at different decomposition scales, and the successive projections algorithm (SPA) is further used to eliminate band collinearity and extract the optimal sensitive band combination. Finally, based on the selected RWC and LCC-sensitive bands, a high-precision predictive model for citrus leaf RWC was established using partial least squares regression (PLSR). The results revealed that (1) CWT preprocessing markedly boosts the estimation accuracy of RWC and LCC relative to the original spectrum (max improvements: 6% and 3%), proving it enhances spectral sensitivity to these two indices in citrus leaves. (2) Combining CWT and SPA, the resulting predictive model showed higher inversion accuracy than the original spectra. (3) Integrating RWC Scale7 and LCC Scale5-2224/2308 features, the CWT-SPA fusion model showed optimal predictive performance (R 2 = 0.756, RMSE = 0.0214), confirming the value of multi-scale feature joint modeling. Overall, CWT-SPA coupled with LCC spectral traits can boost the spectral response signal of citrus leaf RWC, enhancing its prediction capability and stability.
Why it matches plant phenotyping methods柑橘葉の相対含水量という植物生理形質を、ハイパースペクトルデータ、CWT、SPA、PLSRで推定する方法の開発・評価が中心であり、ルーチン測定ではない。
abstractA high-precision estimation model for citrus leaf RWC is established, and the potential of CWT in RWC quantitative inversion is systematically evaluated.
• 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.
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
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Drought is a significant challenge to winter wheat production. Its impact can be mitigated by preventing plant moisture stress through precision agriculture. Remote sensing and machine learning have proven effective for managing moisture stress in winter wheat. This study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat under irrigation treatments: W0 (no irrigation), W1 (45-65%), W2 (55-75%), W3 (65-85%), W4 (75-95%) of field capacity, and Z (irrigation and rainfall). Our findings show that the ratio stress index (RSI), with band combinations such as RSI7 (650, 428) , RSI8 (663, 422) , and RSI9 (671, 450) , performs better in tracking PMC and LMC, demonstrating high correlation and improved average prediction metrics for vegetation index (VI) models with R 2 , RMSE, and MAE of 0.838, 2.791, and 2.093 respectively, for LMC and VI-Tc input models with 0.850, 2.731, and 2.105 for PMC. Incorporating Tc into RSI models enhances prediction accuracy, increasing R² by up to 13.82% in the RSI-Tc-SVM-PMC model and decreasing RMSE and MAE by 15.89% and 18.33%, respectively. Therefore, a combination of RSI-Tc-SVM-ANN is recommended to monitor winter wheat moisture stress.
Why it matches plant phenotyping methods冬小麦の植物・葉の含水量および水分ストレスを、VIS/NIRと冠層温度のデータ融合および機械学習で推定する手法が研究の中心であり、植物生理状態の定量的フェノタイピングに該当する。
abstractThis study highlights the potential of new indices that combine visible (VIS) and near-infrared (NIR) bands along with canopy temperature (Tc), to monitor plant moisture content (PMC) and leaf moisture content (LMC) in winter wheat
Moisture plays a critical role in crop growth and development, making accurate, efficient, and non-destructive detection and monitoring of crop water stress essential for advancing crop science research and optimizing production management. Traditional non-destructive methods for monitoring water stress primarily rely on color imaging or partial 2D spectral analysis. However, these methods are limited to two-dimensional features and fail to capture the spatial variability of water stress within the three-dimensional canopy structure of crops. To address this limitation, this study integrates RGB-D cameras and thermal infrared cameras and introduces a method for calculating the 3D spatial distribution characteristics of crop water stress using RGB-D-T fusion analysis. This approach enables high-precision detection and analysis of water stress in strawberry plants. An RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments. Using the YOLOv8-seg deep learning model, semantic segmentation of the crop canopy and the wet reference surface was performed. The segmentation results were fused with 3D point cloud data to generate a 3D dataset incorporating temperature, color, and semantic information. Subsequently, the three-dimensional distribution characteristics and dynamic changes in the canopy water stress index (CWSI) of strawberry plants were analyzed under varying moisture conditions. The results demonstrated that under low moisture gradients (15%–30%), the CWSI value increased significantly and exhibited a concentrated distribution, indicating severe water stress. Conversely, under high moisture gradients (75%–90%), the CWSI value approached zero, reflecting sufficient water supply and complete stress alleviation. Additionally, the study highlighted the variation in the temperature difference between strawberry leaves and the surrounding air, confirming the sensitivity of strawberries to water stress across different reproductive stages. The response to water deficit was most pronounced during the growth phase. By fusing multi-source data, this study achieves 3D visualization and precise quantification of water stress in strawberries, providing innovative insights and technical support for precision irrigation and crop phenotyping research.
Why it matches plant phenotyping methodsRGB-D・熱赤外センサーの融合、3D点群化、深層学習セグメンテーションにより、イチゴの水ストレスを3D定量化する取得・解析手法が研究の中心である。
abstractAn RGB-D-T acquisition system was designed and implemented to collect RGB images, depth images, and thermal infrared images of strawberries subjected to different moisture gradient treatments.
Accurate monitoring of crop phenology, biophysical attributes and agroclimatic variability is essential for optimizing agricultural practices, particularly in smallholder farming systems. In this study, we evaluated how field camera-derived Green Chromatic Coordinate (GCC) reflected variations in agroclimatic factors (rainfall and soil moisture) and biophysical attributes (leaf area index, crop height, chlorophyll content, and stomatal conductance) across agroecological zones (AEZs) in Kenya. Next, we utilized GCC time series to detect six key phenological stages of maize (Zea mays L.) - emergence, stem elongation, tasseling, kernel development, ripening, and senescence - using an amplitude-based relative threshold method. This approach was cross-validated against field observed phenology. Our analysis revealed positive correlations between GCC and plant height, chlorophyll content, and leaf area index (LAI). Daily-scale Pearson lag correlation between GCC and agroclimatic factors revealed that crops in drier ecosystems exhibited shorter response times to agroclimatic fluctuations (32 days to rainfall and 14 days to soil moisture), highlighting site-specific differences in vegetation dynamics captured by field cameras. Furthermore, results indicate that GCC effectively captured phenological stages with high accuracy (R² = 0.9, RMSE = 7.1–7.7 days), though variability was observed across sites and growth stages. Comparisons between within-site and inter-site validation suggest that localized calibration can improve accuracy. Nevertheless, the method remains robust across varying conditions, which is supported by comparison against established curve-fitting methods. Our findings highlight the potential of field cameras as a cost-effective tool for crop monitoring at a high spatial and temporal scale, with applications in crop phenology detection, biophysical monitoring, and validation of remote sensing products. Integrating this approach into regenerative agriculture frameworks could enhance decision-making and management interventions in smallholder farms.
Why it matches plant phenotyping methods圃場カメラ画像からGCCを抽出し、トウモロコシの生育ステージと生物物理形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe utilized GCC time series to detect six key phenological stages of maize
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
Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R² values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4 % to 8.4 % for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. These findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.
Why it matches plant phenotyping methodsPRISMAおよびNEON AOP hyperspectralデータを用い、複数の植物形質を推定・検証する多段階フレームワークを開発しており、形質取得手法と性能評価が研究の中心である。
abstractThis study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits
Electrochemical impedance spectroscopy (EIS) has been widely applied to bioimpedance measurements in human and animal systems; however, its potential for direct plant monitoring remains less explored. This study uses Komatsuna (Brassica rapa) to examine the feasibility of using EIS for in situ detection of plant water stress. Impedance spectra are measured noninvasively and analyzed using an equivalent circuit model designed to separate plant-related electrical properties from the electrode-plant interface. Changes in the low-frequency impedance region were observed under both irrigation and drying conditions, while the high-frequency response remained relatively stable. In particular, variations in the extracellular resistance parameter ( R o ) preceded visible water-stress symptoms and continued even after visual changes became indistinguishable. Although the number of tested plants was limited, these results suggested the potential of EIS as a rapid and cost-effective tool for early, in situ assessment of plant water status. The present study provides a proof-of-concept for extending bioimpedance-based approaches to plant systems, with implications for precision agriculture and plant physiology research.
Why it matches plant phenotyping methodsEISによる植物の水分ストレス・水分状態の非侵襲的測定法を開発・概念実証しており、植物表現型の取得が研究の中心である。
abstractThis study uses Komatsuna (Brassica rapa) to examine the feasibility of using EIS for in situ detection of plant water stress.
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with cropspecific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVベースの3Dキャノピー再構成を用いて植物構造形質を抽出し、草丈検証と14個の空間・幾何記述子の定量を行っており、表現型取得・解析が実質的に記述されている。灌漑試験への応用ではあるが、方法の検証と再利用可能なワークフローが明示されているため採用。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Accurate estimation of daily actual evapotranspiration (ETₐ ₐcₜ) is important for many aspects of research and field management. ETₐ ₐcₜ can be calculated from the reference crop ET (ETₒ) and actual crop coefficient (Kc ₐcₜ) which are influenced by the actual crop growth conditions and soil water conditions. In this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs). With UAVs flights, the daily ETc ₐcₜ was calculated by the Surface Energy Balance Algorithm for Land model (SEBAL). Without UAVs flights, daily ETc ₐcₜ was corrected by crop coefficient under full water condition (Kc fᵤₗₗ wₐₜₑᵣ) and water stress coefficient (Kₛ), based on remote sensing data, SEBAL model, and soil water balance equation. This framework was tested on winter wheat grown under six irrigation treatments from no irrigation (I0) up to five irrigations (I5) for four seasons from 2019 to 2023. The six irrigation treatments created a wide range of soil moisture and crop growing conditions. The results showed that the best timing to estimate daily ETc ₐcₜ was using the remote sensing data obtained at 11:00 local time with an R² of 0.88 and an RMSE of 0.53 mm/day. The daily ET c ₐcₜ estimated by the new framework on days without UAV flights was consistent with the ET c ₐcₜ calculated using soil water balance equation, with R² value varying from 0.74 to 0.80 under the different irrigation treatments. The results from this study demonstrated that the new framework based on UAV remote sensing data could estimate the daily ET c ₐcₜ in real time and could be further used to estimate daily ET c ₐcₜ on days without UAV flights.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とSEBAL等を統合し、作物の実蒸発散量・水ストレス状態を推定する枠組みを開発・検証しており、測定手法が中心である。
abstractIn this study, a new framework was developed to estimate the daily actual Kc ₐcₜ using multispectral and thermal data obtained from unmanned aerial vehicles (UAVs).
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationPlant / canopy temperatureWater status / transpiration
Estimating plant growth conditions in agrivoltaic, agroforestry, or urban environments are applied examples exhibiting the need to consider the intricate relationships between spatially heterogeneous microclimate conditions (short-wave and long-wave radiation, wind, turbulence, and air temperature), plant and soil energy balances with air and water exchanges. To capture these connections, the Soil–Plant–Atmosphere Continuum model from A. Tuzet has been implemented in the computational fluid dynamics software code_saturne, which simulates spatially heterogeneous and time-varying fluid flows, along with short-wave and long-wave radiation. This coupling is compared to experimental measurements from two French sites of the Integrated Carbon Observatory System (ICOS). Our model achieves significant outcomes in assessing energy exchanges, maintaining a relative error of less than 20% compared to ICOS measurements. In addition to accurately reproducing variations of latent and sensible heat fluxes due to radiation, the coupling of the water balance and stomatal conductance models demonstrates its capability to predict the evolution of soil water content over several days. Finally, an extrapolative study of fictive environments with plants beneath obstacles reveals promising opportunities to understand how obstacle-induced shadows and wakes affect plant temperature. This leads the way for further research in agrivoltaic, agroforestry, or urban configurations with spatial scales from approximatively 10m2 up to 1000m2 and temporal scales ranging from single moments to several consecutive days.
Why it matches plant phenotyping methods植物のエネルギー・水交換を推定するSPAC–CFDモデルを実装し、実測値と比較検証しており、植物の温度や水分状態などの生理状態推定が中心的な技術貢献である。
abstractthe Soil–Plant–Atmosphere Continuum model from A. Tuzet has been implemented in the computational fluid dynamics software code_saturne
Accurate simulation of the crop growth process was the foundation for the development of smart agriculture. However, the uncertainty of crop growth models limits their practical application. This study integrates the Soil Water Atmosphere Plant (SWAP) model with the Iterative Ensemble Smoother (IES) algorithm to develop the SWAP-IES optimization approach and explores various uncertainty factors of the system, including the ensemble size, observational errors setting, combination of observation variables and their corresponding observation stages, and uncertain parameters selection. The results suggested that, under water stress conditions, an ensemble size of 50 was recommended. It was advisable to choose leaf area index (LAI) and soil moisture content (SW) as observation variables, focusing on monitoring data from the flowering to the milk stage. The suitable observational error settings for LAI and SW were 0.3-0.5 m² m⁻² and 0.03-0.05 cm³ cm⁻³, respectively. For uncertain parameters, it was recommended to select the five crop parameters (RGRLAI, SPAN, CVO, EFF, and CVL) and three soil parameters (θₛ, Kₛ, and n) for simulation. The SWAP‐IES, validated with 2020 and 2021 spring wheat (Triticum aestivum L.) experiments, demonstrated high accuracy in simulating yields, with root mean square error values of 0.56 and 0.61 t ha⁻¹, respectively. The SWAP-IES optimization approach could significantly reduce the uncertainty in the simulation process and improve simulation accuracy by optimizing the system settings strategy.
Why it matches plant phenotyping methodsSWAP-IESという計算的な作物成長・収量推定手法を開発し、観測変数や不確実性設定を検討したうえで春コムギ実験により検証しており、植物形質（収量・LAI）の推定手法が中心である。
abstractThis study integrates the Soil Water Atmosphere Plant (SWAP) model with the Iterative Ensemble Smoother (IES) algorithm to develop the SWAP-IES optimization approach
Sap flow serves as the primary carrier for water, nutrients, and signaling molecules, playing a crucial role in fruit development by delivering these essential constituents to the fruit. While the efflux of sap from fruit to other organs (termed reverse sap flow) has been observed in plants, its underlying mechanisms remain unclear due to a lack of effective methodologies for comprehensive studies. Here, we pioneered the integration of real-time sap flow measurements from novel plant-wearable sensors with synchronized environmental monitoring, establishing a multimodal data framework to systematically decode the endogenous causes and exogenous triggers of reverse sap flow in watermelon plants. Our experimental results reveal that plant water supply-consumption imbalance is the core endogenous cause of reverse sap flow, which is induced by two external triggers in the natural environment: rapid light intensity surges and soil drought. Furthermore, a long-term drought stress experiment illustrates that reverse sap flow from the fruit enhances the drought resistance of plants by adjusting water redistribution within the whole plant. This study challenges the unitary view of fruit solely as a "sink" in the traditional source-sink theory, further refines the understanding of the source-sink paradigm, and provides a novel mechanism and insight for plant drought tolerance strategies.
Why it matches plant phenotyping methods新規の植物ウェアラブルセンサーによるリアルタイム樹液流計測と環境モニタリングの統合が研究の中心で、植物の水輸送状態という生理形質を取得・解析している。
abstractlack of effective methodologies for comprehensive studies
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
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discussions. This work was supported by the Center for Research on Programmable Open asset ↗GitHub · desai-sahil/sys-bio-gspdf-raw-page:9 lines:1-74Plant 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.
LettuceSugar beetGreenhouseLeafPhysiological trait estimationBiomass / plant weightWater status / transpiration
Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of R2=0.697 and R2=0.794 for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops.
Why it matches plant phenotyping methods葉の電気応答を用いて葉面水分量と生体重量を推定する非侵襲センシング手法を開発・評価しており、植物表現型の取得が研究の中心である。
abstractthis work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response.
Title: ReDuCit Project Poster: Building a More Sustainable Agriculture Based on Robust, Scalable Applications of Controlled Deficit Irrigation Strategies Description: This poster presents the ReDuCit project, a 24-month initiative focused on developing sustainable irrigation strategies for citrus crops in the Guadalquivir River Basin (Spain). The project addresses the critical challenge of maintaining agricultural productivity while reducing water consumption in a region where citrus represents 5.1% of the irrigated area but accounts for 9.7% of water demand, and where climate projections indicate a 10% reduction in water availability by 2039. Project objectives include: Designing and validating an integrated system for monitoring water status and production in citrus crops Establishing a replicable and scalable model applicable to other crops Developing a digital platform for optimized irrigation management Implementing a robust Regulated Deficit Irrigation Control model capable of reducing water consumption by 15-25% Technical approach: The project combines water status monitoring (using sap flow sensors, trunk stem dendrometers, and microtensiometers on reference trees), production tracking (through autonomous RGB-D cameras with AI for fruit detection and measurement), and an integrated digital platform providing real-time data collection, automated irrigation recommendations, and personalized management alerts. Expected impact: Potential water savings of 60 million m³/year in citrus crops from the Guadalquivir region alone, representing 20% of the required reduction in the agricultural sector by 2039. Consortium: OnTech Innovation, Rovimatica, Universidad de Sevilla, Soltel Group Funding: Co-financed by European Funds through Junta de Andalucía and the Spanish Ministry of Finance Validation: Real-world testing in collaboration with the Irrigation Community of the Lower Guadalquivir Valley Bilingual poster (English/Spanish) Keywords: precision agriculture, deficit irrigation, water management, citrus crops, digital agriculture, IoT sensors, artificial intelligence, sustainability, Guadalquivir, smart farming
Why it matches plant phenotyping methods柑橘の水分状態と果実の検出・計測を行うセンサー／RGB-D・AI統合システムの設計・検証がプロジェクトの中心であり、植物状態・果実形質の取得方法を含むため。
abstractDesigning and validating an integrated system for monitoring water status and production in citrus crops
Title: ReDuCit Project Poster: Building a More Sustainable Agriculture Based on Robust, Scalable Applications of Controlled Deficit Irrigation Strategies Description: This poster presents the ReDuCit project, a 24-month initiative focused on developing sustainable irrigation strategies for citrus crops in the Guadalquivir River Basin (Spain). The project addresses the critical challenge of maintaining agricultural productivity while reducing water consumption in a region where citrus represents 5.1% of the irrigated area but accounts for 9.7% of water demand, and where climate projections indicate a 10% reduction in water availability by 2039. Project objectives include: Designing and validating an integrated system for monitoring water status and production in citrus crops Establishing a replicable and scalable model applicable to other crops Developing a digital platform for optimized irrigation management Implementing a robust Regulated Deficit Irrigation Control model capable of reducing water consumption by 15-25% Technical approach: The project combines water status monitoring (using sap flow sensors, trunk stem dendrometers, and microtensiometers on reference trees), production tracking (through autonomous RGB-D cameras with AI for fruit detection and measurement), and an integrated digital platform providing real-time data collection, automated irrigation recommendations, and personalized management alerts. Expected impact: Potential water savings of 60 million m³/year in citrus crops from the Guadalquivir region alone, representing 20% of the required reduction in the agricultural sector by 2039. Consortium: OnTech Innovation, Rovimatica, Universidad de Sevilla, Soltel Group Funding: Co-financed by European Funds through Junta de Andalucía and the Spanish Ministry of Finance Validation: Real-world testing in collaboration with the Irrigation Community of the Lower Guadalquivir Valley Bilingual poster (English/Spanish) Keywords: precision agriculture, deficit irrigation, water management, citrus crops, digital agriculture, IoT sensors, artificial intelligence, sustainability, Guadalquivir, smart farming
Why it matches plant phenotyping methods灌漑管理プロジェクトだが、果実の検出・計測を行うRGB-Dカメラ／AIと、水分状態を測定するセンサーを統合したモニタリング基盤が技術的中核として明示されており、植物の生産・生理状態の表現型取得に該当する。
abstractDesigning and validating an integrated system for monitoring water status and production in citrus crops
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 · UnverifiedCrossref · checked 15 Sept 2026
Abstract This study evaluated a non-invasive, integrated monitoring approach to characterize the soil-plant-atmosphere continuum (SPAC) in a commercial vineyard of Pignoletto (PG) and Trebbiano Romagnolo (TR). The approach is based on a cosmic-ray neutron sensor (CRNS) to continuously monitor soil water content (SWC), which was normalized into extractable soil water (ESW) to represent plant-available water. Moreover, vapor pressure deficit (VPD) was calculated based on weather data to characterize the atmospheric demand. Finally, remotely sensed NDVI data were used to detect canopy development and vine physiological responses. Over two growing seasons, measurements of midday stem water potential (Ψ stem ) and berry composition complemented the monitoring activities. In 2023, ripening was largely buffered from atmospheric demand, with Ψ stem values between − 0.66 and − 1.06 MPa, reflecting SWC as a non-limiting factor and uniform ripening. Conversely, the 2024 season showed more negative Ψ stem (-0.95 to -1.12 MPa) and an accelerated ripening process, particularly in TR. Principal Component Analysis (PCA) explained 65% of the variance in 2023 and 81.5% in 2024, revealing that environmental drivers (ESW, VPD) became more tightly linked to physiological and grape composition traits (Ψ stem , TSS, TA). Overall, the results showed the capability of the integrated approach to capture the main interactions within the SPAC offering a non-invasive and scalable tool for supporting precision and sustainability in Mediterranean viticulture.
Why it matches plant phenotyping methods土壌水分・大気需要・リモートセンシングNDVIを統合し、ブドウ樹の樹冠発達と生理応答を非侵襲的・スケーラブルにモニタリングする手法が研究の中心であるため。
abstractThe approach is based on a cosmic-ray neutron sensor (CRNS) to continuously monitor soil water content (SWC), which was normalized into extractable soil water (ESW) to represent plant-available water.
Field / plotWhole plant / canopy / plot / fieldStress / disease detectionDisease symptoms / severityWater status / transpiration
Agricultural industry endeavors to increase the productivity and quality of crops at reduced costs, effort, and time. An obvious requirement is extensive crop health monitoring, which includes early detection of crop diseases and treatment, detection of intruders like birds, animals, and humans in the farms and their repulsion, and assessment of crop water requirements and irrigation. Unlike traditional agri-practices, advanced digital frameworks, such as Artificial Intelligence, Computer Vision, Edge Computing, and Internet of Things, provide much promising solutions, thereby escalating exhaustive researches in the agricultural domain. This communication reviews key crop health monitoring systems developed over the past decade, outlining their advantages and limitations, shedding light on real-world implementation challenges, and proposing potential directions for future research.
Why it matches plant phenotyping methods画像処理・AIを用いた作物の健康状態や病害の検出システムをレビューしており、植物状態の取得・判定手法が中心的に扱われている。ただし侵入者検知や灌漑需要評価も含むため、植物表現型への焦点はやや広い。
titleIn-Field Crop Health Monitoring Using Intelligent Image Processing over Internet of Things Framework: A Comprehensive Review
LettuceGrowth chamberRGB / grayscaleLeafClassificationWater status / transpiration
Plant motion provides valuable indicators of physiological responses to water stress. In this study, we present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants subjected to varying irrigation regimes under controlled conditions. Four water availability treatments were imposed − Full Control (FC), Stress Control (SC), Mild Stress (SM), and Severe Stress (SS) − varying in timing, frequency, and intensity of irrigation protocols. Using dense optical flow on time-lapse RGB images, we extracted MK features that link leaf age to motion dynamics. These high-dimensional temporal features were compressed into descriptive and trend-based characteristics for classification. Multi-classification problem was divided into nine sub-tasks, for which feature selection and multiple machine-learning models were tested applying Leave-One-Sample-Out cross-validation. The best models were organised into four explainable hierarchical cascades. The presented system captures enough information to successfully distinguish among subtle differences in plants’ response to water availability dynamics (best architecture cascade obtained 0.93 out of fold balanced accuracy). The framework associating leaf age with MK features along with feature engineering allowed explainability – e.g., central rosette’s features were selected almost twice the expected frequency (19 out of 58) in tasks involving the stress-adapted control (SC), while features capturing linear trends in motion were generally selected over twice as often as simple descriptive statistics (44 vs. 19), proving essential for distinguishing most stress conditions. The MK approach proved effective for differentiating water stress levels, positioning it as a powerful tool for digital phenotyping and a solid foundation for developing advanced temporal-aware models.
Why it matches plant phenotyping methods画像時系列から光学フローで植物の運動形質を抽出し、水ストレス状態を分類する画像ベース表現型解析手法の開発・評価が研究の中心である。
abstractwe present a structured image-based approach to define and test morpho-kinematic (MK) traits from lettuce plants
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
LeafPhysiological trait estimationWater status / transpiration
The development of non-invasive sensors for individualised plant monitoring has become essential in smart farming to increase crop production. However current approaches are focused on the measurement of soil parameters instead, which cannot provide direct information about plant health. Moreover, equipment used for the direct monitoring of plant health are costly with complex operation, hindering their use by the wider community of farmers. This work reports for the first time the development of a flexible and highly transparent sensor, based on thin conductive PEDOT:PSS/PDMS hybrid films directly deposited onto leaves. The films were fabricated by aerosol deposition and could operate under two different modes. The first mode is used for the determination of plant dryness and concentration of ions. The second mode is used as a triboelectric generator to generate up to 7.2 µW cm−2 electrical power through the friction of the sensors with a leaf. The device was assembled using a low-cost (GBP
Why it matches plant phenotyping methods植物の乾燥状態やイオン濃度を直接測定する葉上センサーを開発しており、植物状態の取得方法が研究の中心である。
abstractThis work reports for the first time the development of a flexible and highly transparent sensor, based on thin conductive PEDOT:PSS/PDMS hybrid films directly deposited onto leaves.
CoffeeMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.
Why it matches plant phenotyping methodsコーヒー植物の葉スペクトルから水ポテンシャルという生理形質を機械学習で推定・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・評価に該当する。
abstractThe objective of this study was to estimate water potential in coffee plants using spectral variables.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's datasets (coffee leaf spectral reflectance and water potential measurements) and the MATLAB analysis codes are publicly available at the authors' UFLA repository, which is an allowed URL. This is a paper-specific, public, actionable asset.Dataset · publicData Availability Statement: The datasets and MATLAB codes used in this study are available at
http://www.aia.ufla.br/home/filesdatasets/, accessed on 27 November 2025.Open asset ↗aia.ufla.brpdf-page:18 lines:1-54Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
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.
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Pea storage stability and germination rely on moisture content and morphology, but traditional destructive methods cause sample damage, low efficiency, and subjective errors, limiting practical use. To overcome the destructive and inefficient limitations of traditional methods for pea quality assessment, this study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning. We innovatively converted one-dimensional spectral data into two-dimensional texture images via Gramian Angular Field (GAF) encoding and input them into a residual 2D Convolutional Neural Network (2D-CNN) for moisture prediction. For dimensional analysis, a novel algorithm based on irregular polygon geometry was proposed to accurately measure pea length and width. The GAF-2D-CNN model achieved superior performance for moisture prediction (prediction set R²=0.9818, RMSEP=0.0318 %, RPD=7.4780), significantly outperforming 1D-CNN, Least Squares Support Vector Machine (LSSVM), and Partial Least Squares Regression (PLSR) models. The dimensional algorithm also demonstrated high accuracy, especially for length measurement (R²=0.9946, RPD=13.94). This framework provides a robust, accurate, and high-throughput solution for automated pea quality grading, offering significant potential for applications in precision agriculture and storage management.
Why it matches plant phenotyping methodsハイパースペクトル画像、深層学習、形状アルゴリズムを統合し、エンドウの水分含量とサイズという植物形質を非破壊・高スループットに推定する方法の開発が中心である。
abstractthis study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning.
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (SₜWC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the SₜWC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm³/cm³) indicated a slower decrease compared to healthy tobacco plants (0.021 cm³/cm³). In accordance with this phenomenon, the daily variation of SₜWC near roots of diseased tobacco plants (0.023 cm³/cm³) was significantly less than that of healthy tobacco plants (0.048 cm³/cm³). Moreover, the abnormal changes of SₜWC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of SₜWC was continuously less than 0.037 cm³/cm³. Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection.
Why it matches plant phenotyping methods根域付近の茎水分量という植物の生理状態を測定し、根病害の早期検出に用いるウェアラブルセンサーを開発・検証しており、表現型取得手法が中心である。
abstractwe developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
• A UAV-based novel phenotyping pipeline using multispectral imaging and LASSO regression accurately predicts soybean traits and yield across growth stages by selecting key vegetation indices. • Red-edge and NIR indices best predict plant height, stomatal conductance, and yield. • Chlorophyll-related indices were effective for estimating LAI and leaf chlorophyll. • Best aerial phenotyping time is between pod development and the full seed stage. Morphophysiological parameters, such as plant height, leaf chlorophyll content, stomatal conductance, and leaf area index, are key indicators of soybean ( Glycine max (L.) Merril) yield potential. Traditional in situ methods for assessing these traits, while accurate in small areas, are slow, labor-intensive, and impractical for large-scale monitoring. Similarly, extrapolating yield from manual counts of plant stands, pods, and seeds per pod may provide unreliable results. Therefore, high throughput sensor-based approaches are becoming increasingly popular to efficiently quantify these traits and predict yield. Among various remote sensing sensors, multispectral provides information in the red, green, red-edge, and near-infrared bands, which are critical for studying plant growth and vegetation health by combining multiple spectral bands. While many studies have used vegetation indices (VIs) to estimate individual traits, fewer have predicted multiple traits and yield at the same time using multispectral data. Thus, a study was conducted to identify the most effective VIs and determine the optimal timing for aerial phenotyping using multispectral sensors and LASSO regression. The study suggested Red-edge and NIR-based indices were best for predicting plant height, stomatal conductance, and yield, while chlorophyll-related indices were more effective for LAI and chlorophyll content. The pod development to full seed stages was the best time for aerial phenotyping. Overall, UAV-derived MS images combined with LASSO regression proved to be a practical and efficient approach for large-scale soybean phenotyping and yield monitoring. This study supports precision agriculture by providing a remote sensing-based, rapid, and non-destructive method for assessing crop status.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とLASSO回帰による複数の植物形質・収量の推定手法を開発・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 UAV-based novel phenotyping pipeline using multispectral imaging and LASSO regression accurately predicts soybean traits and yield across growth stages by selecting key vegetation indices.
Accurately estimating individual plant evapotranspiration is essential for precise management and sustainable resource use in greenhouse cultivation. Integrating evapotranspiration models with crop-monitoring devices capable of acquiring images and solar radiation data may enable plant-level estimation of crop evapotranspiration. In this study, a plant-specific crop evapotranspiration estimation system was developed for hydroponic tomato cultivation in greenhouses during the harvest season. The evapotranspiration was estimated using a simplified Penman–Monteith model based on the leaf area index (LAI), solar radiation, air temperature, and relative humidity. The model was subsequently generalized through z-score normalization. To acquire side-view RGB images of individual tomato plants and measure the solar radiation distribution, a rail-based crop-monitoring device was employed. A ResNet-based convolutional neural network model was developed to estimate the LAI from the acquired images. The images were augmented via permutations with repetition to enhance the model’s accuracy. An image-merging method and a You Only Look Once version 8 Nano-based object detection model were used for rapid and automated image acquisition. The system calculated the crop evapotranspiration for each plant, and its performance was evaluated in a tomato cultivation greenhouse. Validation tests revealed strong correlations between the estimated and measured LAI (R² = 0.89, RMSE = 0.06) and between the predicted and actual evapotranspiration values (R² = 0.88, RMSE = 26.43 g h⁻¹ plant⁻¹). Distribution maps for the LAI and evapotranspiration were generated using the developed system. The system can accurately assess plant-specific evapotranspiration, thereby supporting precision crop management and helping improve productivity in greenhouse cultivation.
Why it matches plant phenotyping methods個体別のLAI画像推定と蒸発散量推定を中核とする監視システムを開発し、実測値との相関で検証しているため、植物表現型取得・推定手法として対象に含める。
abstracta plant-specific crop evapotranspiration estimation system was developed for hydroponic tomato cultivation in greenhouses
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
MaizeMultispectral / hyperspectralSeed / grainPhysiological trait estimationWater status / transpiration
The moisture content of maize seeds is a key factor affecting seed quality, germination vigor, storage safety, and shelf life. Rapid, accurate, non-destructive detection can help prevent seed decay and mold growth, thereby reducing economic losses. This study employed hyperspectral imaging (400–1000 nm) to collect 237-band spectral data from 300 Haimai515 maize seeds, resulting in 71,100 pixel-level datasets aimed at achieving rapid, non-destructive, and accurate prediction of seed moisture content. Five machine learning algorithms-Ridge regression, Lasso regression, Support Vector Regression (SVR), CatBoost, and Partial Least Squares Regression (PLSR)-were evaluated for their predictive performance. Among the evaluated models, the one that combined Gaussian Window Smoothing (GWS) preprocessing with Gradient Boosting Decision Tree (GBDT)-based feature extraction for PLSR achieved the best performance, namely the GWS-GBDT-PLSR model, achieved the best performance with an R² of 0.953 and an RMSE of 1.557. To further improve prediction accuracy, a deep temporal learning model (GWS-LSTM) was developed using the same GWS preprocessing. This model achieved superior performance, with an R² of 0.978 and an RMSE of 1.461. The GWS-LSTM model improves accuracy while simplifying preprocessing and feature selection, providing an efficient, non-destructive moisture detection method.
Why it matches plant phenotyping methodsトウモロコシ種子の水分含量という植物器官形質を、ハイパースペクトル画像と機械学習で非破壊推定する手法の開発・性能評価が中心である。
titleA method for monitoring moisture content in maize seeds using a deep temporal network based on hyperspectral features
Black shank disease and root rot disease represent the most destructive diseases of tobacco. Once it occurs, it will spread rapidly, endangering the health of tobacco plants, and even killing them. The stem near the root of tobacco plant is the first part that can exhibit observable signs of root disease. Monitoring the dynamic variations of in-situ stem water content (S t WC) near the root is beneficial for the early detection of tobacco root diseases. Therefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases. The IE probe of wearable sensor was securely affixed to the stem, and the soil moisture (SM) sensors were buried in the corresponding root area. The results demonstrated a clear inconsistency in the observed trend between the S t WC near roots of diseased and healthy tobacco plants. About 60 h before the blackening of the stems near roots, the SM of diseased tobacco plants (0.007 cm 3 /cm 3 ) indicated a slower decrease compared to healthy tobacco plants (0.021 cm 3 /cm 3 ). In accordance with this phenomenon, the daily variation of S t WC near roots of diseased tobacco plants (0.023 cm 3 /cm 3 ) was significantly less than that of healthy tobacco plants (0.048 cm 3 /cm 3 ). Moreover, the abnormal changes of S t WC near roots of diseased tobacco plants after blackening further validated the availability of the wearable sensor in the early detection and warning of tobacco root diseases. The tobacco plant may have been in early diseased stage when the daily change of S t WC was continuously less than 0.037 cm 3 /cm 3 . Future research will focus on the mechanism of water conduction between soil and stem near the root of tobacco plants, and the potential application of the wearable sensor in early disease detection. • A wearable plant sensor is developed for early warning of tobacco root diseases. • The sensors were used to monitor diseased and healthy tobacco plants in the field. • The sensor can achieve early in-situ detection of tobacco root diseases.
Why it matches plant phenotyping methods植物茎内水分状態を測定し、根部病害の早期検出へ用いるウェアラブルセンサーを開発・検証しており、植物状態の取得法が中心である。
abstractTherefore, we developed a wearable plant sensor with a flexible interdigitated-electrodes (IE) probe design for in-situ monitoring of StWC and early identification and warning of tobacco root diseases.
Improving water and nutrient use efficiency is essential for increasing crop yields and addressing global population growth. Optimal irrigation and nitrogen topdressing levels can enhance crop water and nitrogen use efficiency. UAV remote sensing has emerged as an efficient tool for optimizing water and nitrogen management due to its ability to monitor crop traits in real-time. This study proposed a UAV-based hyperspectral imaging method to optimize water-nitrogen management in winter wheat. By analyzing the interaction between nitrogen fertilizer and irrigation, a coupling decision model was developed for precise water-nitrogen application. Leaf water content (LWC) and chlorophyll content (SPAD) were estimated using machine learning algorithms combined with sensitive band selection methods, such as Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS). The SPA-Random Forest (RF) model performed best for LWC estimation (R² = 0.83, RMSE = 5.39 %), while the VIs-RF model was optimal for SPAD estimation (R² = 0.65, RMSE = 4.34 %). Conversion models linked LWC to soil water content (SWC) and SPAD to leaf nitrogen content (LNC), achieving R² values of 0.79 and 0.78, respectively. The proposed water-nitrogen coupling model exhibited strong adaptability and stability during key growth stages by integrating hyperspectral inversion data with field measurements. This model enables dynamic water and nitrogen application rate adjustments across the growing period to achieve target yields, optimize application strategies, and enhance use efficiency. The findings underscore the significant potential of UAV-based hyperspectral technology in optimizing water-nitrogen management. This method provides a reference for improving water-nitrogen use efficiency from the perspective of water-nitrogen coupling on yield.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から葉含水量・クロロフィル含量を推定する手法を提案・評価しており、植物形質の取得・推定が水窒素管理への応用とともに中心的である。
abstractThis study proposed a UAV-based hyperspectral imaging method to optimize water-nitrogen management in winter wheat.
This study evaluates the potential of UAS-based and proximal sensing tools to assess water stress and how derived indices correlates with yield in almond orchards in the semiarid conditions of southeast Spain. Two commercial orchards with contrasting irrigation regimes were monitored in 2023 using multispectral and thermal UAS imaging, alongside ground-based physiological and agrometeorological measurements. The Crop Water Stress Index (CWSI), calculated empirically from thermal data, and multispectral vegetation indices (VIs) were validated against stomatal conductance, stem water potential, and gas exchange parameters. Spatial variability in water status was explored using growth variability maps derived from NDVI and cumulative transpiration estimates. Results revealed significant correlations between UAS-based CWSI and water-related traits, with R² values exceeding 0.85 for stem water potential and intrinsic water-use efficiency. VIs, particularly those related to pigment composition (e.g., CCCI, MTCI, and CRI2), also demonstrated predictive capacity for physiological traits while NIR-related indices showed notable correlations with yield. Yield correlations were most accurate when integrating CWSI with pigment-sensitive indices such as PSRIm and chlorophyll-related VIs. Findings in this work are promising; however, challenges including proper calibration of UAS data and the influence of post-harvest physiological changes were also noted. This study highlights the value of combining thermal and multispectral remote sensing to optimize water management, while presenting promising results that open new windows for future yield prediction in almond orchards, offering a scalable approach for precision agriculture.
Why it matches plant phenotyping methodsUASの熱・マルチスペクトルセンシングによりアーモンドの水分状態や生理形質を推定し、地上測定値との検証および校正課題を扱っており、表現型取得手法が研究の中心である。
abstractThis study evaluates the potential of UAS-based and proximal sensing tools to assess water stress and how derived indices correlates with yield in almond orchards in the semiarid conditions of southeast Spain.
Accurate crop monitoring is essential for agricultural planning and food security. This study developed a coupling framework of unmanned aerial vehicle (UAV) multimodal data and crop models based on a sequential data assimilation method, offering technical support for crop growth simulation and precision management under drip irrigation modes in the Hexi Corridor of Northwest China. Multispectral and thermal infrared image data of spring maize at different growth stages were acquired via UAVs. The UAV-derived leaf area index (LAI) and soil moisture (SM) were assimilated into the WOFOST model using the ensemble Kalman filter (EnKF). Three assimilation schemes including (a) LAI, (b) SM, and (c) LAI+SM were compared to explore the effects of different mulching treatments (mulched vs. non-mulched) and irrigation gradients on assimilation performance under drip irrigation modes. Our results showed that the fusion of UAV-based multispectral and thermal infrared multimodal data enabled accurate retrieval of LAI and SM, with a maximum R² of 0.85. The three assimilation schemes exhibited significant differences, and the joint assimilation of LAI and SM outperformed the others. This may be since LAI and SM, as key indicators of crop growth and development, undergo dynamic changes throughout the growth period, and their joint assimilation fully captures the temporal variability of crops and soil. In addition, the proposed framework demonstrated marked variations in simulation accuracy across different drip irrigation modes. Overall, the performance for shallow buried drip irrigation (SBDI) was superior to that for surface drip irrigation (SDI) and film-mulched drip irrigation (FDI). This may be attributed to the direct influence on soil evaporation and evapotranspiration under the latter two modes, which in turn modifies crop growth and development processes and ultimately affects the model's simulation accuracy.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外データからLAIを推定し、作物モデルへ同化する手法の開発と性能比較が研究の中心であり、植物形質取得の技術的評価を含む。
abstractThis study developed a coupling framework of unmanned aerial vehicle (UAV) multimodal data and crop models based on a sequential data assimilation method
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Wheat quality detection plays a crucial role in the processing of grain storage, and moisture distribution is one of the main factors that affect wheat quality. The uniformity of moisture distribution in wheat grains significantly impacts their morphological structures, nutrient distribution, storage period, and stress resistance. This study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS) to scan wheat grains soaked for different times (0, 2, 4, 6, 8, and 10 h) and dried for different times (0, 1, 2, 3, 4, and 5 h). The scanned results are used to observe the water content changes in wheat grains on both temporal and spatial scales. This study calculates the average spectrum of wheat grains to observe the regular changes in the terahertz time-domain spectrum of wheat grains under different soaking and drying degrees. These changes exhibit opposite trends. The frequency domain spectra are obtained through Fast Fourier Transform (FFT), and comparing the imaging effects at different frequency points, it can be observed that there is a good consistency between frequency-domain imaging and time-domain imaging. The experimental results indicate that THz-TDS can be used to effectively observe the moisture distribution in wheat grains during the soaking and drying processes.
Why it matches plant phenotyping methodsTHz-TDSによる小麦粒内の水分分布という植物器官の状態を画像化・評価する手法が研究の中心であり、吸水・乾燥過程での画像化性能を検討している。
abstractThis study detects the moisture distribution in wheat grains by using terahertz time-domain spectroscopy (THz-TDS)
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
MangoRaman / spectroscopyFruitPhysiological trait estimationWater status / transpiration
Accurate, rapid, and online determination of mango dry matter content (DMC) holds great significance for the mango industry. The integration of near-infrared spectroscopy and deep learning theory offers an opportunity to enhance determination accuracy. In this paper, we propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC. The test results indicate that the model performs optimally when trained on a training set comprising 80 % of the augmented data. The root mean square error (RMSE) of the test set was 0.4073, and the coefficient of determination (R²) was 0.9782. The prediction accuracy of our model surpasses that of models such as Gaussian Process Regression, Support Vector Machines, and Partial Least Squares Regression. This study can assist in fruit quality inspection, processing optimization, variety selection, and breeding, as well as storage and preservation, and has a wide range of application potential and value. It also provides novel insights into data augmentation techniques for near-infrared spectral regression modeling.
Why it matches plant phenotyping methodsマンゴー果実の乾物含量という植物器官形質をNIRSと1-D CNNで推定する手法を開発・評価しており、形質取得・抽出法が研究の中心である。
abstractwe propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC.
PURPOSE: Climate change, increasing aridity, water scarcity and population growth, enhancing food demand and irrigated land expansion, are expected to increase the extent of salinity-affected areas. This study aims to combine the crop-energy-water balance model FEST-EWB-SAFY with Leaf Area Index (LAI) and Land Surface Temperature (LST) data from remote sensing to monitor maize development in a field with a shallow water table and highly affected by salinity. METHODS: The FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which computes time-continuous soil moisture and evapotranspiration, and the SAFY crop model for yield prediction. The model was employed in synergy with satellite observations of LST and LAI. LST was used for the calibration/validation of the water and energy balances, whereas LAI was used both for the calibration of crop parameters and a data assimilation scheme. RESULTS: The data assimilation scheme was able to reproduce the observed spatial heterogeneity in crop development, associated to the uneven water table depth and salinity distribution, as these effects were picked up from satellite. A good correspondence was also found between modelled yield and the distributed samplings from a combined harvester equipped with a yield monitor. CONCLUSION: The results, comparable to those obtained with the a posteriori calibration, show that data assimilation of remote sensing observations allow to improve the model as the agricultural season progresses, including information which is difficult to monitor continuously in-situ.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・LST観測を作物モデルに同化し、圃場内のトウモロコシの発達と空間的不均一性を推定する技術的ワークフローが中心である。
abstractThe model was employed in synergy with satellite observations of LST and LAI.
Field / plotStomata / guard-cell complexPhysiological trait estimationCalibration / preprocessingStomatal traitsWater status / transpiration
The development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis. We compiled a data set comprised of 25 angiosperm species, across a range of field conditions and found that the LI-COR LI-600, an open flow-through porometer, produced an exponentially increasing bias relative to the LI-COR LI-6800 infra-red gas analyser-based instrument in response to increasing stomatal conductance and decreasing relative humidity. This bias was minimal at lower stomatal conductance (below roughly 0.25 mol m -2 s -1 ), but was pronounced for larger values. We hypothesised that this bias is the result of the assumption of a constant air temperature throughout the flow stream used by the instrument software to estimate stomatal conductance from raw sensor measurements. We relaxed this assumption, and applied psychrometrics to augment the typical gas exchange equations with an additional energy balance constraint to solve for the temperature change throughout the air flow stream. We found that including this temperature difference corrects the computed transpiration and stomatal conductance values, and brings the porometer measurement into agreement with that of the infra-red gas analysis-based system. Software is provided to apply the correction to LI-600 output files. For future instrument design iterations, explicit measurement of temperature variation in the flow stream provides a potential opportunity for improvement in measurement accuracy at high stomatal conductance.
Why it matches plant phenotyping methods気孔コンダクタンス測定器の系統誤差を検証し、物理モデルと補正ソフトウェアで植物生理形質の測定精度を改善する方法研究である。
abstractThe development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis.
AppleField / plotRaman / spectroscopyFruitClassificationWater status / transpiration
To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications.
Why it matches plant phenotyping methodsリンゴの水心症状の程度を可搬型Vis/NIR装置と画像・深層学習で定量・分類する計測手法および装置の開発が研究の中心であり、植物病害状態の表現型取得に該当する。
abstracta portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Orchids are highly valued ornamental plants whose growth conditions directly impact the economic returns of the horticultural industry. The substrate, acting both as a physical support and a nutrient reservoir, is critical for orchid development. Therefore, the careful selection of an appropriate growth substrate is of paramount importance. However, existing research on the relationship between orchid growth and substrate properties relies mainly on manual measurements of physiological indicators, with limited application of high-throughput phenotyping (HTP) platforms. In this study, we evaluated three distinct substrate types, peat soil mixed with perlite, pine bark, and river sand, which were applied to two orchid species, Cymbidium goeringii and Cymbidium faberi . Using the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate). This platform enabled precise and rapid quantification of orchid growth indicators. The results show that the type of substrate significantly affects orchid growth. Under controlled conditions, mixed substrates that provide balanced nutrition and excellent drainage enhanced orchid growth compared to other substrates. Additionally, when the data obtained from the HTP platform were compared with those from traditional manual measurements, the automated system showed higher reliability and accuracy. This study not only provides practical guidance for selecting cultivation substrates for orchids, but also establishes a robust scientific framework for integrating advanced phenotyping technologies into orchid cultivation practices.
Why it matches plant phenotyping methodsPlantarray高スループット表現型計測システムによる生長・生理形質の連続測定と、手動測定との信頼性・精度比較が研究の中心であり、基質効果の単なる生物実験にとどまらない。
abstractUsing the high-throughput Plantarray lysimetric system, we continuously recorded environmental parameters (photosynthetically active radiation, humidity, and temperature) as well as key growth metrics (biomass accumulation, canopy conductance, and transpiration rate).
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model's architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.
Why it matches plant phenotyping methods植物ストレス状態を土壌水分とクロロフィルから推定するSAM-L・XAI・LSTM統合手法が研究の中心であり、植物の生理状態を対象とした計算的フェノタイピング手法に該当する。
abstractIn this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content.
Reproduction assets foundThe paper states its plant-stress phenotyping data came from a publicly available Kaggle dataset ('Real-Time Plant Health Insights: Simulated Biosensor Data for AI-Driven Monitoring'), used directly for the SAM-L/XAI stress-prediction experiments. Code is only available on request, so it does not qualify as a public,作者Dataset · publicThe dataset used in this study was sourced from Kaggle and was publicly available.Open asset ↗Kagglepdf-page:16 lines:1-70Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Drought stress poses a significant threat to rice production, particularly in indica cultivars that form the staple diet for a large portion of the world's population. Efficient and reliable screening methods are essential to accelerate the development of drought-tolerant rice varieties. The objective of the study was to assess and validate the efficacy of the Drought Tolerance Degree (DTD) method for early-stage drought tolerance screening in a diverse population of 118 doubled haploid (DH) indica rice lines and their parents. Plants were subjected to controlled severe drought stress under pot culture in a net house environment, and drought responses were assessed using DTD method alongside key physiological traits including relative water content (RWC), chlorophyll content index, leaf rolling and drying scores, leaf canopy temperature, leaf area, tiller and leaf numbers, and plant height. The DTD values exhibited strong positive correlations with RWC (r = 0.771) and chlorophyll content (r = 0.526), and strong negative correlations with leaf rolling (r = -0.850), leaf drying scores (r = -0.778), canopy temperature, and tiller number. Principal component and hierarchical clustering analyses further confirmed the association of DTD with drought tolerance-related traits and effectively discriminated tolerant and susceptible genotypes. In comparison with traditional methods, the DTD assay is cost-effective, rapid, and requires minimal technical expertise, making it practical for high-throughput screening in breeding programs. However, its applicability is limited to early growth stages due to the confounding effects of natural leaf senescence at maturity. Overall, this work demonstrates the reliability and efficiency of the DTD method in assessing drought tolerance in indica rice, offering a valuable phenotyping tool to facilitate the selection of drought-resilient cultivars in breeding pipelines.
Why it matches plant phenotyping methodsDTD法をイネの乾燥耐性表現型スクリーニングに用い、その有効性・信頼性を検証した研究であり、表現型取得法が中心です。
abstractThe objective of the study was to assess and validate the efficacy of the Drought Tolerance Degree (DTD) method for early-stage drought tolerance screening
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.
WheatField / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Root hydraulic properties affect water uptake in wheat ( Triticum aestivum L.) and are strongly influenced by root anatomy, yet how they vary along root axes and interact with cultivar differences remains underexplored. We investigated crown roots of six German winter wheat cultivars spanning one century of release, sampled from a field experiment. Roots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ). Longitudinal anatomical gradients were pronounced: tissue dimensions, metaxylem number, and apoplastic barriers decreased from the base onwards, resulting in K r increasing and k x decreasing with distance from the base. Cultivar differences were also apparent: modern cultivars had smaller tissues and fewer metaxylem vessels, reducing both axial and radial conductance and lowering whole-root water uptake capacity (∼20–30%). By integrating field sampling with high-throughput image analysis and mechanistic modeling, this study establishes an integrated phenotyping approach that links root anatomy to water uptake and uncovers anatomical traits relevant to hydraulic function. The results show that longitudinal gradients and cultivar-associated anatomical differences contribute to variation in hydraulic properties and persist along fully mature root segments. Highlight High-throughput imaging–modeling shows that longitudinal gradients and cultivar-associated anatomical differences along crown roots shape radial and axial conductance, leading to reduced whole-root water uptake capacity in modern winter wheat
Why it matches plant phenotyping methods根の高スループット画像解析と機械論的モデリングを統合し、解剖形質から水理特性を推定するフェノタイピング手法が研究の中心であるため。
abstractRoots were imaged at different positions along their axis using a high-throughput system (Rapid Anatomics Tool), and the resulting anatomical traits were coupled to GRANAR–MECHA to model radial ( K r ) and axial conductance ( k x ).
Drought stress is a major constraint on watermelon production worldwide. Conventional phenotyping methods for drought tolerance are often low-throughput and fail to capture dynamic physiological responses. This study validated the high-throughput phenotyping platform (Plantarray 3.0) against conventional methods by dynamically evaluating drought tolerance across 30 genetically diverse watermelon accessions. The Plantarray system quantified key dynamic traits, including transpiration rate (TR), transpiration maintenance ratio (TMR), and transpiration recovery ratios (TRRs), revealing distinct drought-response strategies. Principal component analysis (PCA) of these dynamic traits explained 96.4% of the total variance (PC1: 75.5%, PC2: 20.9%), clearly differentiating genotypes. A highly significant correlation (R = 0.941, p < 0.001) was found between the comprehensive drought tolerance rankings derived from Plantarray and conventional phenotyping. We identified five genotypes as highly tolerant and four as highly sensitive. The elite drought-tolerant germplasm, notably the wild species PI 537300 (Citrullus colocynthis) and the cultivated variety G42 (Citrullus lanatus), exhibited superior physiological performance and recovery capacity. The results demonstrate that the Plantarray system not only efficiently screens for drought tolerance but also provides deep insights into dynamic resistance mechanisms, offering a powerful tool and valuable genetic resources for breeding climate-resilient watermelon cultivars.
Why it matches plant phenotyping methodsPlantarray高スループット表現型解析プラットフォームを従来法と比較検証し、動的な植物生理形質による乾燥耐性評価を中心に扱っているため。
abstractThis study validated the high-throughput phenotyping platform (Plantarray 3.0) against conventional methods by dynamically evaluating drought tolerance across 30 genetically diverse watermelon accessions.
Abstract Efficient irrigation management is fundamental to sustainable crop production, particularly under increasing temperatures and limited water availability. In vineyards, water stress significantly influences grapevine development and productivity. Controlled water stress is intentionally applied in deficit-irrigated systems to regulate yield and enhance fruit quality. Therefore, vineyards must be routinely monitored to prevent excessive stress that could cause detrimental effects. In this study, we developed a machine-learning framework based on the eXtreme Gradient Boosting (XGB) machine-learning model to estimate grapevine leaf water potential (Y leaf ) using meteorological data and high-resolution imagery from small unmanned aerial systems (sUAS) over commercial vineyards of different varieties and in different climatic zones in California. The framework incorporates key meteorological and image-derived features, including maximum air temperature in the 24 hours prior to the flight, air temperature at the time of flight, the difference between these two temperatures, as well as canopy temperature derived from sUAS thermal imagery. These features were included to indirectly capture plant-water-weather interaction during the 24-hour period preceding data collection, enhancing the model’s practical applicability. The XGB model demonstrated robust performance, achieving an RMSE of 0.16 MPa, a bias of -0.06 MPa, and a correlation coefficient of 0.83 while minimizing computational cost. Model generalizability was further validated in an independent vineyard, demonstrating its potential for commercial application in precision irrigation and vineyard water management. Our research highlights the potential for broader applicability, particularly in addressing flash drought and promoting adaptive water resource management.
Why it matches plant phenotyping methodssUAS熱画像と気象データからブドウ葉の水ポテンシャルを推定する機械学習手法を開発し、独立圃場で妥当性を検証しており、植物表現型取得が中心である。
abstractwe developed a machine-learning framework based on the eXtreme Gradient Boosting (XGB) machine-learning model to estimate grapevine leaf water potential (Y leaf ) using meteorological data and high-resolution imagery from small unmanned aerial systems (sUAS)
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
Non-destructive testing (NDT) methods are playing a crucial role in modern agriculture by providing efficient, rapid, and non-invasive means of evaluating agricultural materials. This shift from traditional, often destructive, testing methods is driven by the need for better quality control, improved food safety, and the demands of intelligent and precise agriculture Polarization spectroscopy analysis (PSA) has emerged as an advanced, non-destructive testing method of growing importance in agricultural engineering. By integrating polarization characteristics with spectral data, PSA enables the detailed analysis of various agricultural products and processes.This review provides a systematic overview of the principles and key parameters of polarimetry. Furthermore, it highlights a wide range of PSA applications in agricultural materials, such as crop health assessment, pest detection, chlorophyll estimation, and the evaluation of water, nitrogen, phosphorus, and potassium content. In addition, it sheds light on further applications, including non-destructive testing of seed health and agricultural product quality, soil moisture and pollution monitoring, underwater and nighttime environmental imaging, and integration with hyperspectral and multispectral technologies.Polarization spectroscopy is an analytical technology capable of revealing physical structural information unresolved by traditional spectroscopy, especially in complex environments where it demonstrates greater resistance to interference. With its ability to monitor plant nutrition, predict seed germination, assess fruit and vegetable quality, and detect early pests and diseases, this technology holds great promise for precision agriculture. Future efforts should optimize data fusion, build efficient models, miniaturize intelligent equipment, and enhance the real-time performance and adaptability of non-destructive testing to support smart agriculture..
Why it matches plant phenotyping methods偏光分光法を農業材料へ適用するレビューであり、作物健全性、クロロフィル、栄養、発芽、病害虫など植物形質・状態の非破壊推定を主要な応用として扱っているため、植物フェノタイピング手法レビューに該当する。
abstractThis review provides a systematic overview of the principles and key parameters of polarimetry.
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
The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data.
Why it matches plant phenotyping methods植物のLAI、赤外線指数、樹冠温度差、水ストレス指数を、カメラ・E-nose・コンピュータビジョンで取得する統合的な植物モニタリング手法が研究の中心である。
abstractThis research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms
Introduction The cigar leaves moisture content (CLMC) is a critical parameter for controlling curing barn conditions. Along with the continuous advancement of deep learning (DL) technologies, convolutional neural networks (CNN) have provided a way of thinking for the non-destructive estimation of CLMC during the air-curing process. Nevertheless, relying merely on single-perspective imaging makes it difficult to comprehensively capture the complementary morphological features of the front and back sides of cigar leaves during the air-curing process. Methods This study constructed a dual-view image dataset covering the air-curing process, and proposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images. Firstly, the model utilizes two independent and parallel ResNet as its backbone structure to capture the heterogeneous features of dual-view images. Secondly, the Dual Efficient Channel Attention (DECA) module is introduced to dynamically adjust the channel attention weights of the features, thereby facilitating interaction between the two branches. Lastly, a Multi-scale convolutional feature fusion (MSCFF) module is designed for the deep fusion of features from the front and back images to aggregate multi-scale features for robust regression. Results On five-fold cross-validation, CADFFNet attains R2 of 0.974±0.007 and mean absolute error (MAE) of 3.80±0.37%. On an independent cross-region, cross-variety testing set, it maintains strong generalization (R2=0.899, MAE=5.82%), compared with the classic CNN models ResNet18, GoogLeNet, VGG19Net, DenseNet121, and MobileNetV2, its R2 value has increased by 0.047, 0.041, 0.055, 0.098, and 0.090 respectively. Discussion Generally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.
Why it matches plant phenotyping methods葉の水分含量という植物状態を、二視点RGB画像と新規深層学習回帰モデルで非破壊推定する手法を開発し、交差検証および独立試験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。
abstractproposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images.
ABSTRACT Climate change is causing vegetation stress across the globe, increasing the need for reliable indicators to monitor plant health. Leaf water and carotenoid content, and the chlorophyll/carotenoid ratio, are established proxies for environmental stress that can be detected by remote sensing. Here, we evaluated the sensitivity of 11 multispectral vegetation indices (VIs) designed to monitor these three stress-related leaf traits across a broad range of environmental and vegetation conditions. For this, we combined radiative transfer modeling with cross-biome field and satellite observations from Sentinel-2, Landsat 8, and MODIS from the National Ecological Observatory Network (NEON), spanning in most major terrestrial ecosystems. Our model-based analysis showed that VIs have a low to moderate sensitivity to their target traits, ranging from water indices with 66% of their variability explained by leaf water content, to carotenoid indices with 27% variability explained by leaf carotenoid content. Surprisingly, our field-based analyses revealed minimal to no sensitivity to leaf water and carotenoid content and chlorophyll/carotenoid ratio across all VIs. In contrast, we showed that leaf area index was the dominant driver of all studied VIs, accounting for 54-74 % of their variability in the field-based analysis. Lastly, we detected that VIś sensitivity to atmospheric conditions and field sampling issues contribute to their low performance in validating ground truth observations. These findings show that improvements in the VIs formulation and field sampling strategies are needed to increase the reliability of vegetation stress monitoring from multispectral satellites and support a generalized use of VIs across ecosystems. Highlights: 3-5 bullet points, 85 characters ● Sensitivity of water and carotenoid multispectral indices was evaluated ● Analysis based on cross-biome field data and radiative transfer models ● Field data showed indices had minimal sensitivity to leaf water and carotenoid ● Leaf area index explained most cross-biome variation in water and carotenoid indices ● We propose strategies to improve stress-related index formulation and validation
Why it matches plant phenotyping methods衛星マルチスペクトル指数による葉の水分・カロテノイド等の形質推定性能を、モデル・野外・衛星データで評価し、感度と検証上の課題を分析しているため、フェノタイピング手法の検証が中心です。
abstractHere, we evaluated the sensitivity of 11 multispectral vegetation indices (VIs) designed to monitor these three stress-related leaf traits across a broad range of environmental and vegetation conditions.
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.
Radiometric infrared (IR) imaging is a valuable technique for remote-sensing applications in precision agriculture, such as irrigation monitoring, crop health assessment, and yield estimation. Low-cost uncooled non-radiometric IR cameras offer new implementations in agricultural monitoring. However, these cameras have inherent drawbacks that limit their usability, such as low spatial resolution, spatially variant nonuniformity, and lack of radiometric calibration. In this article, we present an end-to-end pipeline for temperature estimation and super resolution of frames captured by a low-cost uncooled IR camera. The pipeline consists of two main components: a deep-learning-based temperature-estimation module, and a deep-learning-based super-resolution module. The temperature-estimation module learns to map the raw gray level IR images to radiometric-grade temperature maps while also correcting for nonuniformity. The super-resolution module uses a deep-learning network to enhance the spatial resolution of the IR images by scale factors of ×2 and ×4. We evaluated the performance of the pipeline on both simulated and real-world agricultural datasets composing of roughly 20,000 frames of various crops. For the simulated data, the results were on par with the real-world data with sub-degree accuracy — 0.54∘C mean absolute error (MAE) for ×2 scale factor, and 0.84∘C MAE for ×4 scale factor. For the real data, the proposed pipeline was compared to a high-end radiometric thermal camera, and achieved sub-degree accuracy — 0.81∘C MAE for ×2 scale factor, and 0.81∘C MAE for ×4 scale factor. The results of the real data are on par with the simulated data. We show that our pipeline can compete with high-end thermal cameras in terms of quality and accuracy of the temperature and crop water stress index (CWSI) estimations using affordable hardware, with errors of 1.42% for ×2 and 1.86% for ×4 between the ground truth and the estimated CWSI. The runtime of the pipeline is less than 1sec per frame on a CPU, allowing it to run at video rates. The proposed pipeline can enable various applications in precision agriculture that require high quality thermal information from low-cost IR cameras.
Why it matches plant phenotyping methods低コスト赤外線カメラから植物温度と作物水ストレス指数を推定する深層学習パイプラインを開発し、実データ・シミュレーションおよび高性能熱画像カメラとの比較で精度を検証しており、植物フェノタイピング手法が中心である。
abstractIn this article, we present an end-to-end pipeline for temperature estimation and super resolution of frames captured by a low-cost uncooled IR camera.
Accurate monitoring of cotton water status is crucial for optimizing irrigation management and improving water-use efficiency in precision agriculture. UAV-based remote sensing offers high-resolution, flexible, and efficient data acquisition for agricultural monitoring, presenting significant potential for assessing crop water stress. However, existing approaches often treat spectral and texture features separately, overlooking their complementary nature across resolutions. This increases model complexity and reduces generalizability across phenological stages. To address these limitations, we propose a Dual-Cycle Cognitive Learning (DCCL) framework that integrates multi-resolution vegetation indices and texture features through a two-stage interpretable training pipeline. In the first stage, all extracted features are fed into a random forest model, and their contributions are quantified using SHapley Additive exPlanations (SHAP). The top 20 SHAP-ranked features are further refined using Recursive Feature Elimination (RFE) to select the 10 most informative features. These features are reintroduced into a pretrained model to form a distilled final monitoring model in the second stage, enhancing interpretability and cross-scale monitoring accuracy. Knowledge distillation further facilitates feature integration across different resolutions and growth stages, eliminating the need for manual feature engineering. Experiments conducted on real UAV datasets demonstrate the effectiveness of the proposed DCCL framework. It achieved a training R² of 0.9577 and an RMSE of 0.0026, while maintaining a cross-validation R² of 0.6514 on unseen datasets. In contrast, the baseline random forest model yielded a training R² of 0.9484 but a considerably lower cross-validation R² of 0.4268. These results confirm the improved robustness and generalizability of our approach under real-world field conditions. The DCCL framework offers a scalable, interpretable, and high-precision solution for UAV-based cotton water status monitoring, with significant potential to support sustainable irrigation strategies and intelligent crop management in large-scale agricultural systems.
Why it matches plant phenotyping methodsUAV画像の植生指数・テクスチャからワタの水分状態を推定する解釈可能な学習手法を開発し、実データで性能検証しているため、植物フェノタイピング手法が中心である。
abstractwe propose a Dual-Cycle Cognitive Learning (DCCL) framework that integrates multi-resolution vegetation indices and texture features through a two-stage interpretable training pipeline.
ABSTRACT As an essential species across European forests, Scots pine ( Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time‐consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single‐nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid‐to‐total‐chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water‐related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress‐related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype‐by‐environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate‐adaptive breeding in Scots pine.
Why it matches plant phenotyping methods針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。
abstracthyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code, Dataset · publicThe data supporting the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.27134907.v2 .Open asset ↗Figshare · 10.6084/m9.figshare.27134907.v2lines:454-598Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Water and nutrient availability are crucial factors influencing crop yield. However, the extent of their respective impacts on yield and the potential of remote sensing to clarify these effects remain insufficiently understood. This study explores the relative importance of satellite-derived crop water status (CWS) and leaf chlorophyll concentration (LCC) in determining crop yield production at the field scale. To address this question, we introduce a newly parametrized LUE model for winter wheat yield estimation. It leverages ET a and subsequently CWS from OPTRAM-ET, plus LCC from PROSAIL, to drive yield estimates. The LUE model was calibrated using big-plot field experimental data collected in 2021 and 2022 and was further validated on large areas across 125 farm fields from 2017 to 2021 in South Germany and Switzerland. Results showed that, under various nitrogen fertilization treatments in a region such as Germany with relatively favourable water availability, LCC showed a more dominant role in yield determination and was more sensitive to nitrogen availability than was CWS. Although the interplay between CWS and LCC was important, even slight improvements in the accuracy of LCC measurements considerably enhanced the precision of winter wheat yield estimates. Yield estimation using the LUE model had a high accuracy, with R 2 of 0.89 and RMSE of 0.74 t/ha in the big-plot experiments. Subsequently, the model was validated in large fields in Germany and Switzerland. While the direct impact of CWS on yield was less pronounced, its derivation from optical data provided superior temporal resolution compared with thermal images, which further refined yield predictions by increasing R 2 from 0.21 to 0.56 on the TUM fields and from 0.33 to 0.56 on the SWTZ fields, while decreasing RMSE from 1.22 to 0.91 t ha⁻¹ and from 1.50 to 1.22 t ha⁻¹ , respectively. These findings highlight the importance of taking into account both the CWS and LCC, as well as their derivation methods, in predicting crop yield, presenting a scientifically robust approach to spatially explicit yield estimation under varying nitrogen availability conditions.
Why it matches plant phenotyping methods衛星リモートセンシングから作物水分状態・葉クロロフィル濃度を導出し、LUEモデルで収量を推定する手法を新規パラメータ化・較正・広域検証しており、植物形質取得と推定手法が研究の中心である。
abstractwe introduce a newly parametrized LUE model for winter wheat yield estimation.
Field / plotLeafStress / disease detectionStress response / toleranceWater status / transpiration
Plants are exposed to atmospheric conditions for extended periods, allowing the observation of their color, texture, and internal structure to infer the surrounding atmospheric conditions. High ozone concentrations, particularly in urban areas, are harmful to plants. Optical coherence tomography (OCT) is a non-destructive method to evaluate samples' internal structure and optical properties, allowing potential measurements without any influence on the sample. This study aimed to estimate the ozone damage on plant leaves by measuring the OCT of white clover (Trifolium repens), an indicator plant of ozone influence, collected from different areas. Initially, to compare the influence of ozone and the impact of transportation from sampling regions, the temporal changes in water transpiration from cutting leaves were evaluated. Next, the leaves of white clover collected from various regions were measured using OCT. Changes on OCT images in light intensity, layer thickness, and texture (contrast, correlation, energy) within the leaves were analyzed to compare the presence or absence of the stresses (ozone and water stresses). The first experiment confirmed that the trends in Energy changes in the OCT images were opposite for ozone and water stress, with ozone increasing Energy due to widespread tissue disruption that blurred texture patterns, and water stress decreasing it due to localized shrinkage. In the second experiment, similar to the first experiment, a decrease in the intensity of the palisade tissue, an increase in thickness, and an increase in Energy were observed in regions with particularly high ozone concentrations. These results confirmed that OCT image analysis could detect specific plant changes due to ozone and water stress. This study demonstrates the potential of in-situ measurements of plants using OCT to infer the environmental conditions to which the plants are exposed.
Why it matches plant phenotyping methodsOCT画像から葉の内部構造・テクスチャ・組織厚などの植物状態を抽出し、オゾンおよび水ストレスを検出する手法が研究の中心であるため。
abstractThese results confirmed that OCT image analysis could detect specific plant changes due to ozone and water stress.
Abstract. Frame-based VNIR/SWIR multispectral sensors on UAVs offer promising capabilities for precision agriculture by enabling the easy simultaneous acquisition of spectral and structural crop information. This study provides an independent validation of a two-band VNIR/SWIR sensor system for monitoring winter wheat traits and compares the results with previous findings. The UAV flights were conducted on a single date (May 11, 2022), capturing image datasets at wavelengths of 910, 980, 1100, 1200, 1510, and 1650 nm. Structure from Motion (SfM) processing enabled crop height extraction from the same multispectral datasets. Ground-truth data included fresh and dry biomass, moisture, nitrogen concentration, and nitrogen uptake from 36 samples across six varieties and three fertilization levels. Bivariate regression analyses revealed moderate performance for spectral vegetation indices (NRI: R2=0.52–0.61; GnyLi: R2=0.50–0.62), which was lower than that previously reported. Crop height showed a superior predictive capability (R2=0.63–0.75), demonstrating consistency across studies. Multivariate models combining vegetation indices with crop height significantly improved trait estimation (R2=0.72–0.84, nRMSE=0.12–0.15), confirming that integrated spectral-structural approaches provide robust performance even when individual predictors show limitations. While this single-date analysis limits conclusions about temporal stability throughout the growing season, it provides valuable validation of the capabilities of the sensor system. The ability to derive both structural and biochemical data from single-sensor imagery is the key advantage of this camera system. Future research should expand to multi-temporal analyses across complete growing seasons and implement the recently developed 6-channel VNIR/SWIR system to address the current limitations. This study reinforces the fact that combining SWIR spectral features with structural parameters is essential for reliable estimation of crop traits.
Why it matches plant phenotyping methodsUAV搭載VNIR/SWIRセンサーによる作物形質推定を中心に、センサーシステムの独立検証と構造・生化学形質の抽出性能を評価しているため。
abstractThis study provides an independent validation of a two-band VNIR/SWIR sensor system for monitoring winter wheat traits and compares the results with previous findings.
Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture.
Why it matches plant phenotyping methods植物の生理状態を非侵襲的に取得するマルチモーダル・マルチスケール生体電気センサー基盤を開発し、果実・葉・茎・根の評価と基準指標による検証を行っており、表現型取得法が研究の中心です。
abstractThis study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment
Lysimeters are essential for quantifying soil water content and evapotranspiration, but their high cost limits widespread adoption. This study developed a cost effective, IoT-enabled weighing lysimeter to measure crop evapotranspiration (ET c ) in shallow-rooted crops and enhance sustainable irrigation management. The system, with a 1.38 m² surface area and a one-ton single-point load cell, integrated soil moisture and temperature sensors at three depths and a waterproof ultrasonic sensor for drainage measurement. Data were stored locally on an SD card and transmitted to the cloud via the ThingSpeak IoT platform for real-time monitoring. Field validation with wheat during the 2022-23 winter season recorded a total ET c of 331.9 mm with high accuracy ( R ² = 0.998) and a resolution of 0.20 mm. The total cost of construction was approximately USD 709, making it a highly cost-effective and practical alternative to conventional lysimeter systems. The developed system enables affordable, accurate, and continuous water monitoring, supporting efficient irrigation scheduling and sustainable water resource management.
Why it matches plant phenotyping methods作物蒸発散量を連続測定するIoT対応ライシメータを開発し、圃場で精度検証しており、植物の水利用状態の取得方法が研究の中心である。
abstractThis study developed a cost effective, IoT-enabled weighing lysimeter to measure crop evapotranspiration (ET c ) in shallow-rooted crops
Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.
Why it matches plant phenotyping methodsUAV熱画像とSfMによる3D熱画像から、樹冠温度・水ストレスなどのオリーブ樹の状態を抽出する手法が中心であり、2D手法との比較を含む実質的なフェノタイピング手法研究である。
abstractThis study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture.
CucumberGreenhouseRGB / grayscaleFruitPhysiological trait estimationWater status / transpiration
Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R2 up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R2 = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain.
Why it matches plant phenotyping methodsキュウリ果実の相対含水量という植物状態を、可視光画像と機械学習で非破壊推定する手法を開発しており、表現型取得・推定が研究の中心である。
abstractWe developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest).
The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R2 values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm−2, respectively. The model maintained robust performance in testing (R2 = 0.893 for CWC, and R2 = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いて冬コムギの作物含水量・等価含水厚を推定し、モデル性能を比較検証する手法研究であり、植物水分状態の取得・抽出が中心です。
abstractFour machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT
Field / plotRaman / spectroscopyLeafPhysiological trait estimationStress response / toleranceWater status / transpiration
Monitoring plant water status is vital for optimizing irrigation in precision agriculture. This study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues. This approach is well-suited for the realization of large networks of distributed sensors wirelessly connected to a central hub. An outdoor experiment was conducted over two phases of 20 day-experiment involving six Hydrangea macrophylla plants subjected to two irrigation treatments: a control group (well-irrigated) and a test group (poorly irrigated) designed to induce water stress. The standard relative water content (RWC) method validated the treatment effects on the plants, and both EIS and IR sensors effectively distinguished between the two groups. Impedance-derived parameters, particularly the normalized intracellular resistance (R0) and the cell membrane capacitance (C0), exhibited statistically significant differences between the treatments. In addition, the IR measurements showed moderate correlations with RWC, with determination coefficients of R2 = 0.56 and R2 = 0.51 for first and second phases of the experiment, respectively. Despite some limitations concerning the electrode–leaf conformity and external sunlight interference, the results point to the advantages of these methods for real-time plant monitoring and decision-making in smart irrigation systems.
Why it matches plant phenotyping methods植物の水分状態という生理形質を対象に、EISおよび赤外分光センサーによる非侵襲的測定法を開発・検証しており、センサー手法が研究の中心である。
abstractThis study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues.
Addressing crop responses to drought and nitrogen stress is crucial for improving resilience and ensuring sustainable agriculture under changing climatic conditions. This study investigates the physiological responses of wheat (Videodur [DU], Sensas [SW]) and barley (Tiroler Imperial [SG1], Amidala [SG2]) cultivars to drought and nitrogen stress during early reproductive to full maturity stages (BBCH 70 to 90) using infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging. Evapotranspiration (ET) and respiration were analyzed as functions of mean plant temperature (Tplant), light intensity, plant water status (indicated by the Normalized Difference Water Index, NDWI), and air humidity. Results revealed that drought stress significantly reduced NDWI and ET while increasing Tplant, with wheat cultivars showing greater sensitivity to water deficit. Barley, particularly SG2, exhibited superior water retention and thermal regulation, highlighting its potential for drought resilience with consistently higher NDWI values and lower Tplant. Temporal analysis identified the reproductive stage as the most vulnerable to stress, with a sharp decline in NDWI and rise in Tplant, emphasizing the need for stage-specific interventions. Regression models explained 74% of ET variance and 67% of respiration variance, underscoring the predictive power of NDWI and Tplant as proxies for plant water status and metabolic activity. Real-time evapotranspiration (ET) measurements using a balance during precision watering further validated the predictive capabilities of NDWI and Tplant. These findings provide valuable insights into growth stage-specific breeding programs and sustainable crop management strategies under environmental stress conditions.
Why it matches plant phenotyping methods赤外・ハイパースペクトル画像からNDWI、植物温度、蒸発散量、呼吸を推定し、回帰モデルと実測バランスで検証しており、植物ストレス形質の取得・推定ワークフローが中心的である。
abstractusing infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging
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 · UnverifiedCrossref · checked 14 Sept 2026
Climate change and evolving land management practices are reshaping soil–plant interactions critical for sustainable viticulture. These interactions are driven by soil texture, hydrogeochemical gradients, and climatic conditions, influencing grapevine traits like nutrient and water content. Integrating innovative methods, this study explores the relationship between soil variability and grapevine characteristics in the Médoc wine region, France. The research combines hyperspectral imaging, electromagnetic induction (EMI), and electrical resistivity tomography (ERT) with traditional soil and leaf sampling. Hyperspectral data, using visible-near infrared (VNIR) wavelengths, reliably estimated leaf traits such as nitrogen and water content, yielding strong predictive relationships (R2 up to 0.8). These findings suggest VNIR-based indices are cost-effective for monitoring grapevine physiology. Geophysical data revealed significant soil textural gradients, delineating sand, transitional (loam, sandy loam), and clay textural soil classes. Apparent electrical conductivity (ECa) and inverted electrical conductivity (EC) correlated with soil texture and grapevine traits, particularly at depths around 50 cm, aligning with primary root zones. However, interannual variability in correlations emphasised the influence of weather conditions and phenological stages, highlighting the need to align data acquisition with vine growth phases. The integration of hyperspectral imaging and geophysical methods provides a novel framework for linking soil and plant parameters. This interdisciplinary approach enhances the spatial resolution and scalability of vineyard monitoring, offering actionable insights for precision viticulture. Future work should expand datasets and refine predictive models to improve the understanding of soil–plant dynamics under changing environmental conditions. These findings underscore the potential of combining hyperspectral and geophysical data to develop climate-resilient vineyard management strategies, advancing precision agriculture, and sustainable viticulture practices.
Why it matches plant phenotyping methodsハイパースペクトル画像によりブドウ葉の窒素・水分などの植物形質を推定し、予測性能を評価している。土壌調査も含むが、植物形質の取得・推定手法が主要な技術的貢献である。
abstractHyperspectral data, using visible-near infrared (VNIR) wavelengths, reliably estimated leaf traits such as nitrogen and water content, yielding strong predictive relationships (R2 up to 0.8).
Visual estimates of plant symptoms are traditionally used to quantify disease severity. Yet, the methodologies used to assess these phenotypes are often subjective and do not allow tracking of disease progression from very early stages. Here, we hypothesized that quantitative analysis of whole-plant physiological vital functions can be used to objectively determine plant health, providing a more sensitive way to detect disease. We studied the tomato wilt that is caused by Fusarium oxysporum f. sp. lycopersici . Physiological performance of infected and noninfected tomato plants was compared using a whole-plant pot-based lysimeter functional phenotyping system in a semi-environmentally controlled greenhouse. Water-balance traits of the plants were measured continuously and simultaneously in a quantitative manner. Infected plants exhibited early reductions in transpiration and biomass gain, which preceded visual disease symptoms. These changes in transpiration proved to be effective quantitative indicators for assessing both plant susceptibility to infection and virulence of the fungus. Physiological changes linked to fungal outgrowth and toxin release contributed to reduced hydraulic conductance during initial infection stages. The functional phenotyping method objectively captures early-stage disease progression, advancing plant disease research and management. This approach emphasizes the potential of quantitative whole-plant physiological analysis over traditional visual estimates for understanding and detecting plant diseases.
Why it matches plant phenotyping methods全植物の水収支を連続定量する機能的フェノタイピング法を用い、Fusarium萎凋病の早期進行と感受性を客観評価する手法が中心である。
abstractusing a whole-plant pot-based lysimeter functional phenotyping system
Field / plotMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Abstract There is an urgent need for effective large-scale biodiversity monitoring across ecosystems, given the recent tendency toward global biodiversity loss. The assessment of plant spectral diversity offers a promising approach as it is intrinsically linked to phylogenetic and functional diversity. This study investigates the relationship between taxonomic, functional, and spectral diversity in temperate saltmarsh and seagrass ecosystems in the Gulf of Biscay. Using hyperspectral leaf reflectance data and functional traits from 19 plant species across four estuaries, these three dimensions of biodiversity were compared. The predictive power of spectral data was assessed for estimating biochemical and anatomical traits and species identification. Results reveal significant correlations between functional and spectral diversity, with species sharing similar functional traits exhibiting similar spectral signatures. Spectral diversity is significantly influenced by taxonomic classification, with higher taxonomic levels (e.g., order, class) explaining substantial part of the spectral variation. Spectral regions of 720–770 nm and 1330–1380 nm were important for species discrimination, achieving 98% accuracy. Partial least squares regression models successfully estimated functional traits (e.g., water content, carbon, phosphorus) with high precision in these environments. These findings demonstrate that spectral data can effectively capture taxonomic and functional diversity, offering an effective tool for large-scale biodiversity monitoring in estuarine ecosystems. This study underscores the potential of remote sensing to track biodiversity and ecosystem health, providing a foundation for future applications in conservation and management.
Why it matches plant phenotyping methods葉のハイパースペクトル反射データから機能形質を推定し、スペクトル手法の予測性能も評価しており、植物フェノタイピング手法が中心である。
abstractThe predictive power of spectral data was assessed for estimating biochemical and anatomical traits and species identification.
Reproduction assets foundThe authors explicitly state that the dataset produced and used by this work (hyperspectral leaf reflectance and functional trait measurements from 19 estuarine plant species) is available open-access via the IHCantabria DIES API. No author analysis code or trained models are mentioned.Dataset · publicThe dataset produced and used by this work is available open-access through the link https://apidies.ihcantabria.com/swagger/index.htmlOpen asset ↗apidies.ihcantabria.comlines:288-303Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 6 Sept 2026
ArabidopsisLaboratory / benchtopMicroscopyCell / cellular structureRootPhysiological trait estimationGrowth / development / phenologyWater status / transpirationYield / yield components
Tip growth is a tightly regulated process that enables root hairs to explore their surroundings, enhancing plant development, particularly by improving nutrient uptake. While Lockhart's viscoplastic framework is widely used to describe this process, it has received limited experimental validation. By integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force. The observed growth rate reduction aligns remarkably with a 1D Lockhart viscoplastic model, experimentally validating this framework in tip-growing cells. Additionally, the instantaneous compression upon force application provided an in situ estimate of turgor pressure. Together, these measurements allowed us to determine, for the first time in Arabidopsis root hairs, two critical parameters: the yield turgor pressure and cell wall viscosity. Our approach, including the technique, protocol, and analytical framework, can be readily adapted to other tip-growing species and diverse experimental conditions (e.g., varying nutrient availability or osmotic stress). This opens new opportunities to explore cell wall mechanosensitivity and its role in adapting tip growth to environmental signals.
Why it matches plant phenotyping methods個々の根毛の成長速度・圧縮・膨圧を測定する新規手法と解析枠組みが研究の中心であり、植物形質の取得とモデル検証を実施している。
abstractBy integrating optical microscopy with a custom microplate-based rheometer, we created a novel protocol to simultaneously measure, for individual growing root hairs, both the reduction in growth rate and the instantaneous compression in response to a step in applied axial force.
Estimating stomatal conductance poses significant challenges in plant stress research, since traditional measurement methods interact physically with leaves, thereby altering their position and microclimate. To overcome this problem, we developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately. We evaluated this method by comparing model estimated total plant transpiration with gravimetric measurements. The estimates provided by our approach corresponded favorably with measurements across different environmental conditions, including non-stressed and short-term salinity stress scenarios. This method effectively tracks stomatal responses to rapid osmotic stress, offering a reliable tool for remote assessment of plant physiological dynamics.
Why it matches plant phenotyping methods3D画像・熱画像・エネルギー収支モデルを組み合わせ、植物の気孔コンダクタンスを非接触推定する手法の開発と検証が中心である。
abstractwe developed a contactless approach that combines 3D modeling, thermal imaging, and a modified energy balance equation to estimate stomatal conductance remotely and accurately.
Sweet potato (Ipomoea batatas L.) exhibits strong resilience in nutrient-poor soils and contains high levels of dietary fiber and antioxidant compounds. It also is highly tolerant to water stress, which has also contributed to its global distribution, particularly in regions prone to climatic variability. However, frequent abnormal climatic events have recently caused declines in both the quality and yield of sweet potatoes. To address this, machine learning (ML) and deep learning (DL) models based on a Vision Transformer-Convolutional Neural Network (ViT-CNN) were developed to classify water stress levels in sweet potato. RGB-thermal imagery captured from low-altitude platforms and various growth indicators were used to develop the classifier. The K-Nearest Neighbors (KNN) model outperformed other ML models in classifying water stress levels at all growth stages. The DL model simplified the original five-level water stress classification into three levels. This enhanced its sensitivity to extreme stress conditions, improve model performance, and increased its applicability to practical agricultural management strategies. To enhance practical applicability under open-field conditions, several environmental variables were newly defined to calculate the crop water stress index (CWSI). Furthermore, an integrated system was developed using gradient-weighted class activation mapping (Grad-CAM), explainable artificial intelligence (XAI), and a graphical user interface (GUI) to support intuitive interpretation and actionable decision-making. The system will be expanded into an online and fixed-camera platform to enhance its applicability to smart farming in diverse field crops.
Why it matches plant phenotyping methodsRGB・熱画像と生育指標を用いてサツマイモの水ストレス状態を分類するモデルを開発し、CWSI、XAI、GUIを統合したシステムを構築しており、植物状態の取得・推定手法が研究の中心である。
abstractmachine learning (ML) and deep learning (DL) models based on a Vision Transformer-Convolutional Neural Network (ViT-CNN) were developed to classify water stress levels in sweet potato.
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.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT We conducted research to predict daily transpiration in crops by utilising a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors. Our aim was to improve the accuracy of transpiration estimates. Data were collected from hundreds of plant specimens growing in two semi‐controlled greenhouses over 7 years, automatically measuring key physiological traits (serving as our ground truth data) and meteorological variables with high temporal resolution and accuracy. We trained Decision Tree, Random Forest, XGBoost and Neural Network models on this data set to predict daily transpiration. The Random Forest and XGBoost models demonstrated high accuracy in predicting the whole plant transpiration, with R 2 values of 0.89 on the test set (cross‐validation) and R 2 = 0.82 on holdout experiments. Ambient temperature was identified as the most influential environmental factor affecting transpiration. Our results emphasise the potential of ML for precise water management in agriculture, and simplify some of the complex and dynamic environmental forces that shape transpiration.
Why it matches plant phenotyping methods植物の個体蒸散量を荷重セル・環境センサーと機械学習で推定し、複数モデルの精度検証を行うことが研究の中心であるため。
abstractWe conducted research to predict daily transpiration in crops by utilising a combination of machine learning (ML) models combined with extensive transpiration data from gravimetric load cells and ambient sensors.
Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (Kc) is a dimensionless factor that converts reference evapotranspiration (ET0) into actual crop evapotranspiration (ETc) and is widely used for irrigation scheduling. The Kc reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m2) relative to vine canopy size (~2 m2). This study aimed to improve grapevine Kc predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the Kc prediction models. Unmixing improved the prediction of seasonal Kc values in Shiraz (R2 of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R2 = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R2 = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of Kc values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture.
Why it matches plant phenotyping methodsSentinel-2のスペクトルアンミキシングと回帰モデルにより、ブドウ樹の樹冠被覆・季節的Kcを推定し、品種間・地域間で精度検証している。水管理応用を含むが、植物状態の取得・推定手法が中心的である。
abstractThis study aimed to improve grapevine Kc predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing
The rising prevalence of Phytophthora diseases in forests highlights the need for rapid, non-invasive detection methods. Early-stage root infections are difficult to detect due to the absence of visible above-ground symptoms, while current diagnostics remain slow and invasive. This study investigated whether hyperspectral leaf reflectance could detect root rot caused by Phytophthora alticola in Eucalyptus benthamii. Nineteen commercially planted families were inoculated, and leaf spectra were collected using an ASD FieldSpec 4 sensor. A machine learning pipeline was developed to identify diagnostic spectral signals. Key wavelengths were identified using permutation importance, a genetic algorithm, and self-attention network (SAN) scores. Spectral signals linked to root rot revealed that infection was correlated with leaf pigment accumulation and moisture stress. Three algorithms, random forest (RF), support vector machine (SVM), and SAN, were trained on hyperspectral data to predict P. alticola infection. The SAN achieved 97 % accuracy on a reduced dataset, which included the diagnostic wavelengths from the feature selection step, surpassing the RF (96 %) and SVM (94 %) models. This study demonstrates hyperspectral sensing as an effective tool for detecting Phytophthora root rot using spectra from the foliage and highlights the application of advanced machine learning techniques for plant disease classification.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から根腐病という植物の病態を推定するセンシングと機械学習パイプラインが研究の中心であり、特徴選択と分類性能も評価しているため。
abstractA machine learning pipeline was developed to identify diagnostic spectral signals.
Accurate estimation of comprehensive traits such as yield and quality is crucial for optimizing agricultural management practices across the tomato industry chain. Traditional manual methods are time-consuming, labor-intensive, and prone to errors, reducing estimation accuracy. In contrast, modern intelligent estimation approaches based on multi-temporal spatial and spectral feature fusion offer improved efficiency and accuracy but still face challenges such as non-generalizable segmentation models, asynchronous feature extraction and weak correlations. This study proposes a novel pipeline for estimating yield and quality of greenhouse tomatoes using temporal semantic multispectral (TSM) point clouds. An unsupervised deep learning model was designed to register RGB-D images and multispectral (MS) images collected by an unmanned ground vehicle (UGV) plant phenotyping platform. The digital number (DN) point clouds of tomato organs were reconstructed based on the masks predicted by SegFormer with fusion of multispectral and depth modalities (MSD-SF). These point clouds were then radiometrically calibrated using neural reference field with sparse viewpoints (NeREF-S) to generate accurate reflectance point clouds. Finally, multi-temporal spatial-spectral features of tomatoes were extracted from the TSM point clouds, and random forest regression models were developed to estimate traits such as fruit flavor preference, water content, brix, acidity, brix-to-acid ratio, vitamin C content, single-fruit mass, and single-plant yield. The image registration model achieved high accuracy on the test set, with average structural similarity index measure, peak signal-to-noise ratio and learned perceptual image patch similarity of 0.238, 13.116 dB, and 0.374, respectively. The MS point clouds calibrated by NeREF-S significantly improved the signal-to-noise ratio to 11.56 dB. The average rRMSE for all trait estimations was 9.03 %. The results indicate that the proposed estimation method is efficient and accurate, holding promise to become a new paradigm for estimating the comprehensive traits of greenhouse tomatoes.
Why it matches plant phenotyping methods温室トマトの収量・品質形質を推定するため、UGVフェノタイピングプラットフォーム、マルチスペクトル点群生成、画像登録・放射較正、特徴抽出および回帰推定パイプラインを中心的に開発・評価している。
abstractThis study proposes a novel pipeline for estimating yield and quality of greenhouse tomatoes using temporal semantic multispectral (TSM) point clouds.
Accurate estimation of total leaf area (TLA) is essential for assessing plant growth, photosynthetic activity, and transpiration, but remains a challenge for bushy plants like dwarf tomatoes. Traditional destructive methods and imaging-based techniques often fall short due to labor intensity, plant damage, or the inability to capture complex canopies. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars—Mohamed, Hahms Gelbe Topftomate, and Red Robin—grown under controlled greenhouse conditions. Two experiments, conducted in spring–summer and autumn–winter, included 73 plants, yielding 418 TLA measurements using an “onion” approach, where layers of leaves were sequentially removed and scanned. High-resolution videos were recorded from multiple angles for each plant, and 500 frames were extracted per plant for 3D reconstruction. Point clouds were created and processed, four reconstruction algorithms (Alpha Shape, Marching Cubes, Poisson’s, and Ball Pivoting) were tested, and meshes were evaluated using seven regression models: Multivariable Linear Regression (MLR), Lasso Regression (Lasso), Ridge Regression (Ridge-Reg), Elastic Net Regression (ENR), Random Forest (RF), extreme gradient boosting (XGBoost), and Multilayer Perceptron (MLP). The Alpha Shape reconstruction (α = 3) combined with XGBoost yielded the best performance, achieving an R² of 0.80 and MAE of 489 cm², with significant results across other model combinations. Results were lower when using data from different experiments as train and test datasets (R² = 0.56 and MAE = 579 cm²). Feature importance analysis identified height, width, and surface area as the most predictive features. These findings demonstrate the robustness of our approach across variable environmental conditions and canopy structures. This scalable, automated TLA estimation method is particularly suited for urban farming and precision agriculture, offering practical implications for automated pruning, improved resource efficiency, and sustainable food production.
Why it matches plant phenotyping methodsRGB画像からの3D再構成と機械学習により植物の総葉面積を推定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractThis study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA
Rapid, accurate, and non-destructive estimation of crop water use efficiency (WUE) at the field scale is crucial not only for evaluating water efficient cultivars and practices in scientific research but also for optimizing irrigation schedule in agricultural production. The current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity. This study aimed to utilize unmanned aerial vehicle (UAV) multisensory remote sensing data combined with a process model to achieve rapid WUE determination via accurate daily-scale evapotranspiration and aboveground biomass (AGB) estimates. First, vegetation indices, canopy temperature, and canopy structural parameters were extracted from multispectral (MS), thermal imaging (TIR), and radar data and combined with an automated machine learning (AutoML) for AGB estimation. The beta function was then employed to accurately estimate AGB accumulation at a daily step (AGBdₐᵢₗy) over the entire growth period. The daily evapotranspiration (ETdₐᵢₗy) was calculated by the surface energy balance algorithm for land (SEBAL) model driven by MS, TIR, and meteorological data. Finally, the WUE was determined by the ratio of AGBdₐᵢₗy to ETdₐᵢₗy. Multisensory data fusion and further integration with process-based model proved effective for simultaneously estimating AGBdₐᵢₗy, ETdₐᵢₗy, and WUE with R² values of 0.71, 0.93, and 0.79, respectively. Notably, the proposed WUE estimation method can capture different temporal pattern between cultivars with different levels of tolerance to drought. We applied this approach to screen water efficient cultivars and found that appropriate reduction of irrigation can improve WUE. In conclusion, this study shows promising perspective in the use of a UAV-based approach integrating multisensory data with SEBAL evapotranspiration modeling for monitoring and evaluating water consumption and utilization in maize.
Why it matches plant phenotyping methodsUAVマルチセンサーデータとモデルを統合し、トウモロコシのAGB、蒸発散、WUEという植物形質・状態を推定する方法を開発・評価しており、表現型取得が研究の中心である。
abstractThe current lack of efficient methods for high-throughput phenotyping WUE hinders development of sustainable agriculture under globally intensified water scarcity.
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.
Increasing combined heat and drought extremes due to climate change heighten the risk of crop failure, underscoring the need for improved stress diagnosis for effective management strategies. However, current plant physiology indicators struggle to differentiate crop stresses in hot-dry environments. This study proposes using specific leaf metabolites, detectable by leaf reflectance spectra, for more precise identification of heat and drought stress compared to traditional methods. We conducted two rounds of one-week drought treatments under heat stress on soybean seedlings. Throughout the experiment, we monitored stomatal conductance, reflectance spectra, and metabolites, including Abscisic Acid (ABA), Jasmonic Acid (JA), Salicylic Acid (SA), and proline (Pro), on a daily basis. Our findings revealed that ABA and JA exhibited differential sensitivities to drought and heat stress, respectively. In contrast, stomatal conductance was unable to differentiate between the two stressors. Using partial least-squares regression (PLSR), we determined that both ABA and JA could be detected via leaf spectroscopy with moderate predictive performance (R² = 0.53, relative RMSE = 14.28 %; R² = 0.53, relative RMSE = 14.96 %) and exhibited distinct sensitive spectral signatures. The metabolite-derived, stress-specific spectral models enable more precise and earlier diagnosis and differentiation of stress in a hot-dry environment than traditional physiological indicators (e.g., relying on stomatal conductance). This study provides an example of using metabolites as novel stress indicators, which could contribute to precision agriculture, offering the potential for accurate, stress-specific, and pre-physiological detection of crop health.
Why it matches plant phenotyping methods葉の反射スペクトルとPLSRにより、熱・ drought stressに関連する代謝物を推定し、植物ストレス状態を識別する手法の開発・検証が研究の中心である。
abstractThis study proposes using specific leaf metabolites, detectable by leaf reflectance spectra, for more precise identification of heat and drought stress compared to traditional methods.
PotatoRaman / spectroscopyPhysiological trait estimationWater status / transpiration
Near infrared spectroscopy (NIRS) has been widely used as a nondestructive testing technique and plays a crucial role in the quality inspection of agricultural products. However, the variability between different batches of samples hinders the application of commercial NIRS processes. Therefore, model transfer is usually performed on new samples to enhance the generalizability of the device. In this study, a new algorithm was developed based on the slope and bias correction algorithm (SBC) for model transfer between two different batches of samples, using potatoes of different origins as experimental samples for prediction models of potato quality, and based on this algorithm, model transfer between two different batches of samples was successfully implemented in a self-made portable non-destructive potato detection device. The results showed that the device developed based on the new algorithm gives good results for subsamples prediction. In the dry matter model, the correlation coefficient (R), root mean square error (RMSE) and relative standard deviation (RSD) of the new algorithm optimized compared with the traditional SBC algorithm were improved from 0.7843, 1.2080% and 6.59% to 0.8251, 1.1307 and 6.17%, respectively; in the starch model, the new algorithm optimized R, RMSE and RPD improved from 0.7971, 1.0023% and 7.43% to 0.8176, 0.9570% and 7.31%, respectively, compared with the traditional SBC algorithm. The transfer of NIR correction models for the dry matter and starch content of potatoes was basically achieved, which provided technical and theoretical support to enhance the model universality of convenient nondestructive detection devices.
Why it matches plant phenotyping methodsジャガイモの乾物・デンプン含量という植物器官形質を非破壊NIRで測定する携帯型デバイスと、バッチ間モデル移 transferアルゴリズムを開発・評価しており、表現型取得法が中心である。
abstracta new algorithm was developed based on the slope and bias correction algorithm (SBC) for model transfer between two different batches of samples
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.
Terahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively, for example over the course of the day or at the onset of drought stress. By studying a wildtype and a genetically modified variant of Arabidopsis thaliana, we observed significant differences in their dehydration dynamics. For a better understanding of the underlying processes, we modelled this behaviour with a simple rate equation model, compared the results with the experimental data and correlated our model with the biological regulatory mechanisms. In particular, under drought stress, we found an almost three times ([Formula: see text]) higher maximal stomatal opening in the mutant than in the wildtype. Over the course of the day, the degree of stomatal opening shows an exponential decrease with a half-life [Formula: see text] of [Formula: see text]2.6 h in the wildtype and [Formula: see text]0.8 h in the mutant.
Why it matches plant phenotyping methodsTHz分光法による植物体内水分量と気孔開閉 dynamics の非破壊・経時的測定が研究の中心であり、水分状態・生理形質を定量化するフェノタイピング手法としてモデル検証も行っている。
abstractTerahertz (THz) time-domain spectroscopy allows the detection of temporal changes of plant water content in vivo and non-destructively
Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation and detect signatures of local adaptation across populations. We combined hyperspectral data, inverse modeling, and network analysis to investigate population-level variation in Streptanthus tortuosus. Using a common garden experiment with four geographically distinct populations, we applied partial least square discriminant analysis (PLS-DA) and ridge regression for population discrimination, inverse PROSPECT modeling to estimate leaf biochemical traits, and canonical correlation analysis to examine trait-climate relationships across historical (1900-1994) and recent (1995-2024) periods. We developed a spectral network approach treating wavelength correlations as biologically meaningful trait networks. Populations showed distinct, heritable spectral signatures with high classification accuracy. Significant population differences emerged in anthocyanins, carotenoids, chlorophyll, and water content. Trait-climate correlations shifted between time periods, consistent with historical climate adaptation. Network analysis revealed population-specific integration patterns, with more variable environments displaying greater spectral modularity. Hyperspectral signatures provide a high-throughput tool for detecting population-level adaptation and trait coordination. Our findings provide a framework to investigate how plant populations respond to climate change through evolved shifts in trait networks rather than isolated traits alone.
Why it matches plant phenotyping methodsハイパースペクトル計測と逆モデリングを用いて葉の機能形質を推定し、集団間比較・適応評価を行う手法が研究の中心であるため。
abstractHyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw hyperspectral data and source code in a public GitHub repository, which is an allowed URL.Code · publicRR, JL; Formal Analysis:
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RR, JNM, TSM; Funding acquisition: JRG, JNM, TSM; Investigation: RR, JNM, TSM;
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Raw data and source code are available in the following Github repository.
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References
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Albert R, Barabási A-L. 2002. Statistical mechanics of complex networks. Reviews of Modern
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ScieOpen asset ↗rishavray/spectral-networkpdf-raw-page:23 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Early and accurate detection of crop stress is vital for sustainable agriculture and food security. Traditional vegetation indices such as NDVI and NDWI often fail to detect early-stage water and structural stress due to their limited spectral sensitivity. Method This study introduces two novel hyperspectral indices - Machine Learning-Based Vegetation Index (MLVI) and Hyperspectral Vegetation Stress Index (H_VSI) - which leverage critical spectral bands in the Near-Infrared (NIR), Shortwave Infrared 1 (SWIR1), and Shortwave Infrared 2 (SWIR2) regions. These indices are optimized using Recursive Feature Elimination (RFE) and serve as inputs to a Convolutional Neural Network (CNN) model for stress classification. Results The proposed CNN model achieved a classification accuracy of 83.40%, effectively distinguishing six levels of crop stress severity. Compared to conventional indices, MLVI and H_VSI enable detection of stress 10-15 days earlier and exhibit a strong correlation with ground-truth stress markers (r = 0.98). Discussion This framework is suitable for deployment with UAVs, satellite platforms, and precision agriculture systems.
Why it matches plant phenotyping methods作物ストレス状態を推定するハイパースペクトル指標とCNNを開発し、重症度分類および地上真値との相関で評価しており、植物表現型取得・抽出法が中心である。
abstractThis study introduces two novel hyperspectral indices - Machine Learning-Based Vegetation Index (MLVI) and Hyperspectral Vegetation Stress Index (H_VSI)
This work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants, both indoor and outdoor. Ozone concentrations as low as 30 above ambient levels were detected via physiological responses, enabling the use of phytosensors as biodetectors of environmental pollutants. exposure affects stomatal regulation that in turn alters the hydrodynamics of fluid transport system in plants. The measurement results indicate a reaction of hydrodynamic system to changes in concentration with a delay of 10-20 min between the onset of exposure and biological response. The probability of false-negative responses from a plant is 0.15 ± 0.06. Pooling data from at least three plants allows for 92% confidence in detecting excess . Measurements on days with low and high ozone levels of 80 to 130 result in a 2.33-fold difference in sensor readings at these levels, underscoring the sensitivity of the method. Statistical robustness is supported by 948 plant-sensor measurements with 9 plants over 51 days, totaling 10 7 samples via automated monitoring. Long-term field tests demonstrate the reliability of electrochemical methods. This approach has applications in environmental monitoring, biological pollution detection and biosensing.
Why it matches plant phenotyping methods植物組織の電気化学インピーダンスからオゾン曝露に対する生理応答を検出するセンサー手法を開発・検証しており、植物状態の取得方法が研究の中心である。
abstractThis work demonstrates the biological detection of low-level by measuring electrochemical impedances of stem tissues in tobacco and tomato plants
Climate change-induced erratic weather patterns necessitate the development of cold-tolerant marigold cultivars for sustainable floricultural production. The present study was carried out during winter (rabi) season 2021–22 and 2022–23 at ICAR-Indian Agricultural Research Institute, New Delhi to evaluate the efficacy of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, for quantitative assessment of essential plant traits such as plant area, greenness, water content, and temperature. Ten French marigold (Tagetes patula L.) genotypes (Pusa Deep, Pusa Arpita, Dainty Marietta, Valencia Yellow, Orange Winner, Hisar Beauty, Hisar Jafri, Gulzafri Orange, Fr./W-20, Fr./W-21) were evaluated. The experiment was laid out in a complete randomized design (CRD) with two factors (genotype and environment) and three replications, with 18 plants/environment and 6 plants/replication. Technologies were applied to assess cold tolerance during the early reproductive phase of French marigold genotypes, grown under contrasting environments: Controlled conditions (polyhouse, 30.1°-33.7°C/3.4°-3.7°C) and cold stress (open field, 26.4°-28°C/0.8°-1.2°C) during winter season. Comparative analysis revealed that cold stress significantly impacted morpho-physiological parameters: Plant area decreased by 1.38-fold, caliper length by 1.07-fold, and compactness by 2.10-fold compared to the polyhouse environment. Convex hull area and circumference were reduced by 1.22-fold and 1.05-fold, respectively. Additionally, greenness and plant temperature decreased by approximately 1.03-fold, roundness by 2.07-fold, and plant water content by 1.44-fold. Statistical analysis revealed that open field conditions significantly decreased all measured morpho-physiological parameters, with plant compactness showing the greatest reduction compared to controlled conditions. Notably, genotypes including ‘Hisar Beauty’ and ‘Hisar Jafri ’ exhibited superior cold tolerance, demonstrating the least reductions in measured parameters under cold stress, while maintaining higher water content (NIR reflectance, 140.98%) and lower plant surface temperatures (19.06°C) compared to other genotypes. These findings underscore the potential of non-destructive image-based phenotyping as an efficient tool in screening for cold tolerance in marigold breeding programmes, offering a viable and precise alternative to traditional screening methods for accelerated cultivar development.
Why it matches plant phenotyping methodsRGB・NIR・IRによる非破壊画像計測を用いて植物形態・水分・温度などの形質を定量化し、耐寒性スクリーニングへの有効性を評価しており、表現型取得法が研究の中心です。
abstractevaluate the efficacy of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, for quantitative assessment of essential plant traits such as plant area, greenness, water content, and temperature
We present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant–environment interactions. Each plant carries in-canopy microclimate sensors (temperature, relative humidity, illuminance) paired with nearby ambient references, yielding real-time canopy-ambient differentials. The system is easy to install: at planting or sowing, sensors are fixed at positions that will lie within the developing canopy, and a separate ambient reference area is designated and kept free of vegetation. As plants grow, they envelop the sensors, thereby capturing growth dynamics over time. The sensors accuracy was validated against a commercial weather station and portable system that measures gas exchange, temperature and light (LI-COR 6800/6400), and the system’s ability to resolve plant physiological activity was confirmed using the PlantArray functional phenotyping platform with independent whole-plant transpiration and biomass references. Under controlled growth-room conditions and across two contrasting Cannabis cultivars, daily transpiration strongly predicted biomass gain (R² > 0.9). Microclimate signals mirrored physiology: midday canopy air was cooler by 4–7 °C, more humid by 18–25 % RH, and increasingly shaded as biomass accumulated, with temperature, RH, and light attenuation showing saturating logarithmic relationships with growth. The network operated for months unattended with low packet loss and predictable power use. It provides 4D (x–y–z–time) coverage, where x and y denote horizontal location, z the vertical position within the canopy, and time the dynamics, enabling resolution of where changes occur and how they evolve, and supplying high-frequency labeled data. This system complements, rather than replaces, precision instruments and high-end phenotyping platforms, providing a scalable layer for continuous tracking across wide areas. We outline practical constraints and next steps toward field pilots, modest energy harvesting, expanded sensor suites, and integration with machine learning for predictive crop management.
Why it matches plant phenotyping methods植物キャノピー内のセンサー網を開発し、植物生理・蒸散・バイオマスを連続推定する方法として検証しており、植物フェノタイピング手法が中心である。
abstractWe present a low-cost, standards-based wireless sensor network (WSN) for continuous, canopy-integrated monitoring of plant–environment interactions.
Deep understanding of slow-wilting is essential for developing drought-tolerant crops. Existing approaches to measure transpiration rates are difficult to apply to large populations due to their high cost and low throughput. To overcome these challenges, we developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device. The system tracked the transpiration rate in real time by measuring changes in the pot weight in 224 recombinant inbred lines of Taekwangkong (fast-wilting) x SS2-2 (slow-wilting) under water-restricted conditions. Among five transpiration features we determined, stress recognition time point (SRTP) and decrease in transpiration rate by stress (DTrs) are informative parameters, that are interconnected and independently affect slow-wilting as well. Quantitative trait loci (QTL) for SRTP and DTrs were identified at the same location as the major QTL for slow wilting, qSW_Gm10 , identified in the previous study. Notably, we found a novel major QTL for DTrs, qDTrs_Gm04 , with a LOD value of 42 and PVE of 47 %. As a candidate gene for qDTrs_Gm04 , GmWRKY58 was selected with differential expression between the parental lines under drought conditions as well as upstream sequence variation. Our high-throughput system is of help not only to biological research but breeding programs of drought-tolerant lines.
Why it matches plant phenotyping methods高スループットなセンサー基盤を開発し、ポット重量変化からダイズの蒸散率・乾燥ストレス応答をリアルタイム抽出することが研究の中心であるため。
abstractwe developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device.
Reproduction assets foundThe paper's data availability statement explicitly deposits the processed phenotypic data (transpiration features from the RIL drought experiment) and trained Random Forest/XGBoost model objects on Figshare, which is a paper-specific, publicly accessible asset.Dataset · publicThe processed phenotypic data, along with the trained Random Forest and XGBoost machine learning model objects (.rds files), are publicly available on Figshare at https://doi.org/10.6084/m9.figshare.c.7951601.v1.Open asset ↗Figshare · 10.6084/m9.figshare.c.7951601.v1html-lines:276-299Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published3 Sept 2025Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous TechnologiesCited by 3 · OpenAlex ↗
Water stress significantly impacts plant health and crop yields worldwide. Traditional methods, such as soil moisture sensors, often lack accuracy, are invasive, and labor-intensive. This paper introduces MotionLeaf, a novel mmWave-based prototype system that assesses plant stress by measuring vibration frequencies across multiple leaves. MotionLeaf features a specialized signal processing pipeline to estimate fine-grained damped frequencies from noisy micro-displacement measurements captured via mmWave radar. Specifically, the Interquartile Mean (IQM) of phase differences from neighboring Frequency-Modulated Continuous Wave (FMCW) radar chirps is used to calculate micro-displacements. Additionally, multiple radar antennas isolate the vibration signals of individual leaves through a Blind Source Separation (BSS) method. Experimental results show that MotionLeaf measures leaf vibration frequencies with an average error of 0.0176 Hz, less than half of the 0.0416 Hz error of the state-of-the-art approach (mmVib [26]). In practical drought experiments, MotionLeaf effectively indicated water stress through observed day-night frequency variations below 0.06 Hz over a seven-day trial. Furthermore, additional validation using fan-generated wind confirmed the feasibility of passive excitation in outdoor environments, achieving low-frequency measurement errors (approximately 0.03 Hz) at wind speeds above 2.5 m/s. These results underscore the effectiveness and potential of MotionLeaf as a scalable, non-invasive solution for accurately detecting plant water stress in realistic agricultural scenarios.
Why it matches plant phenotyping methodsmmWaveセンサーと信号処理・BSSを用いて葉の振動周波数を抽出し、水ストレスを評価する手法の開発と技術検証が中心である。
abstractThis paper introduces MotionLeaf, a novel mmWave-based prototype system that assesses plant stress by measuring vibration frequencies across multiple leaves.
Stomata, the gatekeepers of leaf gas exchange, regulate carbon dioxide uptake and water loss, functions increasingly critical as crops face more frequent, intense heat and drought. Under dry conditions, stomatal conductance ( g s ) typically decreases, limiting carbon assimilation and yield. Heat stress, in contrast, elicits variable g S responses: sometimes increasing to facilitate transpirational cooling, while at other times decreasing, especially when combined with drought. Heat and drought also induce complex, context-dependent shifts in stomatal anatomy. Smaller, denser stomata improve drought resilience in some cases, while reduced density confers greater tolerance in others. The optimal stomatal ideotype remains unknown, and different or even opposing traits may confer resilience dependent on the environmental scenario. Substantial genotypic variation in g s and stomatal anatomy, high heritability and co-localized quantitative trait loci for stomatal traits and yield highlight their untapped potential as breeding targets for climate-resilient crops. However, stomatal traits remain largely absent from breeding pipelines due to challenges of phenotyping at scale. This is changing rapidly. Advances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy. Next-generation breeding technologies including clustered regularly interspaced short palindromic repeats (CRISPR), multi-omics approaches, and artificial intelligence-driven ideotype selection models could revolutionize breeding, allowing precise engineering of stomatal traits for resilience to environmental stress. The time has come to move beyond characterizing stomatal traits and start actively incorporating them into breeding strategies. By leveraging these technologies, stomatal traits can become high value targets, unlocking their potential to enhance crop performance in a hotter, drier future.
Why it matches plant phenotyping methods気孔の生理・解剖形質を大規模に取得する深層学習、ポロメトリー、デジタル顕微鏡、リモートセンシング技術を扱うレビューであり、植物フェノタイピング手法が実質的な主題に含まれる。
abstractAdvances in deep learning, porometry, digital microscopy, and remote sensing now enable high-throughput measurement of stomatal physiology and anatomy.
Accurate estimation of leaf biochemical parameters is crucial for understanding crop physiology and monitoring nutritional status. Remote sensing algorithms perform well on limited germplasm, but the transferability to high-throughput phenotyping with diverse genotypes remains unclear. This study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions. Results showed that Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and Modified Simple Ratio (mSR) were strongly correlated with Cab, Cw, and Cm, respectively, highlighting their importance in the inversion models. Comparative analysis revealed that the RF model achieved the highest accuracy for Cab (R 2 = 0.77, RMSE = 8.14 µg cm −2 ), Cw (R 2 = 0.67, RMSE = 1.1 × 10 −3 g cm −2 ), and Cm (R 2 = 0.50, RMSE = 6.2 × 10 −4 g cm −2 ), followed by the PROSPECT model, with R 2 and RMSE of 0.76 and 8.21 µg cm −2 for Cab, 0.61 and 1.2 × 10 −3 g cm −2 for Cw, and 0.38 and 7.7 × 10 −4 g cm −2 for Cm, respectively. However, the PROSPECT model was most effective in Cab inversion across diverse germplasm resources (R 2 = 0.58, RMSE = 7.68 µg cm −2 ), demonstrating its superior transferability and stability. These results underscore its value in high-throughput phenotyping and improving the accuracy and generalizability of crop biochemical parameter estimation. • The PROSPECT model exhibited superior transferability compared to other models across diverse peanut germplasm resources. • Different hyperspectral inversion models exhibited variations in estimating peanut leaf parameters. • Leaf chlorophyll content estimating showed high accuracy than other leaf parameters.
Why it matches plant phenotyping methodsハイスループット分光計測によりピーナッツ葉の生化学的形質を推定し、複数の反転モデルの精度・移植性を比較評価しており、フェノタイピング手法が中心である。
abstractThis study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions.
This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light–canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations of traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R 2 =0.91, RMSE=0.42 m 2 /m 2 ), leaf chlorophyll content (LCC, R 2 =0.61, RMSE=3.89 μ g/cm 2 ), leaf nitrogen content (LNC, R 2 =0.86, RMSE=1.13 × 10 −2 mg/cm 2 ), leaf dry matter content (LMA, R 2 =0.84, RMSE=0.15 mg/cm 2 ), and leaf water content (LWC, R 2 =0.78, RMSE=0.88 mg/cm 2 ). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.
Why it matches plant phenotyping methods3D放射伝達モデル、動的生長モデル、ハイパースペクトル画像、機械学習を統合し、トウモロコシ形質推定法を開発・検証しており、形質取得が研究の中心である。
abstractThis study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data.
This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light–canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations of traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R²=0.91, RMSE=0.42 m²/m²), leaf chlorophyll content (LCC, R²=0.61, RMSE=3.89 μg/cm²), leaf nitrogen content (LNC, R²=0.86, RMSE=1.13 × 10⁻² mg/cm²), leaf dry matter content (LMA, R²=0.84, RMSE=0.15 mg/cm²), and leaf water content (LWC, R²=0.78, RMSE=0.88 mg/cm²). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.
Why it matches plant phenotyping methods3D放射伝達モデル、成長モデル、ハイパースペクトル画像、機械学習を統合し、トウモロコシの複数形質を推定する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractThis study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data.
Accurate knowledge of vegetation water content (VWC) and crop height is crucial for agricultural management, environmental monitoring, and for satellite-based retrieval algorithms for geophysical variables. Traditional methods to estimate VWC, primarily rely on optical indices, which has limitations of biomass saturation, and sensitivity to atmospheric conditions. This study introduces a novel application of geospatial foundation models (GFMs), leveraging extensive, unlabeled datasets through self-supervised learning to enhance the skill of VWC and crop height estimation. We developed a comprehensive model integrating Sentinel-1 A C-band SAR and Sentinel-2 A/B indices with weather parameters to estimate soybean and corn VWC and crop height. Our research study area spans a variety of climatic zones and management practices, from the humid continental climate of Iowa and Michigan to the subtropical environment of Florida, encompassing both irrigated and non-irrigated fields as well as diverse tillage practices. We compared the performance of Single-Task Learning GFM (STL-GFM), Multi-Task Learning GFM (MTL-GFM), and machine learning techniques including Random Forest (RF), and XGBoost (XGB) to evaluate their effectiveness in estimating VWC and crop height. Results demonstrated that STL-GFM outperforms other methods in accuracy and generalizability. For VWC estimation, STL-GFM achieved R² values of 0.90 and 0.89 for soybean and corn, respectively. For crop height, R² values reached 0.95 for soybean and 0.98 for corn. The integration of SAR, optical, and climate data provided more reliable estimations than using individual data sources. Feature importance analysis identified NDVI, NDWI, VH backscatter, and precipitation as key drivers for accurate VWC and height estimations. The red-edge band emerged as significant for VWC estimation but showed limited importance for height prediction. Notably, surface roughness demonstrated a noticeable impact on corn VWC and height estimations, while soil moisture exhibited less influence than initially anticipated. Notably, without directly incorporating soil moisture and surface roughness data, but by including diverse field conditions in training and validation, the STL-GFM model demonstrated strong generalization capabilities. This study highlights the potential of GFMs in advancing crop monitoring techniques, offering more reliable data for precision agriculture, and supporting sustainable farming practices across diverse agricultural landscapes.
Why it matches plant phenotyping methodsSAR・光学衛星データと自己教師あり地理空間基盤モデルを統合し、作物のVWCと草丈を推定する手法を開発・比較・評価しており、植物表現型の取得・抽出が中心である。
abstractThis study introduces a novel application of geospatial foundation models (GFMs), leveraging extensive, unlabeled datasets through self-supervised learning to enhance the skill of VWC and crop height estimation.
Drought is a major abiotic stress that adversely affects plant growth, physiology, and crop yield. Conventional methods for assessing drought stress tend to be fragmented, targeting either leaves, canopies, or roots, and are often expensive, low-throughput, and lack the ability to provide real-time, whole-plant insights. Addressing these limitations, this study presents a novel, integrated pipeline titled Intelligent Decision Support for Drought Stress (IDSDS) that leverages remote sensing and artificial intelligence (AI) for accurate, real-time monitoring of drought stress across entire plants. The IDSDS pipeline employs low-cost RGB images collected at various growth stages and uses a deep learning-based model to reconstruct hyperspectral data, which is typically costly and complex to obtain. This reconstructed data enables the extraction of key physiological traits, including greenness, saturation, and pigment content. A novel phenotyping metric—Greenness Coefficient (GC), was also proposed, offering precise spatial analysis of drought impact within the plant. The hyperspectral reconstruction model was validated using standard performance metrics such as the correlation coefficient, mean squared error, standard deviation of squared error, and spectral angle mapper (SAM). IDSDS further calculates a comprehensive set of spectral indices (e.g., greenness, leaf pigment, water content) that are closely linked to drought-induced changes. Finally, by integrating these indices with machine learning-based classification models, IDSDS accurately stratifies plant drought stress into seven distinct categories. The results showed that the proposed hyperspectral reconstruction model effectively converts RGB plant images into accurate hyperspectral data, achieving a SAM value between 0.14 and 0.30. This indicates strong spectral similarity, meaning the reconstructed pixel spectra closely align with the reference spectra. The GC, along with other reconstructed spectral indices, supports visual interpretation and enhances the traceability of the system’s outputs, thereby increasing transparency. Additionally, the findings demonstrate statistically significant results (p < 0.001) for these indices in detecting plant drought stress, with a high classification accuracy of 99 % and an average area under the curve (AUC) of 1.00, reflecting precise differentiation of stress across the entire plant. Overall, the study introduces a breakthrough in drought stress monitoring, combining high-throughput and cost-effective RGB imaging with AI to support both scientific research and practical crop management. The IDSDS pipeline lays the groundwork for informed, drought-adaptive decision-making of agricultural crops.
Why it matches plant phenotyping methodsRGB画像から植物のハイパースペクトル情報と生理形質を推定し、乾燥ストレスを分類するIDSDSパイプラインを開発・検証しており、植物表現型取得法が研究の中心である。
abstractthis study presents a novel, integrated pipeline titled Intelligent Decision Support for Drought Stress (IDSDS) that leverages remote sensing and artificial intelligence (AI) for accurate, real-time monitoring of drought stress across entire plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate diagnosis of crop water demand is a core challenge in alleviating agricultural water scarcity. Traditional methods rely on soil moisture sensors or empirical models based on meteorological data, and have significant limitations. Therefore, developing real-time, non-destructive, and precise diagnostic technologies that reflect the crop's own water status will be crucial. In recent years, the high-throughput phenotyping technology has advanced rapidly and provided revolutionary tools to address this challenge. This paper explores the use of this technology to capture water-sensitive phenotypic traits of crops under water stress and construct water demand diagnosis models for real-time irrigation decision-making. By systematically reviewing research progress, technical methods, modeling strategies, and existing challenges, this study aims to provide theoretical support for precision irrigation and smart agriculture.
Why it matches plant phenotyping methods作物の水ストレス関連形質を高スループットに取得し、水需要診断モデルへ応用するフェノタイピング手法を体系的にレビューしており、方法論が中心である。
abstractThis paper explores the use of this technology to capture water-sensitive phenotypic traits of crops under water stress and construct water demand diagnosis models for real-time irrigation decision-making.
Accurate estimation of leaf biochemical parameters is crucial for understanding crop physiology and monitoring nutritional status. Remote sensing algorithms perform well on limited germplasm, but the transferability to high-throughput phenotyping with diverse genotypes remains unclear. This study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions. Results showed that Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and Modified Simple Ratio (mSR) were strongly correlated with Cab, Cw, and Cm, respectively, highlighting their importance in the inversion models. Comparative analysis revealed that the RF model achieved the highest accuracy for Cab (R² = 0.77, RMSE = 8.14 µg cm⁻²), Cw (R² = 0.67, RMSE = 1.1 × 10⁻³ g cm⁻²), and Cm (R² = 0.50, RMSE = 6.2 × 10⁻⁴ g cm⁻²), followed by the PROSPECT model, with R² and RMSE of 0.76 and 8.21 µg cm⁻² for Cab, 0.61 and 1.2 × 10⁻³ g cm⁻² for Cw, and 0.38 and 7.7 × 10⁻⁴ g cm⁻² for Cm, respectively. However, the PROSPECT model was most effective in Cab inversion across diverse germplasm resources (R² = 0.58, RMSE = 7.68 µg cm⁻²), demonstrating its superior transferability and stability. These results underscore its value in high-throughput phenotyping and improving the accuracy and generalizability of crop biochemical parameter estimation.
Why it matches plant phenotyping methods植物葉の生化学的形質をハイスループット分光センシングと反転モデルで推定し、複数モデルの精度・移植性を比較評価しており、表現型取得手法が中心である。
abstractThis study estimated leaf chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm) using the single vegetation index (SVI), random forest (RF), and the PROSPECT model to evaluate the performance and transferability of these models under diverse peanut germplasm conditions.
Crop water stress (CWS) monitoring using UAV remote sensing has traditionally been limited to empirical models and specific growth stages, restricting dynamic, season-long assessment. This study proposes an integrated framework combining multispectral UAV observations with the SAFYE crop model via Ensemble Kalman Filter -based data assimilation (DA) to improve maize growth simulation and enable continuous CWS monitoring. Based on three years of field experiments, accurate inversion models for leaf area index (LAI; R 2 = 0.837, RMSE = 0.397) and aboveground biomass (AGB; R 2 = 0.862, RMSE = 224 g m −2 ) were developed using a random forest algorithm. Model parameters were calibrated using particle swarm optimization, and UAV-derived data were assimilated to optimize simulations of crop growth and actual evapotranspiration (ET c act ). Results show that DA significantly enhanced model performance: LAI simulation RMSE decreased from 0.29–0.61–0.11–0.36 (NRMSE: 3.57–11.56 %), AGB simulation RMSE from 148.2–255.7–49.3–136.8 g m −2 (NRMSE: 5.39–14.27 %), and agreement index (d) exceeded 0.92. ET c act simulations accurately reflected responses to irrigation and rainfall, with only 4.97 % relative error under full irrigation (W4). The developed crop water stress index (CWSI) effectively quantified water stress under different irrigation treatments. A significant negative correlation was observed between CWSI reduction and irrigation amount, while the severity of water deficit was positively correlated with the peak value of CWSI differences in terms of both timing and magnitude. This study establishes a robust UAV–crop model DA framework for dynamic, season-long CWS diagnosis and assessment.
Why it matches plant phenotyping methodsUAV画像からLAI・AGB・作物水ストレスを推定し、作物モデルとのデータ同化で季節を通じて評価する技術的フレームワークが研究の中心である。
abstractThis study proposes an integrated framework combining multispectral UAV observations with the SAFYE crop model via Ensemble Kalman Filter -based data assimilation (DA) to improve maize growth simulation and enable continuous CWS monitoring.
Rapid and accurate monitoring of crop water status is essential for ensuring sustainable agricultural development and food security. Crops exhibit a complex set of growth and physiological responses under water deficit. Existing studies primarily focused on the monitoring of phenotypic parameters, while the physiological indicators highly relevant to crop water status were ignored. In this context, we aimed to develop a novel model to comprehensively and accurately monitor maize water status using multi-source UAV data and multiple growth and physiological indicators. We first composed the original dataset, including feature variables based on multi-source UAV data (spectral indices, texture indices, thermal indices, and structural indices) and prediction variables based on field measurements (equivalent water thickness, stomatal conductance, transpiration rate, and actual photochemical efficiency) in 2023 and 2024. Next, the tabular denoising diffusion probabilistic model (TabDDPM) was employed for synthesizing new samples to adequately train the models. Then, a deep learning network named TAM-Net, with the hybrid attention mechanism and multi-task learning, was trained on the synthetic dataset. Finally, the fuzzy comprehensive water index (FCWI) considering uncertainty and variability was obtained in 2023–2024. The results indicated that multi-source data significantly improved the model performance, with the R² of 0.52–0.63, and NRMSE of 27.89 %–29.86 %. TabDDPM was able to synthesize new datasets with high similarity and effectiveness. TAM-Net achieved the highest monitoring accuracy for the four indicators of crop water status (R² of 0.76–0.90, NRMSE of 12.92 %–22.95 %). FCWI effectively assessed the water status across different treatments. Overall, TAM-Net was demonstrated with powerful performance for monitoring maize water status, which has potential in supporting precision irrigation practices.
Why it matches plant phenotyping methodsUAVマルチソース画像からトウモロコシの水分状態・生理形質を推定するTAM-Netを開発し、精度評価まで行っており、表現型取得・推定手法が研究の中心である。
abstractwe aimed to develop a novel model to comprehensively and accurately monitor maize water status using multi-source UAV data and multiple growth and physiological indicators.
Satellite land surface temperature (Ts) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in Ts and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite Ts and its derived metrics for early detection of vegetation drought stress before visible canopy changes occur has not been widely assessed. Here, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress using the cumulative sub-diurnal difference from late-morning to early-afternoon between Ts from the Himawari-8 geostationary satellite and hourly air temperature (Ta) from meteorological grids. SATDA utilised Ts−Ta as the physical driving gradient for sensible heat flux (H) to capture anomalous sensible heating due to reduced plant transpiration. We used SATDA to monitor the spatio-temporal patterns of the 2017–2019 Tinderbox Drought in southeast Australia. We benchmarked the skill of SATDA in forecasting visible drought-induced vegetation greenness decline against both conventional water availability-based indices (i.e., precipitation and soil moisture anomalies) and existing satellite Ts indices (i.e., Temperature Condition Index and Temperature Rise Index) across diverse climates and land covers. SATDA effectively captured a rapidly intensifying flash drought event at multi-week timescales (Jul to Sep 2019) embedded within the multi-year Tinderbox Drought, which contributed to detrimental impacts on agricultural production and increased wildfire risk. SATDA showed the best vegetation greenness forecast skill in the transitional semi-arid and sub-humid climates, with forecast correlation >0.5 at 32-day lead time. The advantage over water availability-based indices was more evident in woody-dominated ecosystems than herbaceous-dominated ecosystems, likely due to the importance of physiological regulations by trees during droughts such as deeper roots and stronger stomatal control. SATDA, based on Ts−Ta, showed overall better vegetation greenness forecasts than two Ts-only indices, especially in woody vegetation. Finally, SATDA showed consistently greater advantage over water availability-based and Ts-only indices in forecasting visible vegetation decline as the drought intensity increased. The parsimonious process-based SATDA method suits global-scale operational implementation to complement vegetation drought monitoring and early warning systems.
Why it matches plant phenotyping methods衛星温度と気象データから植物の干ばつストレスを推定するSATDA法を開発し、既存指標と比較検証しており、植物状態の取得手法が研究の中心である。
abstractHere, we developed a parsimonious Surface-Air Temperature Difference Anomaly (SATDA) method for tracking vegetation drought stress
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.
Introduction Potato ( Solanum tuberosum L .) production in semi-arid regions requires precision irrigation management to address water scarcity, highlighting the critical need for real-time, non-destructive plant water status assessment techniques. This study aimed to investigate the feasibility of measuring the leaf-air temperature difference (LAD) as an indicator for diagnosing potato water status. Methods A field experiment was conducted with five irrigation levels (0-300 mm) to evaluate LAD responses at three leaf positions (L 1 , L 4 , and L 8 ) across different growth stages. Results The results demonstrated that LAD significantly correlated with irrigation levels, plant water content (PWC), and soil moisture, with the strongest relationships observed for the fourth leaf from the top (L 4 ). L 4 exhibited the highest sensitivity to water status, the lowest variability among plants. A binomial regression between LAD and yield was identified, revealing a threshold LAD beyond which further LAD increases did not enhance the yield. These findings not only suggest that LAD can be a reliable indicator for monitoring potato water status but also identify L 4 as the optimal leaf position for LAD-based water status monitoring. Discussion The study provides a foundation for precision irrigation in potato production, enabling improved water use efficiency and sustainable potato production in a semiarid region.
Why it matches plant phenotyping methods葉温−気温差（LAD）によるジャガイモの水分状態推定を中心に、葉位別の感度・変動性・指標性能を評価しており、植物生理状態の計測法の検証に該当する。
abstractThis study aimed to investigate the feasibility of measuring the leaf-air temperature difference (LAD) as an indicator for diagnosing potato water status.
Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R2) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R2 = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future.
Why it matches plant phenotyping methods冬小麦の作物水分状態（植物水分含量）を、キャノピー hyperspectral データと環境データから機械学習で推定するモデル開発・性能評価が中心であり、植物生理状態のセンシング手法に該当する。
abstractTo address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms.
Abstract. Plant hydraulic properties are critical to predicting vegetation water use as part of land–atmosphere interactions and plant responses to drought. However, current measurements of plant hydraulic properties are labor-intensive, destructive, and difficult to scale up, consequently limiting the comprehensive characterization of whole-plant hydraulic properties and hydraulic parameterization in land-surface modeling. To address these challenges, we develop a pumping-test analogue method, using sap-flow and stem water-potential data to derive whole-plant hydraulic properties, namely, maximum hydraulic conductance, effective capacitance, and Ψ50 (water potential at which 50 % loss of hydraulic conductivity occurs). Experimental trials on Allocasuarina verticillata indicate that the parameters derived over short periods (around 7 d) exhibit good representativity for predicting plant water use over at least 1 month. We applied this method to estimate near-continuous whole-plant hydraulic properties over 1 year, demonstrating its potential to supplement existing labor-intensive measurement approaches. The results reveal the seasonal plasticity of the effective plant hydraulic capacitance. They also confirm the seasonal plasticity of maximum hydraulic conductance and the hydraulic vulnerability curve, known in the plant physiology community, while neglected in the hydrology and land-surface modeling community. It is found that the seasonal plasticity of hydraulic conductance is associated with climate variables, providing a way forward to represent seasonal plasticity in models. The relationship between derived maximum hydraulic conductance and Ψ50 also suggests a trade-off between hydraulic efficiency and safety of Allocasuarina verticillata. Overall, the pumping-test analogue offers potential for better representation of plant hydraulics in hydrological modeling, benefitting land-management and land-surface process forecasting.
Why it matches plant phenotyping methods sap-flow と幹の水ポテンシャルから全植物体の水理特性を推定する新手法を開発し、予測性能を検証しているため、植物生理形質の取得方法が中心である。
abstractwe develop a pumping-test analogue method, using sap-flow and stem water-potential data to derive whole-plant hydraulic properties
Abstract. Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (fg). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic fg improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.
Why it matches plant phenotyping methodsUAVの熱・マルチスペクトル・LiDARデータとTSEB-PTモデルにより、作物の蒸発散と水ストレスを推定する手法を中心に扱い、渦相関データで技術検証しているため、植物フェノタイピング手法研究に該当する。
abstractThis study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Growth / development / phenologyStress response / toleranceWater status / transpiration
The field of plant sensing technologies is undergoing a transformative shift, driven by innovations in both flexible wearable devices and genetically encoded sensors (GESs). From this standpoint, we emphasize their potential in real-time, in situ monitoring of plant physiology and stress responses. Wearable sensors enable continuous detection of plant growth, microclimate, water transport, surface potential, and immune responses, offering unprecedented insight at the tissue level. In parallel, GESs provide high-resolution, intracellular visualization of key signaling molecules such as calcium, reactive oxygen species, and plant hormones, as well as dynamic changes in pH. While these technologies represent significant advancements over traditional methods, practical challenges remain. Issues of adaptability, sensing stability, spatial resolution, limited parameter coverage, and integration across sensing modalities require further investigation. We envision a future in which interdisciplinary approaches, including material science, engineering, synthetic biology, and data analytics, enable the development of robust, scalable, and multimodal plant sensing systems. These next-generation tools could revolutionize high-throughput phenotyping, precision agriculture, and fundamental plant biology, ultimately contributing to more sustainable and resilient agricultural systems.
Why it matches plant phenotyping methods植物の生理・ストレス状態をリアルタイム測定するウェアラブルおよび遺伝子コード型センサーを中心に扱う方法論的レビューであり、植物フェノタイピングへの応用も明示されている。
abstractThe field of plant sensing technologies is undergoing a transformative shift, driven by innovations in both flexible wearable devices and genetically encoded sensors (GESs).
Deep understanding of the structural composition and growth of biological specimens is becoming increasingly important for the development of bio-based and sustainable material systems. Full-field nano-computed tomography is particularly suitable for this purpose as it allows for non-destructive 3D imaging at high spatial resolution. However, most biological samples are functionalized by water and respond sensitively to any changes in climate conditions, specifically relative humidity, by adjusting their material moisture content. To date, only a limited number of tomography instruments offer an in situ climate control option to users. These, however, are limited either by the range of relative humidity states, the long times required to change the climate state, or obstruction or attenuation of the beam. Here, the first fully automatized climate cell for in situ full-field nanotomography is presented. It has been designed, built and integrated at the nanotomography station at the P05 imaging beamline, operated by Hereon at the DESY storage ring PETRA III, Germany. The highly flexible and windowless design allows the humidity dependent swelling and shrinking of lignified plant cell walls to be studied in situ, using phase contrast nanotomography. The concept of this climate chamber can easily be integrated into other setups. It operates in the relative humidity range of 0-90% and can be controlled in a temperature range of 10-50°C. Climate conditions can be adjusted at any time, remotely from the control hutch by using a humidity generator. Results show that the developed setup maintains a stable climate during the entire duration of a tomographic scan at different humidities and does not obstruct the sample or hinder the imaging conditions. During the tomographic investigation the sample remains stable in the flow of the air stream and shows typical cell wall swelling and shrinking behaviour depending on the equilibrium moisture content. This new climate cell is now available to all users of the P05 nanotomography instrument for conditioning samples, serving a wide range of scientific applications.
Why it matches plant phenotyping methods植物細胞壁の膨潤・収縮を非破壊に取得するナノトモグラフィー用の自動環境制御セルを開発・統合し、安定性と撮像性能を検証しているため、植物表現型取得法が中心である。
abstractHere, the first fully automatized climate cell for in situ full-field nanotomography is presented.
Phenotyping is pivotal in biological and agronomical research, enabling the characterization of phenotypic traits in living organisms. Recent advancements have led to the development of innovative platforms that enhance the precision of phenotyping, integrating genetic and ecophysiological analyses for a comprehensive understanding of plant growth under controlled conditions. These technologies are instrumental in studying plant responses to environmental stresses, such as drought, which disrupts water balance in plants. This study focuses on the adaptability of grafted grapevines (Vitis vinifera L.) to drought stress, emphasizing the rootstock influence on scion performance. The experimental trial was performed at 'PhenoPlant,' a cutting-edge phenotyping platform at the University of Torino, DISAFA. PhenoPlant is a non-invasive, high-throughput tool that employs advanced technologies, including a PlantEye sensor for 3D vision and multispectral imaging, measurement of the potted-plant evapotranspiration by gravimetric technique, water potential assessment and Infra-Red Gas Analysis for leaf-to-atmosphere gas exchange detection. Grapevine responses to drought stress across eleven scion/rootstock combinations, featuring clones of Nebbiolo and Pinot Noir grafted onto rootstocks with varying drought tolerance were assessed. A 13-day drought-recovery experiment on grafted 1-year old plants, three months after in-pot-transplanting revealed significant differences in drought responses among rootstock/scion combinations. Drought-tolerant rootstocks (e.g., 1103 P, 110 R, 140Ru, M2) maintained stable spectrometric indices (e.g.: GLI, Green Leaf Index) mirroring morpho-physiological ones (e.g., Leaf Surface Angle - SA, Stomatal Conduction - gs, Stem Water Potential and Evapotranspiration), unlike their less tolerant counterparts (e.g., Kober 5BB, SO4, 420 A, Gravesac). In particular, after 10 days of water removal, a reduced variation in some traits was observed in tolerant combinations (SA: 39-44°; GLI ≈ 0.33-0.35; gs: 34.5-45.4 mmol H₂O·m⁻²·s⁻¹), while decreasing markedly in sensitive ones (SA: 27-35°; GLI: 0.28-0.32; gs: 8.6-10.8 mmol H₂O·m⁻²·s⁻¹), underscoring the rootstock's crucial role in drought response, independently from scion cultivar. These findings are vital for a fast and early assessment of multiple rootstock/scion combinations to optimize grapevine management and breeding programs for enhanced performance under water-limited conditions. Intrinsic limitations of the measurement system and aspects to be considered to export results from the platform to the vineyard are presented and discussed.
Why it matches plant phenotyping methods3D・マルチスペクトルセンサーを用いる高スループット表現型解析プラットフォームを中心に、干ばつ応答の早期評価と測定系の限界を検討しているため。
titleCan high-throughput 3D and multispectral phenotyping detect early grapevine responses to water stress events?
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 6 Sept 2026
ABSTRACT Improving transpiration efficiency (TE) offers a pathway to increase yield in drought-prone environments. This study examined genotypic variation in TE and its physiological determinants across diverse wheat lines. An initial experiment with six cultivars was expanded to 105 genetically-diverse genotypes evaluated under well-watered conditions and fluctuating vapour pressure deficit (VPD). Using a high-throughput lysimeter platform, transpiration rates were recorded every 10 minutes and normalised daily at low VPD to account for genotypic variations in canopy size. TE was strongly associated with reduced normalised transpiration rate at high VPD (TR norm-highVPD ), while no significant relationship was found with maximum photosynthetic capacity. High-TE lines achieved either greater biomass with similar water use or similar biomass with lower water use, reflecting a water-saving strategy under high evaporative demand. A complementary experiment under low VPD revealed limited genotypic variation in intrinsic TE, reinforcing the value of TR norm-highVPD as a screening trait. Consistent correlations between TE and TR norm-highVPD across experiments highlight the potential of TR norm-highVPD as a robust phenotyping target. Several high-TE lines outperformed modern cultivars, offering promising sources of novel alleles. These findings provide a scalable framework to identify drought-resilient genotypes and support breeding strategies to improve water productivity and achieve more crop per drop. HIGHLIGHT Wheat genotypes with reduced normalised transpiration rate under high evaporative demand achieved greater transpiration efficiency. A new automatised high-throughput phenotyping framework is proposed to assist drought-resilient breeding.
Why it matches plant phenotyping methods高スループットライシメータによる蒸散効率・正規化蒸散速度の取得とスクリーニング枠組みが研究の中心であり、育種向け表現型測定法を提案・評価している。
abstractUsing a high-throughput lysimeter platform, transpiration rates were recorded every 10 minutes and normalised daily at low VPD to account for genotypic variations in canopy size.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture.
Why it matches plant phenotyping methodsUAVリモートセンシングによる作物の水分・栄養状態という植物形質の推定を中心に、センサー構成、画像処理、データ融合、推定モデル、性能要因を体系的にレビューしているため、方法レビューとして含める。
abstractThis review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring.