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.
ArabidopsisThermalLeafTissueGrowth / time-series analysisStress response / tolerancePlant / canopy temperature
Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cues to the later stage of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of the injury-associated cold response and downstream salicylic acid (SA) signaling. Our findings suggest that the CBF-SA pathway acts coordinately to promote lignin and callose deposition, thereby facilitating wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.
Why it matches plant phenotyping methods熱画像とコンピュータビジョン・深層学習を組み合わせ、植物の創傷治癒を定量的・非侵襲的・リアルタイムに測定するワークフローを開発しており、表現型取得法が研究の中心である。
abstractUsing thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive, and real-time manner.
Thermal imaging enables non-invasive assessment of canopy temperature, an essential indicator of plant stress, yet the lack of color cues and strong shadow interference make plant segmentation in thermal images difficult. Recent advances in foundation models have demonstrated improved performance and generalizability across applications, showing promise for domain-specific applications with limited annotated datasets such as plant segmentation in thermal images. This study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping. Building upon the SAM-CLIP framework, we design a unified pipeline spanning zero-shot inference, few-shot and low-shot fine-tuning, and active learning to maximize accuracy with minimal supervision. Evaluations on two thermal datasets, LadyBird Brassica and UGA Brassica, demonstrate robust performance after minimal adaptation across both datasets and superior performance compared with baselines, achieving mIoU D values of 97.54% on the LadyBird dataset and 76.94 % on the UGA dataset. We also release the resulting thermal segmentation annotations to support community benchmarking and reproducible research, highlighting the potential of LMMs to enable scalable, high-quality dataset construction for field phenotyping. The released datasets can be found at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89
Why it matches plant phenotyping methods熱画像から植物を分割する手法を開発・評価し、植物フェノタイピング用データセットとアノテーションも公開しているため、フェノタイピング手法が中心的である。
abstractThis study investigates large multimodal models (LMMs) for thermal image segmentation in plant phenotyping.
Reproduction assets foundThe authors publicly released the paper-specific thermal segmentation annotations (20,538 LadyBird masks and 37,790 UGA masks) via a Cornell Box link stated in the abstract, results, and data availability statement. No author analysis code or trained model checkpoints are explicitly released; the mmsegmentation GitHub/Dataset · publicwe generated and publicly released segmentation annotations for the complete LadyBird and UGA thermal image datasets using the best-performing SAM-CLIP model. Specifically, the final model obtained through the multi-round training process was used to generate 20,538 masks for the LadyBird dataset and 37,790 masks for the UGA dataset. Details of the generated annotations are provided in Supplementary Fig. S1 , and both annotated datasets are publicly available at: https://cornell.box.com/s/dh69xf84464yrc1vlws92l1tflx7qa89Open asset ↗lines:220-232Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
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.
Abstract. Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.
Why it matches plant phenotyping methodsUAV LiDAR・熱画像を用いて個体樹木の樹冠高や樹冠面積などの植物構造形質を抽出する手法と統合ワークフローが中心であり、単なる環境測定ではない。
abstractLiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density.
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.
Published23 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Field / plotNeRF / 3D Gaussian SplattingThermalWhole plant / canopy / plot / field2D/3D reconstructionVisualization / data managementPlant / canopy temperature
Abstract. Urban trees provide critical ecosystem services in dense city environments, yet current workflows for monitoring their thermal behaviour remain confined to 2D desktop-based analysis with no three-dimensional spatial context or field-deployable visualization capability. This paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment. TIR images of a Tilia tomentosa acquired with a FLIR T560 camera are preprocessed with a standardized false-colour palette and fed into the MILo (Mesh-In-the-Loop Gaussian Splatting) framework to reconstruct a thermally attributed 3D mesh. Geometric evaluation against a Z+F IMAGER 5016 TLS reference using the M3C2 algorithm demonstrates that MILo recovers 13.5 times more canopy geometry than traditional multi-view stereo under thermal imagery, with a standard deviation of 4.0 cm. A colourmap inversion procedure recovers per-vertex temperature estimates from the GS-derived mesh colours, yielding a mean absolute difference of 0.7°C against direct T-Cam measurements (thermal camera mounted on the laser scanner), within the combined instrument accuracy of both sensors. The resulting thermal Gaussian Splat was deployed in a custom Android AR application supporting hybrid marker-based and GPS-based spatial anchoring for in-situ visualization. These results demonstrate the technical feasibility of GS-based thermal reconstruction and mobile AR as a medium for communicating three-dimensional canopy thermal information to educators and urban forestry practitioners.
Why it matches plant phenotyping methods都市樹木の葉冠温度と3D形状を取得・可視化する熱画像ベースの再構成パイプラインを開発し、TLSおよび熱カメラとの定量検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment.
TomatoMultimodalLeafClassificationDisease symptoms / severityStress response / tolerancePlant / canopy temperature
Wearable plant sensing systems for simultaneous biochemical and physiological monitoring with real-time multimodal data analysis remain limited. Here, we present FolioClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, light, CO 2 , and three volatile organic compounds (VOCs) with high selectivity. Its bookmark-inspired design enables secure attachment to plant leaves of diverse morphologies and it integrates a flexible printed circuit board for wireless data transmission. We also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated FolioClip–FolioOmni platform detects and classifies nine stresses, including light, water, CO 2 , mechanical cut, P. infestans , and A. alternata , in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.
Why it matches plant phenotyping methods葉の生理・健康状態とストレスを測定するウェアラブルセンシング装置および機械学習解析基盤を開発し、複数ストレスで性能評価しているため、植物フェノタイピング手法が中心である。
abstractWe also develop FolioOmni, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.
Selecting ideal drought-tolerant wheat varieties requires a holistic synthesis of digital phenotypes, molecular markers, and agronomic indices. This study evaluated 16 wheat genotypes for drought tolerance by integrating digital phenotyping (UAV-based thermal imaging), molecular data (DREB gene expression profiles), and 12 agronomic indices. While vegetative DREB accumulation remained mostly homogeneous, the generative stage triggered pronounced transcriptional shifts, and late-stage thermal screening revealed highly significant genotypic differences during grain filling. A multivariate PCA biplot identified early canopy temperature differences during tillering (ΔCT_TL) as the most informative non-destructive selection indicator. ΔCT_TL showed a strong positive association with terminal yield stability metrics (YSI and RSI) and a marked negative relationship with the drought sensitivity index (SDI). This early canopy temperature regulation contributed to the maintenance of yield stability in the modern hexaploid variety MFTBY-T and advanced tetraploid lines OR2-T and OR4-S. In contrast, poorly adapted ancient varieties (P5-S and S3-S) exhibited high drought sensitivity accompanied by pronounced late-stage induction of DREB1 and DREB2, suggesting a delayed stress-response mechanism activated under severe tissue dehydration. Conversely, the modern tetraploid variety KZLTN-T and hexaploid landraces appeared to rely on an early vegetative molecular priming strategy. These findings suggest that breeding programs should prioritize the incorporation of vegetative transcriptional traits associated with effective canopy temperature homeostasis into elite genetic backgrounds.
Why it matches plant phenotyping methodsUAV熱画像によるキャノピー温度の非破壊測定をデジタル表現型として用い、乾燥耐性選抜指標として評価しており、表現型取得・解析が研究の主要な構成要素である。
titleINTEGRATING UAV-BASED THERMAL IMAGING, DREB EXPRESSION, AND AGRONOMIC INDICES TO EVALUATE DROUGHT TOLERANCE IN DIVERSE TRITICUM SPECIES
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.
Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。
abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.
A new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight. Transport of water and saccharides from plant stem to fruit cells is computed following biophysical rules. Saccharide fruit sink is based on sugar metabolism, rates of cell division and expansion, and starch and cell wall dynamics. Osmotic and hydraulic potentials in cells and their vacuoles drive water import at given cell-wall extensibility. The interaction of demand and transport determines saccharide flow and biomass. We incorporated physiological responses to temperature, pruning, and plant shading. Existing and new parameters were calibrated with data from fruit heating and fruit pruning experiments of contrasting tomato cultivars. Model validation for different strategies of fruit heating and pruning, and plant shading was successful. Increased fruit temperature was shown to reduce fruit weight, as expected. Growth response to fruit pruning or shading were fully explained by changes in phloem sucrose concentration. Hydraulic conductivity of vascular tissue as well as sucrose and hexose carrier capacities were crucial fruit properties determining sugar flux. Model scenarios on knockdown of sucrose synthase and active hexose uptake affected sugar composition. The model creates an important step towards predicting fruit quality and taste under diverse growth conditions.
Why it matches plant phenotyping methodsトマト果実の細胞動態、糖含量、重量、品質を予測する新規モデルを開発し、複数条件・品種で較正および検証しているため、植物形質推定手法が中心です。
abstractA new tomato fruit model predicts cell numbers, cell sizes, sugar contents, and fresh weight.
ThermalRootGrowth / time-series analysisPlant / canopy temperature
Plants rely on the circadian clock to anticipate daily environmental fluctuations and to coordinate key physiological, metabolic, and developmental processes. Most if not all plant cells have semi-autonomous circadian oscillators. Roots possess a modified yet robust circadian oscillator that is entrained by external cues such as light and temperature to synchronize nutrient uptake, water transport, and metabolic activity. It has been shown that the root and shoot oscillators can communicate through long-distance signals including mobile proteins and carbon assimilates such as sucrose. Moreover, recent studies indicate root-microbe interactions; root-associated microbial communities exhibit diurnal oscillations structured by the host circadian system, while microbes can in turn modulate the circadian period and rhythmic outputs of the plant. However, in general, while the shoot circadian oscillator has been extensively characterized, much less is known about the root circadian system. Progress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity. Existing methods including luciferase reporters, quantitative RT-PCR, and microscopy remain limited by cost, destructive sampling, or require transgenic lines with reporter genes. We have developed a thermal infrared imaging platform that enables non-invasive, high resolution of circadian rhythms in roots across plant species and growth conditions. We show that our system can be used to analyse metabolite and microbial effects on root circadian regulation. This platform provides new opportunities to investigate below-ground circadian regulation and the possibilities of harnessing the root clock to enhance plant performance and resilience.
Why it matches plant phenotyping methods根の概日リズムという植物状態を、非破壊・高解像度の熱赤外画像で測定するプラットフォームを開発しており、フェノタイピング手法が研究の中心である。
abstractProgress has been hampered by a lack of high-throughput, non-invasive methods to study root rhythmicity.
Continuous monitoring of canopy temperature (Tc), a key indicator of crop water-heat stress and physiological dynamics, using unmanned aerial vehicle (UAV) imagery is inherently limited by temporal discontinuity and the limited physical realism of purely data-driven models. This study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products. The model leverages sparse UAV thermal measurements as supervisory signals while integrating them with continuous meteorological forcing and daily UAV-derived crop phenotypic features. Validated through a comprehensive season-long rice field experiment using walk-forward cross-validation, the proposed PINN framework demonstrated superior performance. It achieved R 2 values of 0.92 for reconstruction and 0.90 for forecasting, with RMSE of 0.71 °C and 0.82 °C, respectively. Ablation analysis further showed that crop phenotypic variables contributed more strongly than temporal descriptors, reducing predictive uncertainty by approximately 4.8–14.3 %, while the integration of SEB physical constraints and uncertainty modeling improved R 2 by 8.4–9.5 % and reduced Total STD by 28.4–37.7 %. The model successfully captures diurnal dynamics, spatial variability, and canopy thermal hysteresis while maintaining physical consistency through improved energy closure. This framework bridges sparse aerial observations with continuous physiological monitoring and highlights its potential to support precision irrigation, early stress detection, and high-throughput phenotyping in smart agriculture.
Why it matches plant phenotyping methodsUAV熱画像による疎な観測からイネ群落温度を連続再構成・予測するPINNを開発し、交差検証とアブレーション分析で性能評価しており、表現型取得・推定手法が研究の中心である。
abstractThis study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products.
TomatoMultimodalLeafClassificationStress / disease detectionStress response / tolerancePlant / canopy temperature
Wearable plant sensing systems for simultaneous biochemical and physical monitoring with real-time multimodal data analysis remain limited. Here, we present PhytoClip, a multimodal wearable patch that continuously monitors leaf temperature, humidity, three volatile organic compounds (VOCs) with high selectivity, and microenvironmental light intensity and CO2 concentration. PhytoClip features a bookmark-inspired design for secure attachment to leaves of diverse morphologies, supported by a flexible printed circuit board for data acquisition, wireless communication, and cloud-based monitoring. We develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection. The integrated PhytoClip-PhytoSense platform detects and classifies nine biotic and abiotic stresses in tomato plants with 92% accuracy. Notably, P. infestans on tomato was detected within 15.5 h post-inoculation, earlier than quantitative polymerase chain reaction (qPCR) (~4 days) and visual phenotyping (~7 days), highlighting the potential of integrating multimodal wearable sensing and online ML for precision agriculture.
Why it matches plant phenotyping methods植物の葉に装着するマルチモーダルセンサーとオンラインMLによるストレス・病害状態の取得および分類が研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe develop PhytoSense, an open-source machine learning (ML) framework for sensor importance ranking, multi-stress classification, and early stress detection.
Abstract Canopy temperature (Tc) is a critical physiological indicator of water and heat stress in cotton. Although weather-driven Tc forecasting is used by 60% of Australian cotton growers for irrigation scheduling, current methods typically rely on a single in situ sensor to represent an entire management area. This uniform assumption overlooks substantial spatial variability in Tc and can lead to suboptimal water application. We propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts. Experimental results show that UAV-linear forecast stress conditions at unmeasured locations as effectively as models trained on exhaustive historical data, achieving a 25-35% improvement over the standard uniform-forecast assumption. Furthermore, in a large-scale validation across 50,000 hectares of commercially active farms (practical dataset), UAV-linear improved stress-hour prediction by 22% relative to the uniform assumption while maintaining accuracy comparable to historical benchmarks. These findings show that the proposed spatio-temporal framework provides the spatial detail needed for differentiated precision management, with potential to improve crop yield and water-use efficiency.
Why it matches plant phenotyping methods綿花のキャノピー温度という植物の生理状態を、UAV熱画像とセンサーから推定・予測する手法を開発し、大規模に検証しているため、灌漑管理への応用でもフェノタイピング手法が中心です。
abstractWe propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts.
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
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.
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 · 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 · Crossref · checked 5 Sept 2026
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement.
Why it matches plant phenotyping methods植物フェノタイピング用のマルチセンサーUAVプラットフォームを設計・検証し、植物形質の抽出精度を評価しているため、方法が中心的である。
abstractThis study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems.
Abstract Temperature fundamentally impacts plants growth and physiology. However, the mechanisms by which plants sense and response to environmental changes remain unclear due to the lack of effective methods for measuring internal plant temperatures. Here, by combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature in response to environmental temperature variations. We discovered a multilevel temperature regulation mechanism during the process by which plants establish thermal homeostasis. In Nicotiana benthamiana leaves, when environment temperature changes from approximately 24°C to 45°C, the maximum of internal plant temperature change is only approximately 10°C near cell wall, and less than 7°C in cytoplasm, while remaining nearly constant in chloroplasts (ΔTchl ≈ 1°C). Similar compartment-specific thermal regulation was observed in Arabidopsis thaliana and tomato, indicating that hierarchical regulation represents a conserved strategy for maintaining internal temperature stability in plants. Together, these findings provide direct evidence for multiscale thermal homeostasis in plants and establish a framework for understanding how cellular and subcellular organization contributes to temperature regulation.
Why it matches plant phenotyping methodsナノ温度計プローブと時間ゲート imaging により植物内部温度を測定する手法が研究の中心であり、植物の生理状態を直接定量している。
abstractby combining lab-made nanothermometric probes with time-gated imaging technique, we accurately detected the change in internal plant temperature
RootPhysiological trait estimationPlant / canopy temperature
Wireless temperature monitoring of plant roots remains challenging due to signal dependence on variable sensor-reader distance. To overcome this limitation, this paper proposes a wireless, battery-free RLC-based temperature sensing system incorporating a data-driven distance compensation framework based on a two-stage polynomial regression strategy. A multi-model is proposed to estimate temperature from resistance data, while a fourth-degree polynomial model is used to estimate the sensor-reader distance from self-inductance measurements. The final predicted temperature is obtained by linear interpolation. The model approach is developed using data collected from an inductanceto- digital converter (LDC1101) reader of an RLC sensor with a PT1000. Experimental results show that at a sensor-reader distance of 2 mm, the system achieves a root mean square error (RMSE) of 0.788 °C, with errors normally distributed near zero (σ = 0.705 °C), and an RMSE of 2.163 °C with an error distribution (σ = 1.24 °C) at a distance of 6 mm. This performance corresponds to a reduction in prediction error of up to 94% at short distances and over 80% at larger separations compared to a single global model.
Why it matches plant phenotyping methods植物根の温度という生理状態を測定する無線センサーと距離補償・推定手法を開発し、誤差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す。
abstractthis paper proposes a wireless, battery-free RLC-based temperature sensing system incorporating a data-driven distance compensation framework based on a two-stage polynomial regression strategy.
Sugarcane is a high-value industrial crop vital for sugar and biofuel production, yet increasingly constrained by climate variability, biotic and abiotic stresses, soil degradation, and inefficient input use. Traditional breeding and crop management approaches are often slow, labour-intensive, and less precise, emphasizing the need for digital transformation in sugarcane agriculture. AI now offers powerful tools to accelerate genetic improvement, enhance stress resilience, and optimize resource-use efficiency. This review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems. ML and DL models enable automated, accurate prediction of key traits such as biomass, canopy temperature, nitrogen status, and sugar recovery using UAV, satellite, and proximal sensing data. AI-powered genomic selection approaches leveraging convolutional networks, transformers, and attention mechanisms improve prediction accuracy for yield, ratooning ability, and stress tolerance by integrating SNPs, pedigree, and multi-environment datasets. Emerging innovations such as digital twins, multimodal data fusion, reinforcement learning-based irrigation scheduling, and climate-smart advisory models further strengthen real-time crop intelligence. The integration of blockchain-enabled breeding databases, FAIR data standards, and interoperable analytics pipelines supports scalable and collaborative research. Literature analysis reveals 15-30% gains in selection efficiency, >90% accuracy in disease detection, and phenotyping cost reductions of up to 70%. Key challenges remain, including scarce annotated datasets, genotype × environment complexity, model interpretability, and adoption barriers for smallholders. A future roadmap is proposed featuring multimodal foundation models, edge-AI deployment, and explainable breeder dashboards. AI is redefining sugarcane research from reactive to predictive, enabling climate-resilient, sustainable, and profitable production systems.
Why it matches plant phenotyping methodsサトウキビ育種におけるAI応用の総説であり、高スループット表現型解析、UAV・衛星・近接センシングによる形質推定を主要な対象として扱っているため、フェノタイピング手法レビューとして適格。
abstractThis review synthesizes recent advances in AI applications across the sugarcane improvement pipeline, including high-throughput phenotyping, genomic prediction, digital crop monitoring, and AI-driven decision-support systems.
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.
Evaluating the drivers of variation in plant thermal tolerance limits requires a clearer understanding of how methodological matters can lead to different tolerance estimates. Chlorophyll fluorometry – to measure the temperature-dependent change in F V / F M – is a well-established approach to derive tolerance thresholds of photosystem II (PSII) in plants, but one-off, time-specific thermal exposures do not consider the fundamental dose-dependent effect of heat. The resurgent thermal death time (TDT) approach integrates both the temperature intensity and the exposure duration to derive time-based critical temperature thresholds and sensitivity parameters. We build upon this foundation to develop a protocol for evaluating thermal load sensitivity (TLS; non-lethal heat stress) of PSII in plants. Through five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery. There were dramatic changes in tolerance threshold estimates based on thermal load (i.e. dose-dependent) effects on F V / F M , and strong effects of light intensity during heat and the presence of light post-heat. We offer recommendations pertaining to method implementation and discuss future empirical avenues. Appraising cumulative heat stress will enhance the utility of thermal tolerance estimates – the TLS approach outlined here moves us toward a new standard.
Why it matches plant phenotyping methods植物のPSII熱耐性をクロロフィル蛍光で定量する方法を開発・検証し、実装上の条件を評価した研究であり、方法が中心的です。
abstractThrough five experiments across four diverse species, we tested the moderating effects of light, leaf sectioning, time since collection, and the temporal dynamics of F V / F M recovery.
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.
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
MelonThermalLeafStress / disease detectionDisease symptoms / severityPlant / canopy temperature
Powdery mildew, a disease caused by the biotrophic fungus Podosphaera xanthii, is one of the most destructive diseases affecting melon crops worldwide. This pathogen causes alterations in the physiology of the host plant even before visible symptoms appear, which in turn can be detected using non-invasive imaging techniques. In this piece of work, infrared thermography was used to evaluate the temperature dynamics of melon leaves infected with P. xanthii during the first 72 h after infection. Infected leaves showed a significant decrease in temperature compared to mock-controls from 18.5 hpi onwards, before the appearance of visible mycelium. This temperature difference between mock-control and P. xanthii-infected melon leaves remained significant throughout the experiment, suggesting a sustained disruption of water-balance regulation caused by the fungus. This imbalance could be linked to haustorium-mediated interference with stomatal function or epidermal osmotic homeostasis. Overall, these results highlight thermography as a powerful and sensitive tool for detecting early physiological responses during P. xanthii infection of melon leaves. Therefore, thermography could be used as a valuable complement to ‘omics’ and other image-based phenotyping methods, helping to provide a comprehensive view of the responses that different diseases trigger in host plants.
Why it matches plant phenotyping methodsメロン葉の感染に伴う温度変化を赤外線サーモグラフィーで非侵襲的に測定し、可視症状前の病態・生理状態を評価する方法の適用が中心である。
abstractwhich in turn can be detected using non-invasive imaging techniques
This study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China. Although sensing technologies have advanced considerably, cross-country comparisons between regions with differing levels of technological maturity remain limited. China, recognised for its advanced adoption of UAV and sensor-based agriculture, provides a benchmark against Malaysia’s developing digital agriculture landscape. Each method was assessed based on accuracy, responsiveness, scalability, and cost-effectiveness under field conditions. UAV-based remote sensing achieved the highest overall accuracy (mean 92%) and demonstrated superior scalability, enabling rapid large-area monitoring using vegetation indices such as NDVI and NDRE. Ground-based sensors, including soil moisture probes and chlorophyll meters, showed moderate accuracy (mean 81%) and were suitable for plot-level monitoring with real-time feedback. Manual inspection recorded the lowest accuracy (mean 68%) and limited scalability due to labour dependency and subjective assessment. UAV methods were particularly effective in early stress detection, with thermal imaging identifying canopy temperature anomalies 3–5 days before visible symptoms, especially in maize and coconut fields. Integrating UAV and ground-based sensing provided more comprehensive and timely assessments than individual approaches. These findings support the development of scalable precision agriculture frameworks tailored to tropical and subtropical systems.
Why it matches plant phenotyping methods作物ストレスという植物状態を対象に、手動観察・地上センサー・UAVリモートセンシングを精度、応答性、拡張性、費用で比較評価しており、センシング手法の技術評価が中心である。
abstractThis study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China.
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
MultimodalFruitLeafClassificationObject detectionPhysiological trait estimationGrowth / development / phenologyFruit / seed / panicle traitsPlant / canopy temperature
ABSTRACT Plant‐wearable sensors are essential for real‐time, in situ monitoring in smart agriculture, yet their adoption is constrained by functional simplicity and inadequate mechanical compliance. Herein, we present a conformable plant‐attachable Janus electronic skin (SPM/HMA@P) for multimodal phenotyping. Featuring a scenario‐specific encapsulation strategy fabricated via a bottom‐up approach, the device enables non‐invasive monitoring of multiple physiological parameters. The sensor integrates a conductive base composed of biocompatible quaternized chitosan, multi‐walled carbon nanotubes, and silver nanowires, a gas‐sensing functional layer, and an adhesive polydimethylsiloxane encapsulation. In the fully encapsulated configuration, SPM/HMA@P exhibits a 210.9% fracture elongation and an adhesion force exceeding 0.3 N on leaves, alongside excellent biocompatibility. This configuration allows for simultaneous monitoring of leaf temperature and growth‐induced strain, maintaining stable signals over 5 days. Conversely, the semi‐open configuration demonstrates high ethylene sensitivity (0.5–100 ppm, theoretical detection limit of 0.12 ppm), with response and recovery times of 300 and 120 s, and a lifespan of over 10 days. Coupled with a convolutional neural network (CNN), it achieves 96.8% accuracy in classifying ethylene signals from fruits. This robust multimodal platform addresses key challenges in plant physiological monitoring, holding great potential for smart crop breeding, postharvest assessment, and phenotyping.
Why it matches plant phenotyping methods植物に装着するマルチモーダルセンサーを開発し、葉温度・成長誘導ひずみ・エチレンなどの生理状態を非侵襲的に測定するプラットフォームであり、植物フェノタイピング手法が中心的に扱われている。
titleConformable Plant‐Attachable Janus E‐Skin with Sandwich Architecture for Plant Multimodal Phenotyping
Aim of study: To identify different levels of devitalization in individuals of Abies religiosa (Kunth) Schltdl. & Cham. (oyamel) in the core zone of the Monarch Butterfly Biosphere Reserve (MBBR), using multispectral and thermal information captured with drone. Area of study: The study area, comprising 26 ha, is located within the federal property of the MBBR. This area also belongs to the core zone of the Protected Natural Area. Material and methods: Overflights were conducted with drones equipped with multispectral and thermographic sensors in monitoring plots. Using high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated. These values were combined with field data to determine three different levels of tree devitalization (i.e., healthy, devitalized, and dead trees). Main results: The results demonstrated that this technology can be used to statistically distinguish the different levels of devitalization in oyamel individuals. Research highlights: This is the first research in Mexico that use Thermal and Multiespectral high resolution information related with field data applied to Abies religiosa and provides a methodological precedent for identify forest decline symptoms.
Why it matches plant phenotyping methodsドローン搭載のマルチスペクトル・熱赤外センサーと画像解析で樹木の活力度を推定し、健全・衰弱・枯死を識別する手法が研究の中心である。
abstractUsing high-resolution images captured by drone and photogrammetric processing, the normalized difference vegetation index (NDVI), surface temperature (ST), and temperature-vegetation dryness index (TVDI) were calculated.
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Heat stress damage leads to yield penalties in many wheat-growing areas. Climate change models predict warmer scenarios and more frequent heat shocks. Consequently, wheat breeders need to develop more productive varieties for warm conditions, and therefore, the identification of heat-tolerance traits is needed. Albedo is an integrative trait of the optical properties of the canopy defined as the ratio of reflected light to total light received. High albedos in warm conditions may help reduce damaging radiation. Despite its potential relevance for heat avoidance, albedo has been little explored in wheat breeding. In this work, a selection of 30 wheat ( Triticum aestivum L.) genotypes of diverse origin were sown at two sowing dates in Australia (NSW) in 2018 and 2019. A high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed to explore its relationship with temperature and other heat tolerance-related traits. ASD albedo was validated via continuous albedometer measurements on a subset of genotypes. Data were captured at flowering (one of the most critical periods for heat-related damage). Genotypic differences for albedo were found in most environments. However, genotypic effects were most noticeable at noon in optimally sown materials (H 2 0.71–0.86). Albedo was directly related to canopy architecture and light interception (r = 0.74) and varied depending on genotype and genotype by environment interaction. Air temperature in the canopy profile and canopy temperature (CT) were also monitored continuously in a subset of genotypes to explore the relationship between albedo and canopy micrometeorology. Canopies with higher albedos had larger air temperature differences across the canopy profile at the flowering stage (r = 0.48). However, canopy temperature was not related to albedo, even though it was strongly correlated (r = 0.99) with air temperature around the spike. Overall, these results indicate that canopy architecture is the primary influence on albedo under warm conditions. Although higher albedo was not associated with lower canopy temperature, its influence on canopy micrometeorology suggests that albedo may contribute to heat avoidance and could therefore be considered an additive trait for phenotyping and breeding for environments under high temperatures.
Why it matches plant phenotyping methods小麦群落アルベドを分光放射計で測定するハイスループット表現型計測法を開発し、連続アルベドメーターで検証しているため、方法が研究の中心である。
abstractA high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed
Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.
Why it matches plant phenotyping methodsUAV画像・センサーによる植物形質計測を中心に扱う明示的なフェノタイピングレビューであり、手法の応用、技術、課題を総合的に論じている。
abstractDrone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement.
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 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.
Abstract We present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping that quantifies dynamic decoupling between a thermal channel (e.g., leaf tem-perature proxy ∆T or canopy temperature) and a photochemical channel (e.g., ΦPSII, NPQ, or fluorescence-derived yields). The core measurement is a signed loop-area in a phase planespanned by the two signals under a symmetric perturbation (light or VPD ramp; option-ally sinusoidal forcing). We formalize this as the Sakib Thermo-Photochemical Hys-teresis Index (Sakib-Index) and provide mathematically grounded normalizations: the Sakib Coupling Coefficient (SCC) and the Sakib Phase-Lag Constant (SPLC). We show how loop area connects to phase-lag for periodic forcing and propose minimalcomputational checks for robustness (closure, sampling invariance, and directionality). Tendata-based illustrations are generated from open-access plant datasets (tomato chlorophyllfluorescence/reflectance and cottonwood leaf-temperature microclimate records), plus sixconceptual diagrams clarifying the assay pipeline.
Why it matches plant phenotyping methods植物ストレスの熱・光化学シグナルから新たな定量表現型を抽出する測定プロトコルと計算指標を中心に提案しており、方法開発に該当する。
abstractWe present a protocol-defined, scalar, cross-modal hysteresis phenotype for plant stress phenotyping
Multispectral / hyperspectralFlowerObject detectionStress response / tolerancePlant / canopy temperature
Hyperspectral imaging (HSI) is a noncontact camera-based technique that enables deep learning models to learn various plant conditions by detecting light reflectance under illumination. In this study, we investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses, which are vulnerable to abiotic stresses. Cut roses 'All For Love' and 'White Beauty' were used to compare cultivar-specific visible reflectance characteristics associated with contrasting petal pigmentation. HSI was performed at four time points, yielding 640 images per light source from 40 cut roses. The results revealed that the light source strongly affected both the image quality (mAP@0.5 60-80%) and VL (0-3 d) of cut roses. The HAL lamp produced high-quality spectral images across wavelengths (WL) ranging from 480 to 900 nm and yielded the highest object detection performance (ODP), reaching mAP@0.5 of 85% in 'All For Love' and 83% in 'White Beauty' with the YOLOv11x models. However, it increased petal temperature by 2.7-3 °C, thereby stimulating leaf transpiration and consequently shortening the VL of the flowers by 1-2.5 d. In contrast, INC produced unclear images with low spectral signals throughout the WL and consequently resulted in lower ODP, with mAP@0.5 of 74% and 69% in 'All For Love' and 'White Beauty', respectively. The INC only slightly increased petal temperature (1.2-1.3 °C) and shortened the VL by 1 d in the both cultivars. Although FLU and LED had only minor effects on petal temperature and VL, these illuminations generated transient spectral peaks in the WL range of 480-620 nm, resulting in decreased ODP (mAP@0.5 60-75%). Our results revealed that HAL provided reliable, high-quality spectral image data and high object detection accuracy, but simultaneously had negative effects on flower quality. Our findings suggest an alternative two-phase approach for illumination applications that uses HAL during the initial exploration of spectra corresponding to specific symptoms of interest, followed by LED for routine plant monitoring. Optimizing illumination in HSI will improve the accuracy of deep learning-based prediction and thereby contribute to the development of an automated quality sorting system that is urgently required in the cut flower industry.
Why it matches plant phenotyping methods切り花の状態評価に用いるHSIについて、照明条件が画像品質と検出精度に及ぼす影響を比較・検証し、実運用向けの照明戦略を提案しているため、植物フェノタイピング手法が中心である。
abstractwe investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses
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 · publicSupplementary Table S1: SNR of hyperspectral images acquired under different illumination sources in two cut rose cultivars (‘All For Love’ and ‘White Beauty’); Figure S1: Effect of light sources on hyperspectral image (HSi) quality in cut roses ‘All For Love’ and ‘White Beauty’.Open asset ↗lines:64-174Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.
Why it matches plant phenotyping methods植物ストレスを対象としたマルチモーダル画像・分光・熱・蛍光センシングと機械学習による表現型抽出を中心に扱う方法レビューであり、植物フェノタイピング手法の範囲に明確に該当する。
titlePlant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.
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
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
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.
LiDAR / point cloudThermalFruitGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traitsPlant / canopy temperature
Temperature plays a vital role in plant metabolism, and effective crop temperature appears to be influenced by variables related to climate change. While extreme weather events are widely discussed, the effects of moderate temperature changes pose consistent yet underexplored challenges for farmers. The “growing degree days” (GDD) also termed “heat unit”, is the most widely used approach in agricultural and ecological studies to quantify the relationship between temperature and plant development. This review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest. It is the first integrated synthesis of the conceptual evolution, methodological refinement, and broad application of GDD, thereby highlighting the need to optimize GDD approaches in light of emerging technological tools. While the GDD model is valuable for predicting crop development based on heat accumulation, it has limitations in capturing the effects of other environmental factors. Additionally, air temperature may not provide precise data on each plant organ. Recent advances in remote sensing, such as the integration of thermal imaging, RGB cameras, and lidar have enabled the measurement of spatially resolved temperature distribution within crop canopies, including fruit surface temperature. Recent advances, highlighted in the literature, suggest that integrating sensor innovations with machine learning approaches holds high potential for improving the precision of modeling temperature-dependent growth responses and their interactions with other environmental variables. By addressing these challenges and expanding its applications, GDD can continue to serve as an essential tool in promoting sustainable horticultural practices and adapting to global warming.
Why it matches plant phenotyping methodsGDDを用いて温度から作物の発育段階・成熟を推定する方法論を中心にレビューしており、植物状態の計算的な表現型推定に該当する。
abstractThis review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest.
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
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The 3D heterogeneity in nitrogen content and temperature within the canopy affects canopy photosynthesis. Currently, there are no methods for efficiently assessing the heterogeneous 3D-distribution of leaf nitrogen content and leaf temperature and integrating that information into a 3D model of canopy photosynthesis. We therefore developed a high-throughput pipeline for collecting canopy photosynthesis parameters in maize (Zea mays) by combining several innovations. First, we used readily obtained SPAD502Plus meter readings to infer local leaf nitrogen content. Second, a Bayesian inference method allowed us to parameterize a C4 leaf photosynthesis model. Third, we used neural radiance fields (NeRFs) to recreate 3D plant architecture and SPAD distribution. Finally, we developed an indoor ray tracing and energy balance model to estimate local light distribution and leaf temperature within a canopy. SPAD values showed a distinct 3D pattern, suggesting within-canopy variation in photosynthesis. Bayesian inference efficiently parameterized the C4 leaf photosynthesis model, with estimated parameter values correlating well with SPAD values. In addition, NeRF more accurately reconstructed 3D architecture and estimated 3D SPAD distribution than traditional methods. This resulted in calculated leaf temperatures being similar to measured values. Different model assumptions can cause significant differences in simulated canopy photosynthetic rate. Omitting 3D SPAD heterogeneity alone produced a 1% to 8% difference in simulated canopy photosynthetic rate. Ignoring leaf temperature heterogeneity led to a difference in the calculated canopy photosynthetic rate of only 1% to 3% near the optimal temperature, but of up to 38% at 35 °C. This pipeline can be realized by high-throughput phenotyping platforms, making it suitable for exploring genetic differences and optimizing ideotype design for improved canopy photosynthesis.
Why it matches plant phenotyping methodsトウモロコシ群落の葉窒素・温度・光合成関連形質を3Dで取得・推定する高スループットパイプラインを開発しており、表現型取得手法が研究の中心である。
abstractWe therefore developed a high-throughput pipeline for collecting canopy photosynthesis parameters in maize (Zea mays) by combining several innovations.
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.
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
Introduction Accurate assessment of sugarcane leaf disease severity is crucial for early warning and effective disease control. Methods In this study, we propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits. Field-collected data-including Soil and Plant Analyzer Development (SPAD) values, leaf surface temperature, and nitrogen content-were acquired using a plant nutrient analyzer (TYS-4N) from sugarcane leaves infected with brown stripe disease, ring spot disease, and mosaic disease at four severity levels (mild, moderate, moderately severe, and severe). After min-max normalization, six classification models-KNN, AdaBoost, Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and XGBoost-were developed, and the Sparrow Search Algorithm (SSA) was employed to optimize hyperparameters for enhanced performance. Results Results demonstrate that SSA significantly improved the classification capability of all models. The SSA-XGBoost model achieved the best performance, with Precision, Recall, F1 Score, and Accuracy all exceeding 0.9186, and a comprehensive PRFA score of 0.9326. When validated on an independent dataset from Gengma County, the model achieved an overall accuracy of 0.91, indicating strong generalization ability and field applicability. Discussion Compared to image-based deep learning approaches, the proposed method offers advantages in terms of data accessibility, computational efficiency, and model transparency, making it well-suited for rapid on-site diagnosis in agricultural settings. This study provides an efficient and reliable technical framework for intelligent diagnosis and early warning of sugarcane disease severity.
Why it matches plant phenotyping methods植物の生理形質から葉病害の重症度を推定する分類手法を開発し、独立データセットで検証しているため、植物フェノタイピング手法が中心である。
abstractwe propose an intelligent method for identifying sugarcane foliar disease severity based on physiological traits.
Infrared thermal imaging offers a rapid and sensitive approach to assessing temperature changes in plants caused by salt stress, even in the early stages of exposure. Given the increasing prevalence of salt contamination in the environment, it is essential to accurately estimate salinity levels, as the effects strongly depend on salt concentration: moderate salinity elicits a reversible, osmotic driven rise in leaf temperature, whereas higher salinity induces a larger, sustained temperature increase indicative of ion toxicity related stress. We propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions. Thermal measurements are then used to train machine learning models used to perform multi-class classification to distinguish between four different salt concentrations. To test the proposed method, we cultivated Arabidopsis thaliana plants under controlled conditions. Data collected from the prepared samples were used to assess the accuracy of various approaches and classifiers with lead-one-out cross-validation. This experimental evaluation shows that the optimal performance is achieved when the datasets used for training consist of longer sequences of thermal data provided to models using neural networks.
Why it matches plant phenotyping methods植物の熱画像から塩ストレスの重症度を推定・分類する画像計測と機械学習手法が研究の中心であり、植物状態の表現型取得・推定に該当する。
abstractWe propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate-resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought-heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches-genomics, transcriptomics, proteomics, and metabolomics-to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA-SnRK2 signaling cascade, HSF-HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This "pixels-to-proteins" paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties.
Why it matches plant phenotyping methodsリモートセンシング・高スループット表現型解析プラットフォームを中心に、作物のストレス関連形質測定とマルチオミクス統合をレビューしており、フェノタイピング手法が中核である。
titleMulti-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought–Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture
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.
Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape (Brassica napus), and analyzed using MATLAB 2021b to quantify NDI, ExG, Fd/Fₘ, and plant leaf temperature. NDI, ExG and Fd/Fₘ decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in Fd/Fₘ, while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 h enabled the diagnosis of herbicide MOAs with 89.6 % accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 %. The indices acquired at 6 h, and Fd/Fₘ and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 %, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.
Why it matches plant phenotyping methods植物のスペクトル画像から葉の生理状態・温度指標を抽出し、機械学習で除草剤作用機構を診断する手法の開発と精度検証が研究の中心であるため、植物フェノタイピング手法として採用。
abstractthis study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).
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.
Drought priming represents a potential strategy to bolster wheat yields in the face of recurring droughts, and there is a need to identify responsive cultivars and decipher the underlying mechanisms of priming. Here, the responses of 157 wheat cultivars to drought-priming were phenotyped using a high-throughput phenotyping (HTP) platform across two growing seasons, and a drought priming index (DPI) was devised to assess the priming sensitivity for each cultivar. A DPI comprehensive score (DPICS) was derived from 13 sensitive traits identified by principal component analysis, and significant variations in this score led to the classification of the cultivars into two distinct groups, one sensitive to drought priming and one not. The sensitive group contained 58 cultivars that had higher DPI values for traits including yield components, harvest index, post-anthesis assimilation, photochemical efficiency, canopy coverage, and normalized difference vegetation index, and lower DPI values for traits including remobilization of dry matter stored pre-anthesis, non-photochemical quenching, plant senescence reflectance index, and canopy temperature. A genome-wide association study (GWAS) based on the DPI identified 499 significant markers related to drought priming using a commercially Wheat660 SNP array. Notably, one marker situated on chromosome 5B consistently appeared in both the growing seasons that were studied. This marker resides within a 261.2 kb genomic block containing seven genes, including the candidate gene TraesCS5B03G1259700, which exhibited distinct transcriptional memory related to drought priming. Our results suggest that integrating HTP and GWAS has great potential for deciphering the genetic basis of acquired drought tolerance induced by priming and could facilitate the breeding of improved wheat varieties that can respond to recurring drought events.
Why it matches plant phenotyping methodsHTPプラットフォームを用いた多形質の取得と、乾燥プライミング感受性指標（DPI）の開発・適用が研究の中心であり、植物表現型解析手法の実質的な応用に該当する。
abstractthe responses of 157 wheat cultivars to drought-priming were phenotyped using a high-throughput phenotyping (HTP) platform across two growing seasons
Traditional methods of early diagnosis of diseases, such as pure culture method, microscopic, mycological, polymerase chain reaction, enzyme immunoassay are invasive and require highly qualified personnel, expensive equipment and are not suitable for their effective use in practice. Since plant health is a fundamental indicator in assessing the phenotype of a crop plant, modern non-invasive methods for early diagnosis and determination of plant phenotype are considered. The purpose of the research is to select a rational method for early diagnosis of plant diseases and determination of their phenotype directly in the field of cultivated crops. Advantages and disadvantages of the vision method based on the analysis of the changes in color parameters of RGB images of plant leaves; fluorescence analysis, in which the efficiency of photosynthesis is estimated; multispectral and hyperspectral imaging methods carried out by determining the limited or continuous spectrum reflected from the surface of plant leaves; thermal imaging method in which the distribution of infrared radiation emitted by the plant is recorded. The analysis of the methods showed that the determination of thermal energy dissipation is a promising potential indicator of health and the presence of disease. In addition, when exposed to most environmental factors, the thermal properties of plant organs, such as leaf, stem, root, and reproductive organs, change. The reason for the limited use of thermometry in the early diagnosis of plant diseases is explained: false rejection by researchers of the fact that it is a highly organized complex of terrestrial and underground organisms. The requirements for devices for obtaining and processing thermal images are formulated and justified. An experimental setup based on the TE-Q1 thermal imaging camera, capable of working with Android devices, has been developed. Its operation has been tested on garden strawberry samples.
Why it matches plant phenotyping methods植物病害の表現型を非侵襲的に取得する画像・熱画像手法を比較検討し、熱画像取得装置を開発・検証しており、フェノタイピング手法が中心である。
abstractmodern non-invasive methods for early diagnosis and determination of plant phenotype are considered
Abstract Hand-held or vehicle-mounted active proximal sensing technologies offer a rapid, non-destructive method for real-time crop monitoring through spectral vegetation indices. This study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction. Specifically, wheat yield is predicted using normalized difference vegetation indices (NDVIs), canopy temperatures (CTs), and plant height (PH) through a deep neural network (DNN) optimized using a genetic algorithm (GA). The model is trained on data from 3,350 diverse wheat germplasm grown under irrigated and rainfed conditions at two locations during the 2020–21 winter season. Comparative analysis demonstrates that the GA-optimized DNN outperforms traditional machine learning models such as Random Forest Regression (RFR), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Regression (SVR). Among individual feature groups, NDVIs measured at five wheat growth stages showing strong predictive capability, with R² values ≥60% under irrigated and ≥50% under rainfed conditions. Additionally, RFR is employed to identify the most influential features within each group. This pioneering study introduces the first-ever application of a GA-optimized deep neural network, leveraging handheld or vehicle-mounted proximal sensing data for predicting crop yield, in the context of Indian agriculture. The proposed approach offers a robust and scalable solution for pre-harvest yield estimation, supporting breeders and researchers in efficient genotype selection and contributing to the achievement of sustainable development goals.
Why it matches plant phenotyping methods近接センシングによるNDVI・群落温度・草丈を用いた小麦収量推定手法を開発し、複数モデルと比較検証している。収量という植物形質の取得・推定が研究の中心である。
abstractThis study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Technological advances have made drones (UAVs) increasingly important tools for the collection of trait data in plant science. Many costs for the analysis of plant populations have dropped precipitously in recent decades, particularly for genetic sequencing. Similarly, hardware advances have made it increasingly simple and practical to capture drone imagery of plant populations. However, converting this imagery into high-precision and high-throughput tabular data has become a major bottleneck in plant science. Here, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types. Methods can be flexibly combined to extract data related to canopy temperature, area, height, volume, vegetation indices, and summary statistics derived from complex segmentations and classifications including using methods based on artificial intelligence (AI), computer vision, and machine learning. We then describe educational and training resources for these methods, including a web page (PlantScienceDroneMethods.github.io) and an educational YouTube channel (https://www.youtube.com/@travisparkerplantscience) with step-by-step protocols, example data, and example scripts for the whole drone data processing pipeline. These resources facilitate the extraction of high-throughput and high-precision phenomic data, removing barriers to the phenomic analysis of large plant populations.
Why it matches plant phenotyping methods植物集団のドローン画像から温度、面積、高さ、体積、植生指数などの形質を抽出する高スループット手法と、処理パイプライン・教育資源を体系的に説明しており、植物フェノタイピング手法が中心である。
abstractHere, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types.
Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector.
Why it matches plant phenotyping methodsUAV熱画像から温度分布とCrop Water Stress Indexを抽出するMATLAB手法を開発・提示しており、植物の水ストレス状態を定量化する方法が中心である。
abstractThis paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone.
The brown planthoppers (BPHs) are serious pests of rice in Southeast Asia which often cause a heavy loss of rice. Monitoring BPH populations and prediction of their damages to rice are more important for the precious control of this pest. Nowadays, highly efficient monitoring and predicting methods for BPHs are still rare. Here, the canopy temperatures of rice damaged by different number of BPHs were examined using the thermal imaging technique, and relationships between canopy temperature and population size of BPHs or rice yield were analyzed. The result showed that there was a significant and stable correlation between rice canopy temperatures and BPH population sizes on these rice plants in four consecutive years of plot experiments. Further, canopy temperatures of rice measured at 8:00–10:00 am were negatively related to the population size of BPHs, but not the temperature measured at noon and in the afternoon. Canopy temperatures of rice at booting and heading growth periods were also strongly related to the rice yield and its loss rate damaged by BPHs. Based on the difference between air temperature and mean canopy temperature of rice, the BPH population size could be monitored using an exponential function model, and the rice yield could be predicted by a linear model. A promising framework of automatic monitoring BPH populations was developed based on canopy temperatures of rice.
Why it matches plant phenotyping methodsイネの熱画像によるキャノピー温度から害虫個体数と被害状態を推定する測定・予測手法を開発しており、表現型取得が研究の中心である。
abstractBased on the difference between air temperature and mean canopy temperature of rice, the BPH population size could be monitored using an exponential function model, and the rice yield could be predicted by a linear model.
The radiometric temperature of the plants is known to be a good indicator of their level of water stress. The use of thermal cameras on board UAVs allows operational monitoring of the canopy temperature in orchard plantations at the single-tree level. The radiometric processing of the flight data becomes critical in this task to maintain the accuracy provided by field measurements using proximal thermal radiometers. This work focuses on evaluating the Crop Water Stress Index (CWSI) as a good indicator of the plant water status in almond orchards. This study compares the performance of CWSI by three different techniques: i) using proximal high-precision thermal radiometry (CWSI_CIMEL); ii) by UAV thermal flights for canopy temperature assessment (CWSI_UAV) and iii) combining multispectral and thermal data, also by UAV, to run a simplified two-source surface energy balance for the traditional formulation of the CWSI in terms of canopy transpiration (CWSI_STSEB). This study was conducted on two commercials almonds (Prunus dulcis (Mill.) D.A. Webb) orchards located in Albacete (SE Spain), one of them with 3 irrigation treatments (well-watered, moderate water stress, and severe water stress). Periodic measurements of stem water potential (SWP) were carried out around noon throughout 3 experimental campaigns from 2019 to 2021. Canopy temperature measurements were made with a high-precision thermal radiometer, the CIMEL CE312-C2. In addition, six flights were carried out using a DJI-M600 drone equipped with a FLIR Tau2 thermal sensor and a Micasense RedEdge camera. Maps of the CWSI were performed during these dates, showing temporal and spatial variability. The three different techniques showed similar CWSI trends across dates and treatments. When treatments were pooled within the same date, the assessment with SWP measurements showed correlations (R²) of 0.86, 0.68, and 0.70 for CWSI_CIMEL, CWSI_UAV, and CWSI_STSEB, respectively. These results reinforce the potential of accurate measurements of radiometric canopy temperatures using both proximal and remote sensing techniques to reproduce the crop water status in almond orchards. However, this study points to the necessity for accurate sensor calibrations and an appropriate methodology for the treatment of both canopy temperature and meteorological data. Monitoring CWSI serves as an operational tool for the early detection of water deficits in almond trees and meets farmerś needs to improve water use efficiency and optimize irrigation scheduling at the plot level.
Why it matches plant phenotyping methodsアーモンド樹の水ストレス状態を示すCWSIを、近接熱放射計およびUAV搭載熱・マルチスペクトルセンサーで取得・比較し、SWPとの相関で技術評価しているため、植物フェノタイピング手法が中心である。
abstractThis work focuses on evaluating the Crop Water Stress Index (CWSI) as a good indicator of the plant water status in almond orchards.
Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies.
Why it matches plant phenotyping methods熱・マルチスペクトル画像と気象センサーを統合し、作物の水ストレス状態（crop water stress index）を推定する取得・推定システムが研究の中心であり、フィールドおよび衛星データとの相関検証も行っている。
abstractaiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery.
Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T C ) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T C . Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T r -3T C ), derived from T C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T r -3T C . The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H 2 O m -2 s -1 and 0.555 mmol H 2 O m -2 s -1 for the respective years, indicating strong interannual stability.
Why it matches plant phenotyping methodsUAV熱画像のセグメンテーションと改良3温度モデルにより、トウモロコシの蒸散速度という生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中心である。
abstractThe Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T C .
Common beanGreenhouseThermalTissueStress / disease detectionDisease symptoms / severityPlant / canopy temperature
Abstract The common bean ( Phaseolus vulgaris L.) is of great socioeconomic importance in Brazil, being widely cultivated by family farmers who preserve traditional varieties adapted to regional conditions. These varieties represent a strategic source of genetic variability for breeding programs. Among the main phytosanitary obstacles to cultivation, common bacterial blight (CBB), caused by Xanthomonas phaseoli pv. phaseoli stands out as it compromises bean productivity. This study aimed to evaluate 54 traditional genotypes for resistance to CBC, using visual severity scales and infrared thermography as a complementary tool. The experiment was carried out in a greenhouse, in a randomized block design with three replicates, in two seasons (May and October 2019). Inoculation was performed by two methods (cutting with scissors at 10⁷ CFU·mL -1 and infiltration with a syringe at 10⁶ CFU·mL -1 ) with the strain Xpp ‘139-y’. The variables analyzed included area under the disease progress curve (AUDPC), incubation period (IP), and final score (FS). Thermal images were obtained up to three days after inoculation, allowing the calculation of the mean temperature difference (MTD) between healthy and infected tissues. Thermographic analysis enabled early detection of infection, before the appearance of visual symptoms, distinguishing resistant genotypes such as BAC-6 and UENF 2599. The results highlight the potential of thermography as a fast, accurate, and non-destructive method for selecting resistant genotypes, contributing to the modernization and sustainability of bean breeding programs.
Why it matches plant phenotyping methods赤外線サーモグラフィーで感染植物組織の温度差を測定し、視覚症状前の病害状態を推定する方法を、抵抗性選抜へ実質的に適用しているため含める。
abstractusing visual severity scales and infrared thermography as a complementary tool
The uneven spatial and temporal distribution of precipitation poses significant challenges to the growth and development of winter wheat. Screening drought-resistant and water-saving winter wheat varieties in water-limited regions is crucial for increasing crop production. However, quickly screening suitable cultivars remains a challenge. Utilizing unmanned aerial vehicles (UAVs) for remote sensing (RS) offers a solution by enabling the prediction of yields, overcoming issues such as the labor-intensive process of manual yield data collection and the difficulty of screening during the growing season. In this study, three types of water treatments were applied to 48 varieties screened in the North China Plain, with each water treatment repeated three times using a randomized block design. The aim is to explore the potential of UAVs for non-destructive yield prediction at various crop growth stages by integrating UAVs-based RS with machine learning, while also screening for drought-resistant and water-saving variety based on predicted yields, actual evapotranspiration (ET) derived from soil water balance and water use efficiency (WUE) at grain yield level. The results indicate that the random forest regression (RFR) model achieved the best prediction results. The optimal data combination of RS, canopy temperature, and data of variety by using RFR yielded the highest coefficient of determination (R²). Additionally, the RFR performs best when using data from the mid-filling stage (single-stage data) and the entire growth stage data (multi-stage data), with R² 0.58 and 0.69, respectively. Among the varieties, Malan 1 and Jimai 765 ranked first and second in both predicted and measured yield assessments, indicating the reliability of the yield prediction model for top-performing varieties. By combining predicted yields from RFR with ET, the screening results demonstrated high consistency between predicted and measured yields. Notably, even yield prediction models with lower R² can still provide satisfactory screening results. These findings will contribute to screening drought-resistant and water-saving winter wheat varieties by UAV. This research accelerates the variety screening process and addresses the conflict between agricultural production and water scarcity in the North China Plain.
Why it matches plant phenotyping methodsUAVリモートセンシングと機械学習による冬コムギの収量予測を中心的に開発・評価し、予測収量を品種スクリーニングに利用しているため、植物フェノタイピング手法に該当する。
abstractThe aim is to explore the potential of UAVs for non-destructive yield prediction at various crop growth stages by integrating UAVs-based RS with machine learning
ThermalPhysiological trait estimationPlant / canopy temperature
Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m²·sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUST-DART is distributed together with DART (https://dart.omp.eu).
Why it matches plant phenotyping methods植物を含む混合画素から温度・放射率という生理状態関連の形質を抽出する熱赤外リモートセンシング手法を開発し、植生シーンで精度検証しているため、都市監視用途を含むが方法論的中心性が高い。
abstractWe propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas.
GreenhouseThermalLeafPhysiological trait estimationStress response / tolerancePlant / canopy temperature
ABSTRACT Extreme heat can push plants beyond their thermal safety margin ( TSM ) if maximum leaf temperature ( T leaf_max ) exceeds leaf critical temperature ( T crit ). The TSM is potentially useful for assessing heat vulnerability across species but needs further validation, so we exposed 50 tree/shrub species in controlled glasshouses to a 6‐day heatwave (peak air temperature = 41°C). Many species increased their mean T crit during the heatwave (42%), with Δ T crit ranging from +1°C to 4°C, but other species did not acclimate or were impaired by heat stress (58%). Species T leaf_max explained ~55% of the variation in species T crit and was a key correlate of the plasticity of T crit among species. Species with high Δ T crit also had higher Δ T leaf_max , with leaves being 7°‒12°C hotter during the heatwave than under baseline conditions. Both T leaf_max and TSMs were correlated with heatwave damage across diverse species from contrasting climate zones. Species differences in TSMs were stable across measurement temperatures, correctly identified the most vulnerable species, and were strongly associated with T leaf_max . Our results suggest that (1) T leaf_max alone is more informative than T crit for ranking species heat tolerance, and (2) species vulnerability to heatwaves is most reliably assessed by using TSMs that integrate T leaf_max with T crit across species.
Why it matches plant phenotyping methods葉温・熱安全余裕度（TSM）を用いた植物の熱脆弱性評価手法を、多種の植物で検証し、損傷予測性能や種間比較の妥当性を評価しているため、方法的役割が中心である。
abstractThe TSM is potentially useful for assessing heat vulnerability across species but needs further validation
Reproduction assets foundThe article's Data Availability Statement explicitly states the supporting data (phenotype measurements: Tcrit, Tleaf_max, TSM, damage indicators for 50 species) are openly available on Figshare at the authors' public DOI, which is an allowed URL.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.29345549.v1 .Open asset ↗Figshare · 10.6084/m9.figshare.29345549.v1lines:721-817Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring plant physiology. However, challenges remain in identifying direct physiological indicators of plant stresses, hindering the development of accurate diagnostic models for predicting symptom progression. Here, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes. MapS-Wear continuously tracks leaf surrounding temperature, humidity, and unique in-situ transmission spectra, which are critical stress-related indicators. The machine learning framework processes these multimodal data to predict gradual stress progression and diagnose nutrient deficiencies in plants over 10 days earlier than conventional computer vision methods. Moreover, MapS-Wears enables portable and large-scale screening of grafted tomato varieties in greenhouses, accelerating the identification of compatible grafting combinations. This demonstration highlights the potential for high-throughput plant phenotyping and yield improvement.
Why it matches plant phenotyping methods植物ストレスの生理状態を連続センシングし、機械学習で早期診断・進行予測するウェアラブル計測システムが研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractHere, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes.
Reproduction assets foundThe paper's Data and materials availability statement explicitly deposits the tomato leaf photos, transmission spectral data, and ML algorithms on Zenodo, matching an allowed URL.Dataset · publicThe photos of tomato leaves in different health statuses, the transmission spectral data of these leaves, and the ML algorithms are openly available on Zenodo ( https://zenodo.org/doi/10.5281/zenodo.15192884 ).Open asset ↗Zenodo · 10.5281/zenodo.15192884lines:129-274Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Phenotyping is critical in tree breeding, but traditional methods are often labour-intensive and not easily scalable. Resistance to biotic and abiotic stress is a key focus in tree breeding programmes. While heritable traits derived from spectral remote sensing have been identified in trees, their application to tree phenotyping remains unexplored. This study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don. Methods Using UAV-based hyperspectral and thermal imaging during a severe DNB outbreak in a clonal trial in New Zealand, we computed narrow-band hyperspectral indices (NBHIs), canopy temperature indices, radiative transfer inverted plant traits, and solar-induced fluorescence. Visual severity scores and remote sensing indices were modelled using spatially explicit mixed-effect linear models integrating pedigree and genomic data in a single-step genomic evaluation. Multi-trait models and sampling simulations were used to evaluate the potential of remote sensing indices to supplement or replace traditional phenotyping. Results Remote sensing indices exhibited narrow-sense heritability values comparable to severity scores (up to 0.37) and high absolute correlation coefficients with severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs were the most informative, reflecting physiological impacts of DNB. Combining partial visual scoring with NBHIs maintained high estimated breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy (0.59) at 20% scoring. EBV correlation with full scoring was above 0.8 even at 20% scoring. Using solely the most heritable NBHI achieved 0.71 breeding value accuracy and 0.79 absolute EBV correlation with severity scores, suggesting NBHIs can replace visual scoring with minimal precision loss. Discussion By utilising UAV-based hyperspectral and thermal imaging to capture single-tree phenotypes related to disease in a forestry trial and pairing the data to genomic evaluation, this study establishes that remote sensing data offers an efficient, scalable alternative to traditional phenotyping. Our approach constitutes a major step towards characterising specific physiological responses, facilitating the discovery of the genetic architecture of physiological traits, and significantly enhancing genetic improvement.
Why it matches plant phenotyping methodsUAVハイパースペクトル・熱画像を用いて単木の病害関連形質を取得し、従来の視覚評価との比較・代替可能性まで検証しており、フェノタイピング手法が中心である。
abstractThis study investigates in-situ high-throughput hyperspectral and thermal imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus radiata D.Don.
Crop temperature regulation is a fundamental aspect of plant physiology, especially under fluctuating environmental conditions. Temperature-based indices such as Crop Canopy Air Temperature Difference (CCATD) and Canopy Temperature Depression (CTD) are vital indicators of plant water status, transpiration efficiency, and drought response. CCATD, defined as the difference between canopy temperature (Tc) and air temperature (Ta), provides insights into water stress, with higher values indicating limited transpiration and increased canopy heat accumulation. In contrast, CTD—calculated as the difference between Ta and Tc—reflects the plant’s evaporative cooling capacity, where higher values denote active transpiration and efficient water use. The inverse relationship between CCATD and CTD enhances their utility in crop stress monitoring, precision irrigation, and the selection of stress-resilient genotypes in breeding programs. Advanced technologies such as infrared thermometry, UAV-mounted thermal imaging, and satellite-based remote sensing support accurate assessment of these indices at multiple scales. Environmental variables—including solar radiation, vapor pressure deficit (VPD), wind speed, and soil moisture—significantly influence CCATD and CTD, highlighting the need for their integration with multispectral and physiological data for more effective stress detection. This review emphasizes the critical role of CCATD and CTD in optimizing water management, guiding climate-resilient crop selection, and advancing precision agriculture. Future research should focus on integrating these indices with AI-driven analytics and high-throughput phenotyping to enhance their predictive value and support sustainable crop production under increasing climate variability.
Why it matches plant phenotyping methods作物の水分状態・ストレスを熱画像や赤外線計測で評価する指標を中心に扱うレビューであり、植物表現型の取得・評価手法が主題である。
abstractTemperature-based indices such as Crop Canopy Air Temperature Difference (CCATD) and Canopy Temperature Depression (CTD) are vital indicators of plant water status, transpiration efficiency, and drought response.
Herbicide screening requires a substantial amount of time, effort, and cost, making a new herbicide discovery expensive and time-consuming. Various diagnostic methods have been developed, but most of them are destructive and require significant time and effort to identify herbicide activity. Therefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs). RGB, chlorophyll fluorescence (CF), and infrared (IR) thermal images were acquired after treating herbicides with different MOAs to a model plant, oilseed rape ( Brassica napus ), and analyzed using MATLAB 2021b to quantify NDI, ExG, F d /F m , and plant leaf temperature. NDI, ExG and F d /F m decreased, while plant leaf temperature increased after herbicide treatment. Distinctive spectral responses were found depending on the herbicide MOAs. PSII and PPO inhibitors showed rapid responses in IR thermal and CF images within 1 day after herbicide treatment. HPPD inhibitor showed a continuous decrease in F d /F m , while EPSPS inhibitor showed gradual changes in all spectral indices. Machine learning by Subspace Discriminant algorithm of spectral indices acquired at 6 h enabled the diagnosis of herbicide MOAs with 89.6 % accuracy, which gradually increased by adding new spectral indices acquired later time points until 3 DAT, when validation accuracy scored 100 %. The indices acquired at 6 h, and F d /F m and leaf temperature data were shown to contribute to higher accuracies of identifying herbicide MOAs. Overall test accuracy scored 87.5 %, verifying the possibility of diagnosing herbicide MOAs based on spectral indices. Therefore, we could conclude that herbicide activity and MOAs can be diagnosed by analyzing spectral images combined with machine learning, suggesting the possibility of high-throughput screening of herbicide MOAs using plant image analysis.
Why it matches plant phenotyping methods植物への除草剤処理を目的とするが、スペクトル画像から葉温度や蛍光などの植物状態を抽出し、機械学習で作用機序を診断する画像解析手法が中心であるため、植物フェノタイピング手法として含める。
abstractTherefore, this study was conducted to apply spectral image analysis for early and rapid diagnosis of herbicidal activity and modes of action (MOAs).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 6 Sept 2026
Abstract Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and phenotyped using RGB, thermal infrared, chlorophyll fluorescence and hyperspectral imaging sensors. Temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (R 2 ≥ 0.97) even when exclusively using predictors only from the early phase after drought induction. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage were identified as key classification features. Temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Prediction accuracy for these traits remained high (R 2 ≥ 0.84) when using only predictors from the first half of the experiment. Models trained on pooled drought and control data outperformed single-treatment models and retained high accuracy when applied across treatments. These findings support the integration of high-throughput phenotyping and temporal modelling to enable timely and more cost-effective selection of drought-resilient genotypes, and illustrate the broader potential of phenomics-driven approaches in accelerating crop improvement under stress-prone conditions.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる高スループット表現型取得と、時系列モデルによる干ばつ状態および収穫形質の予測が研究の中心である。
abstractwe investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants
Rice blast disease poses a significant threat to rice yield. The disease progresses rapidly once symptoms appear, making timely control challenging. Moreover, once lesions form, the damage becomes irreversible. Existing detection methods often suffer from delays and lack effective strategies for identifying the disease at its asymptomatic stage, hindering early diagnosis. In this study, we collected thermal and optical data from rice canopies at different infection stages and integrated physiological and biochemical analyses to investigate the infection mechanism during the early, asymptomatic phase. Additionally, we employed the SURF feature extraction algorithm to fuse thermal and optical images, developing a preliminary method for identifying asymptomatic rice regions based on thermal signatures. This approach effectively captured the spectral responses of asymptomatic rice and mitigated the limitations of single-sensor detection in early disease identification. By analyzing spectral and temperature characteristics, we applied feature dimensionality reduction techniques to construct early detection models at both the canopy and leaf levels. The models achieved overall classification accuracies (OA) of 92 % and 97 %, respectively, enabling detection 72 h prior to lesion formation. Finally, we designed fixed-point IP and multi-level register-cascade pipeline architecture, implementing low-power FPGA-based edge computing system. The leaf-level detection model deployed on the FPGA achieved an accuracy of 92 %, with a power consumption of 0.076 W and an inference speed of 0.11 ms. This study proposes an effective real-time detection method for identifying early asymptomatic rice blast, thereby facilitating timely disease monitoring and prevention.
Why it matches plant phenotyping methodsイネの無症状病害を熱画像・光学画像から抽出し、検出モデルとFPGA実装まで開発・評価しており、植物状態の取得手法が中心である。
abstractwe employed the SURF feature extraction algorithm to fuse thermal and optical images, developing a preliminary method for identifying asymptomatic rice regions based on thermal signatures.
High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology. Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms, using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change. Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency. Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.
Why it matches plant phenotyping methods植物の成長・生理形質を取得するマルチスペクトル自動イメージング基盤を開発し、蛍光パラメータを検証してストレス事例で実証しているため、フェノタイピング手法が中心である。
abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency.
Growth chamberLeafPhysiological trait estimationPlant / canopy temperatureWater status / transpiration
Plant factories require effective ventilation to promote proper plant growth. Computational Fluid Dynamics (CFD) is commonly used to evaluate ventilation strategies in these environments. Traditionally, porous models have been employed to study ventilation in plant factories. However, this study proposes an alternative approach using actual plant geometry, consisting of leaves and stems, which reduces the need for fitting parameters typically used in porous models. The study focuses on basil, with plant geometry based on experimental data to ensure accurate representation. This new plant model accounts for the heat and mass balance of each leaf, assigning individual temperature and humidity values. Radiative heat exchange was also included in the plant model by using the solar ray tracing algorithm to solve for shortwave radiation and the surface to surface radiation model for longwave thermal radiation. Validation was conducted in a small plant factory-like environment (1500 mm × 420 mm x 800 mm) under night-like conditions without shortwave radiation and day-like conditions, with shortwave radiation. Key variables such as transpiration rate and leaf temperature were measured and simulated. The coefficient of variation between measured and simulated transpiration rates ranged from 10 % to 15 % for night-time and 15 % for day-time. Root mean square deviations for leaf temperature were 0.4–0.6 °C at night and 0.5–1.6 °C during the day. A different test case, with air supplied from the bottom instead of the side, demonstrated the new model's capabilities. Overall, the new plant model visualises airflow around and through the canopy, and shows promise for improving ventilation strategies in vertical farming systems.
Why it matches plant phenotyping methods個葉形状を用いたCFD植物モデルを開発し、葉温度と蒸散速度を実測値と比較検証しているため、植物状態の取得・推定手法が研究の中心である。
abstractthis study proposes an alternative approach using actual plant geometry, consisting of leaves and stems
ArabidopsisThermalLeafGrowth / time-series analysisStress response / tolerancePlant / canopy temperature
Repairing damaged tissues is essential for the survival of all organisms. In plants, tissue injury rapidly triggers defense and repair programs. However, the molecular mechanisms linking early injury cue to the later stages of wound repair remain unclear. Here, we show that wounding of Arabidopsis leaves induces localized low temperature at the injury site, likely caused by evaporative cooling, which is accompanied by an activation of cold-responsive genes. Using thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive and real-time manner. Mechanistically, we show that C-repeat Binding Factor (CBF) transcription factors are required for the activation of injury-associated cold response and downstream salicylic acid (SA) signaling. The CBF–SA module promotes lignin deposition and wound repair. Together, these findings reveal a link between a wound-induced biophysical cue and the tissue repair program.
Why it matches plant phenotyping methods熱画像とコンピュータビジョン・深層学習を組み合わせ、植物の創傷修復動態を定量・非侵襲・リアルタイムに測定するワークフローを開発しており、フェノタイピング手法が中心的である。
abstractUsing thermal imaging combined with computer vision and deep learning, we developed a workflow to monitor the dynamics of wound healing in a quantitative, non-invasive and real-time manner.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Technological advances have made drones (UAVs) increasingly important tools for the collection of trait data in plant science. Many costs for the analysis of plant populations have dropped precipitously in recent decades, particularly for genetic sequencing. Similarly, hardware advances have made it increasingly simple and practical to capture drone imagery of plant populations.However, converting this imagery into high-precision and high-throughput tabular data has become a major bottleneck in plant science. Here, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types. Methods can be flexibly combined to extract data related to canopy temperature, area, height, volume, vegetation indices, and summary statistics derived from complex segmentations and classifications. We then describe educational and training resources for these methods, including a web page ( PlantScienceDroneMethods.github.io ) and an educational YouTube channel ( https://www.youtube.com/@travisparkerplantscience ) with step-by-step protocols, example data, and example scripts for the whole drone data processing pipeline. These resources facilitate the extraction of high-throughput and high-precision phenomic data, removing barriers to the phenomic analysis of large plant populations.
Why it matches plant phenotyping methodsドローン画像から植物形質を抽出する高スループット表現型解析手法と、処理パイプライン・教育資源を中心に提示しており、方法開発およびプラットフォームに該当する。
abstractHere, we describe high-throughput phenotyping methods for the analysis of numerous plant traits based on imagery from diverse sensor types.
WheatLeafPhysiological trait estimationPlant / canopy temperature
The physiological state of functional leaves in crops plays a vital role in yield formation. Over two consecutive winter wheat growing seasons, we continuously monitored the flag leaf temperature (Tf) during the reproductive growth stage and collected key meteorological indicators, including air temperature (Ta), relative humidity (Ha), soil temperature (Ts), and photosynthetically active radiation (PAR). Pearson correlation analysis, stepwise regression analysis, and path analysis revealed that Ta, PAR, Ts, and Ha are the main environmental factors influencing Tf. These variables were identified as key for further analysis. Notably, Tf exhibited a positive time lag correlation with PAR, while Ta and Ts lag showed positive lag correlation with Tf, and Ha demonstrated a negative lag correlation with Tf. Among the analyzed meteorological factors, soil temperature displayed the smallest lag effect relative to Tf, consistently trailing behind it. PAR showed a pronounced lag effect, shifting an hour earlier than Tf, while Ta exhibited a significant hour-long delay after Tf. Ha primarily functioned as a cooling influence, lagging approximately one hour behind Tf. Moreover, the intensity of the time delay effect will vary depending on the developmental stage. Integrating these time-lag relationships significantly enhanced the accuracy of Tf simulations. Support Vector Regression (SVR) demonstrated robust predictive performance (R² = 0.937, RMSE = 2.048 °C), indicating its potential for accurate prediction of Tf in wheat production. This study highlights the time-delay effects between Tf and meteorological factors during the reproductive growth stage of wheat, offering a predictive model that provides a foundation for monitoring crop physiological conditions in real time.
Why it matches plant phenotyping methods小麦の旗葉温度という生理形質を連続計測し、気象要因との時間遅れを組み込んだSVR予測モデルを開発・評価しており、形質取得と推定手法が中心である。
abstractwe continuously monitored the flag leaf temperature (Tf) during the reproductive growth stage and collected key meteorological indicators
Abstract: Plant diseases cause significant agricultural losses, affecting both crop yield and quality. Early detection is crucial for effective disease management. This study explores thermal imaging as a non-invasive method for identifying plant stress in Patharchatta (Kalanchoe pinnata). Two cases were analyzed: wilting due to dehydration and black spot disease from overwatering.Thermal thresholds of 16°C (early stress) and 18°C (critical damage) were experimentally identified, particularly in fungal-infected Patharchatta plants.A thermal image-based classification model was developed to support detection, achieving over 91% accuracy.The findings demonstrate that thermal imaging is a promising, real-time toolfor early disease detection, enabling proactive plant health management.
Why it matches plant phenotyping methods植物のストレス・病害状態を熱画像から推定する分類モデルと閾値を開発・評価しており、表現型取得手法が研究の中心である。
abstractThis study explores thermal imaging as a non-invasive method for identifying plant stress in Patharchatta (Kalanchoe pinnata).
Alfalfa is a deep-rooted perennial forage crop with diverse drought-tolerant traits. This study evaluated 250 alfalfa half-sib populations over three growing seasons (2021–2023) under irrigated and rainfed conditions in the Mediterranean drought-prone region of Central Chile (Cauquenes), aiming to identify high-yielding, drought-tolerant populations using remote sensing. Specifically, we assessed RGB-derived indices and canopy temperature difference (CTD; Tc − Ta) as proxies for forage yield (FY). The results showed considerable variation in FY across populations. Under rainfed conditions, winter FY ranged from 1.4 to 6.1 Mg ha−1 and total FY from 3.7 to 14.7 Mg ha−1. Under irrigation, winter FY reached up to 8.2 Mg ha−1 and total FY up to 25.1 Mg ha−1. The AlfaL4-5 (SARDI7), AlfaL57-7 (WL903), and AlfaL62-9 (Baldrich350) populations consistently produced the highest yields across regimes. RGB indices such as hue, saturation, b*, v*, GA, and GGA positively correlated with FY, while intensity, lightness, a*, and u* correlated negatively. CTD showed a significant negative correlation with FY across all seasons and water regimes. These findings highlight the potential of RGB imaging and CTD as effective, high-throughput field phenotyping tools for selecting drought-resilient alfalfa genotypes in Mediterranean environments.
Why it matches plant phenotyping methodsRGB画像指標と冠層温度差を用いた高スループット表現型解析を、アルファルファ集団の収量・干ばつ耐性選抜に実質的に適用しており、表現型取得手法が中心的である。
titleSelecting High Forage-Yielding Alfalfa Populations in a Mediterranean Drought-Prone Environment Using High-Throughput Phenotyping
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 Mosaic tool software and Cereal-Scanner plugin,
developed by Shawn Kefauver from the University of Barcelona, were utilized for further
analysis (available at https://gitlab.com/sckefauver/cerealscanner (accessed on 6 March
2025)).Open asset ↗gitlab.com/sckefauver/cerealscannerpdf-page:7 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
ABSTRACT Infrared thermography (IRT) for real‐time stress detection in plant factories (PFs) remains largely unexplored. Hence, this study investigates the feasibility of implementing IRT in PFs, using machine learning (ML) to address the challenges in information processing. Herein, purple basil plantlets were subjected to root dehydration within a pilot‐scale PF, and canopy temperature was monitored at regular intervals using a thermal camera. Subsequently, eight ML models using the ‘support vector machines’ algorithm were tested for stress detection. Our findings revealed that differences in canopy temperature due to microenvironmental variations led to inaccurate representation of stress. Nonetheless, binary classification models trained using plants at medial and high stress overcame this issue by identifying stressed samples with 81%–94% accuracy. However, although models trained with medially stressed samples performed well for all stress levels, models trained using highly stressed samples failed to identify medial stress reliably. Additionally, ternary and quaternary classification models were able to identify unstressed samples but could not distinguish between different levels of stress. Hence, binary classification models trained using medially stressed samples overcame spatiotemporal variations in canopy thermal profile most effectively and provided probabilistic estimates of plant stress within the PF most consistently.
Why it matches plant phenotyping methods植物工場での赤外線サーモグラフィーと機械学習による植物ストレス推定が研究の中心であり、手法の実装・評価と精度検証を行っている。
abstracteight ML models using the ‘support vector machines’ algorithm were tested for stress detection
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology . Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms , using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change . Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency . Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.
Why it matches plant phenotyping methods植物の成長・葉温・光合成効率・クロロフィル含量を取得するマルチスペクトル自動計測プラットフォームの開発と検証が中心であり、植物フェノタイピング手法として適格。
abstractHere, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging , and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency .
Field / plotThermalWhole plant / canopy / plot / fieldPhysiological trait estimationPlant / canopy temperatureWater status / transpiration
Background Accurately evaluating the water status of walnuts in different growth stages is fundamental to implementing deficit irrigation strategies and improving the yield of walnuts. The crop water stress index (CWSI) based on the canopy temperature is one of the most commonly used tools for current research on plant water monitoring. However, the suitability and effectiveness of using the CWSI as an indicator of the walnut water status under field conditions are still unclear. This paper focuses on walnut orchards in Northwest China using synchronous monitoring of the canopy temperature, meteorological parameters, and water physiological parameters of walnut trees under both full irrigation and deficit irrigation treatments. The aim is to test the effectiveness of the simplified crop water stress index (CWSI s ) and the theoretical crop water stress index (CWSI t ) in tracking the diurnal and daily variations of the water conditions in walnut orchards. Results The CWSI s can reflect the diurnal and daily changes in the water status of walnut orchards. It was found that the CWSI s at 12:00 local time had the best performance in tracking the daily changes in the water status. Compared to the daily averaged CWSI calculated using the measured transpiration (CWSI Tr_day ), the correlation coefficient, index of agreement, and root mean squared error between the CWSI s and CWSI Tr_day were 0.82, 0.94, and 0.11, respectively. However, due to the calculation errors of the aerodynamic resistance in walnut trees, the CWSI t was unable to track the diurnal variations in the water status in walnut orchards and the degree of water stress was underestimated. In addition, the variations in minimum canopy resistance in the various growth stages of walnut orchards may also affect the accuracy of the CWSI t in terms of indicating the seasonal changes in the water status. Conclusions The CWSI s provides a non-destructive, quickly and effective method for monitoring the water status of walnuts. However, the results of this study suggest that the effects of aerodynamic resistance parameterization and variations in minimum canopy resistance in the various growth stages of walnut orchards in the CWSI t calculation should be noted.
Why it matches plant phenotyping methodsクルミ樹の水分状態という植物生理状態を、樹冠温度に基づくCWSIで非破壊推定し、簡易法・理論法の有効性と誤差を比較検証しているため、フェノタイピング手法が中心である。
abstractThe aim is to test the effectiveness of the simplified crop water stress index (CWSI s ) and the theoretical crop water stress index (CWSI t ) in tracking the diurnal and daily variations of the water conditions in walnut orchards.
Climate change and the increasing resistance of pathogens are driving the need for innovative methods of plant disease diagnostics, particularly for high-risk pathogens such as Fusarium graminearum, which causes significant wheat yield losses. Traditional visual inspection methods suffer from low throughput and subjectivity, limiting their effectiveness in large-scale monitoring. This study aimed to explore the principles of high-throughput phenotyping and to evaluate the effectiveness of a sensor platform for the non-invasive investigation of Fusarium ear blight under field conditions in Ukraine (Kyiv region). A combination of multispectral imaging, thermal imaging, and machine learning algorithms was applied in a 1-hectare experimental field with 20% of the plots artificially infected. The results demonstrated that the proposed system achieved a 92% accuracy rate in early pathogen detection, representing a 37% improvement over visual assessment methods. Spectral indices showed a strong correlation with pathogen concentration: a decrease in the normalised difference vegetation index from 0.72 to 0.35 corresponded with an 80% increase in fungal biomass. Thermal imaging revealed a rise in leaf temperature of 2.5°C as early as 5-7 days after infection. The integration of all methods enabled an accuracy of 96% in processing one hectare within 2.5 hours, which is three times faster than traditional approaches. Polymerase chain reaction analysis confirmed the specificity of the techniques: 95% of infected samples contained Fusarium deoxyribonucleic acid, while sequencing revealed a 100% match for β-tubulin. Automated data processing required 2.5 hours per hectare, compared with 8 hours per hectare for visual inspection, and scaling up to 10 hectares reduced time expenditure by a factor of 12. The study confirmed the effectiveness of high-throughput phenotyping for precision plant protection and highlighted the need for further refinement of the methods in line with local climatic conditions. The practical significance of this research lies in the potential to reduce fungicide use through targeted treatment of infected areas, minimise crop losses in regions with high infection pressure, and establish a foundation for automated monitoring systems compatible with precision agriculture technologies
Why it matches plant phenotyping methods植物病害の早期検出を目的に、マルチスペクトル・熱画像と機械学習を統合した非侵襲的ハイスループット表現型解析プラットフォームを開発・評価しており、植物の病害状態を直接推定する方法が中心である。
abstractThis study aimed to explore the principles of high-throughput phenotyping and to evaluate the effectiveness of a sensor platform for the non-invasive investigation of Fusarium ear blight under field conditions in Ukraine (Kyiv region).
Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R 2 = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.
Why it matches plant phenotyping methodsドローン熱画像から作物の樹冠温度と水ストレス指数を抽出する手法を開発し、センサー測定との相関で検証しており、植物状態の取得法が中心である。
abstractA novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI.
The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI
Why it matches plant phenotyping methodsUAVデータとランダムフォレストにより、トウモロコシ葉温度由来の水ストレス指標（CWSI）を推定する手法が研究の中心であり、植物の生理状態を定量化している。
abstractThis study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands.
Leaf and canopy temperature have long been recognized as important indicators of plant water status because leaves cool when water is transpired and warm up when leaf stomata close and transpiration is reduced. Unmanned aerial vehicles (UAVs) open up the possibility to capture high resolution thermal images of forest canopies at the leaf scale. However, a careful calibration procedure is required to convert the thermal images to absolute temperatures, in addition, at high spatial resolution, the complexity of forest canopies leads to challenges in stitching overlapping thermal images into an orthomosaic of the forest site. In this study, we present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan. Six UAV flight campaigns were conducted over a tropical dry forest in Costa Rica. For each flight five different calibration methods were tested. The most accurate calibration was used to analyze the tree canopy temperature distributions of five tree species. From the distribution we correlated its mean, variance, 5th and 95th percentile against individual tree transpiration estimates derived from sapflow measurements. Our results show that the commonly applied calibration provided by the cameras manufacturer (factory calibration) and empirical line calibration were less accurate than the novel repeated empirical line calibration and the factory calibration including drift correction (MAE 3.5°C vs. MAE 1.5°C). We show that the orthomosaic is computable by directly estimating the thermal image orientation from the visible images during the structure from motion step. We found the 5th percentile of the canopy temperature distribution, corresponding to the shaded leaves within the canopy, to be a better predictor of tree transpiration than the mean canopy temperature (R 2 0.85 vs. R 2 0.60). Although these shaded leaves are not representative of the whole canopy, they may be the main transpiration site in the heat of the day. Spatially high-resolution, validated temperature data of forest canopies at the leaf scale have many applications for ecohydrological questions, e.g., the estimation of transpiration, for comparing plant traits and modeling of carbon and water fluxes by considering the entire canopy temperature distribution in mixed-species forests.
Why it matches plant phenotyping methodsUAV熱画像の飛行計画、温度校正、オルソモザイク生成を開発・比較検証し、樹冠温度という植物生理形質を推定しているため、フェノタイピング手法が中心である。
abstractwe present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan.
Stomatal conductance (g s ) quantifies the rate of exchange of carbon dioxide for photosynthesis and water vapor for transpiration between plant leaves and the atmosphere. g s is usually measured by handheld devices like porometers , and readings are manually taken in the field, which is time-consuming and labor-intensive. In this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling. The experiment was conducted in a research field equipped with an HTP platform in 2020 and 2021 involving maize, sorghum, soybean, sunflower , and winter wheat . Weather variables including dew point temperature, wind speed , air temperature, solar radiation, and relative humidity were collected by an onsite weather station . Plot-level canopy temperature, soil temperature , and seven vegetation indices were acquired using a thermal infrared camera, a multispectral camera, and a visible near-infrared spectrometer integrated on the HTP platform. Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for g s , and model performance was evaluated by Coefficient of Determination (R 2 ) and Root Mean Squared Error (RMSE). The result showed that RFR and SVR outperformed PLSR in g s modeling. The RFR model achieved R 2 of 0.63 and RMSE of 0.16 mol m −2 ·s −1 with the combination of phenotyping data and weather data. It outperformed the model using only the weather data (R 2 =0.35 and RMSE=0.21 mol m −2 ·s −1 ), or the model using only the phenotyping data (R 2 =0.46 and RMSE=0.19 mol m −2 ·s −1 ). This result suggested that high-throughput plant phenotyping data effectively complement weather data in estimating g s rapidly and non-destructively through ML. With the wide adoption of HTP technologies in aerial and ground-based platforms, this research provides a practical framework to estimate g s at large scale for crop breeding and irrigation management .
Why it matches plant phenotyping methodsHTPセンサーデータと機械学習を用いて、植物の生理形質である気孔コンダクタンスを大規模・非破壊推定する方法が研究の中心であり、モデル性能も評価している。
abstractIn this study, we investigated the use of high-throughput phenotyping (HTP) data combined with weather data to estimate g s through machine-learning (ML) modeling.
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Pale terricolous lichens are a vital component of Arctic ecosystems, significantly contributing to carbon balance, energy regulation, and serving as a primary food source for reindeer. Their characteristically high albedo also impacts land surface temperature (LST) dynamics across various spatial scales. However, remote sensing of lichens is challenging due to their complex spectral signatures and large spatial variations in coverage and biomass even within local landscape scales. This study evaluates the influence of pale lichens on LST at local and landscape scales by integrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data. An Extreme Gradient Boosting algorithm was employed to map pale lichen biomass, areal extent, and the occurrence of major plant functional types in the sub-arctic heath tundra landscape in the Jávrrešduottar and Sieiddečearru areas on the Finland-Norway border. Generalized Additive Models (GAMs) were used to elucidate the factors affecting LST. The UAV model accurately predicted pale lichen biomass (R 2 0.63) and vascular vegetation cover (R 2 0.70). GAMs revealed that pale lichens significantly influence thermal regimes, with increased biomass leading to decreased LST, an effect more pronounced at the landscape scale (deviance explained 47.26 % and 65.8 % for local and landscape models, respectively). Pale lichen biomass was identified as the second most important variable affecting LST at both scales, with elevation being the most important variable. This research demonstrates the capability of UAV-derived models to capture the heterogeneous and fine-scale structure of tundra ecosystems. Furthermore, it underscores the effectiveness of combining high spatial resolution UAV and high temporal resolution satellite platforms. Finally, this study highlights the pivotal role of pale lichens in Arctic thermal dynamics and showcases how advanced remote sensing techniques can be used for ecological monitoring and management.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外画像と衛星データを統合し、機械学習で地衣類 biomass と植生被覆を推定し、精度を検証しているため、植物状態の取得手法が中心です。
abstractintegrating RGB, multispectral, and thermal infrared imagery from an Unmanned Aerial Vehicle (UAV) with multi-temporal Landsat 8 thermal data
Field / plotRootWhole plant / canopy / plot / fieldStress / disease detectionRoot system architectureStress response / tolerancePlant / canopy temperature
Abiotic stresses, such as drought, salinity, and heat, exacerbated by climate change, pose significant challenges to global agriculture. These stresses negatively impact crop physiology, leading to yield losses and complicating efforts to breed resilient varieties. While advancements in molecular biology and genomics have identified stress-resistance genes, their effective utilization in breeding programs depends on precise phenotypic evaluation under diverse stress conditions. High-throughput phenotyping (HTP) technologies have emerged as indispensable tools, enabling non-destructive, rapid assessment of critical traits like root architecture, chlorophyll content, and canopy temperature in controlled and field environments. Unlike existing reviews, this manuscript critically addresses technological barriers such as cost scalability, field adaptability, and the integration of artificial intelligence for real-time data analysis. Additionally, it provides a fresh perspective on multi-omics integration in phenomics to bridge the genotype–phenotype gap, ensuring a more holistic approach to precision agriculture. This review bridges gaps in crop improvement by identifying practical solutions to enhance the adoption of HTP in breeding programs. It ensures food security amidst the escalating impacts of climate change.
Why it matches plant phenotyping methods植物のハイスループット表現型解析技術を主題とし、技術的障壁やAI統合、育種への適用を論じるレビューであるため、方法レビューとして収載する。
titleAdvanced High-Throughput Phenotyping Techniques for Managing Abiotic Stress in Agricultural Crops—A Comprehensive Review
Abstract Context Climate change is causing landscape shifts and locally-adapted plants are becoming increasingly maladapted. As a foundation species, Fremont cottonwood facilitates adaptation to changing climate for the whole community. Populations within this species, however, have varying adaptive responses and facilitative capacity due to genetic variation. It is important to identify these differences to inform landscape restoration and management. Objectives UAV hyperspectral, thermal, and lidar images might reveal genetic trait differences within a single tree species. This study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content as indicators of population-level productivity, fitness, adaptability, and biodiversity they can support, and (2) UAV hyperspectral-thermal-lidar fusion in detecting and classifying 16 populations sourced from different environments across Arizona, USA. Methods UAV hyperspectral, thermal, and lidar images were acquired from a common garden with 16 different Fremont cottonwood populations growing together. The UAV hyperspectral image was used to calculate spectral indices for canopy leaf area (LAI), canopy water content, nitrogen, carbon, and carbon-to-nitrogen ratio (C:N). The hyperspectral indices (EVI, LAI, PRI, MSI, NDWI, NDNI, NDLI, and C:N) were also examined with the UAV thermal image-derived canopy temperature data for potential correlations. Finally, all hyperspectral bands (n = 487 bands), thermal image-derived canopy temperature, and lidar-derived maximum canopy height estimates were stacked into a single image and then classified to detect 16 different populations of Fremont cottonwood using a random forest classification. Results The UAV hyperspectral indices and canopy temperature were significantly different among populations suggesting that the productivity, fitness, and adaptability of varying populations are significantly different. Many of the UAV hyperspectral indices were strongly correlated with canopy temperature. Populations with greater canopy cover, lower canopy temperature, and greater canopy height were well detected in the UAV hyperspectral-thermal-lidar fusion-based classification (producer’s accuracies of > 75%), whereas populations at low abundance were poorly classified (producer’s accuracies of Conclusions This study demonstrates the first application of UAV hyperspectral-thermal-lidar data fusion in phenotyping. The machine learning-based classification detects various populations within a single tree species. Future studies can use similar UAV data sources, derived variables, and data fusion to detect populations that have better fitness and adaptability to changing environments. Such populations can be strategically managed to sustain healthy landscapes that support diverse communities and species.
Why it matches plant phenotyping methodsUAVのハイパースペクトル・熱・LiDAR融合により、樹冠形態・生理形質を推定し、集団差を分類する手法を中心的に開発・実証しているため。
abstractThis study tests and demonstrates: (1) UAV hyperspectral images in detecting differences among populations in canopy leaf area, water content, carbon, and nitrogen content
O_LIUnderstanding how vegetation responds to drought is fundamental for understanding the broader implications of climate change on foundation tree species that support high biodiversity. Leveraging remote sensing technology provides a unique vantage point to explore these responses across and within species. C_LIO_LIWe investigated interspecific drought responses of two Populus species (P. fremontii, P. angustifolia) and their naturally occurring hybrids using leaf-level visible through shortwave infrared (VSWIR; 400-2500 nm) reflectance. As F1 hybrids backcross with either species, resulting in a range of backcross genotypes, we heretofore refer to the two species and their hybrids collectively as "cross types." We additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery. We employed several analyses to assess genotype-by-environment (GxE) interactions concerning drought, including principal component analysis, support vector machine, and spectral similarity index. C_LIO_LIFive key findings emerged: (1) Spectra of all three cross types shifted significantly in response to drought. The magnitude of these reaction norms can be ranked from hybrids>P. fremontii>P. angustifolia, suggesting differential variation in response to drought; (2) Spectral space among cross types constricted under drought, indicating spectral--and phenotypic--convergence; (3) Experimentally, populations of P. fremontii from cool regions had different responses to drought than populations from warm regions, with source population mean annual temperature driving the magnitude and direction of change in VSWIR reflectance. (4) UAV thermal imagery revealed that watered, warm-adapted populations maintained lower leaf temperatures and retained more leaves than cool-adapted populations, but differences in leaf retention decreased when droughted. (5) These findings are consistent with patterns of local adaptation to drought and temperature stress, demonstrating the ability of leaf spectra to detect ecological and evolutionary responses to drought as a function of adaptation to different environments. C_LIO_LISynthesis. Leaf-level spectroscopy and canopy-level UAV thermal data captured inter- and intraspecific responses to water stress in cottonwoods, which are widely distributed in arid environments. This study demonstrates the potential of remote sensing to monitor and predict the impacts of drought on scales varying from leaves to landscapes. C_LI
Why it matches plant phenotyping methods葉面分光とUAV熱画像を用いて、植物の乾燥応答や葉温・葉保持を測定し、リモートセンシングによる表現型評価の有効性を実証しているため。
abstractWe additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery.
Water conditions in soil are measured with soil moisture sensors such as tensiometer and time-domain reflectometry. However, installed soil moisture sensors may not fully represent the entire cultivation area due to factors such as topography, meteorological conditions, and irrigation systems.The purpose in this study is to identify spatial variations of crop growth and moisture conditions using drone images and weather data. The drone, equipped with multi-spectral, hyper-spectral, and infrared cameras, captured images, and precipitation information up to 3 days later was automatically collected from numerical weather prediction model. Thermal images of crops and soil responded immediately depending on the presence or absence of irrigation. In irrigated crops, leaf temperature decreased due to transpiration. The hyper-spectral images, including short-wave infrared wavelengths, proved sensitive to soil water conditions. However, reflectance-based water indices showed no immediate differences for crops unless soil moisture fell below the wilting point. There was a difference in crop growth depending on the level of irrigation, which was clearly revealed in the vegetation index. Crop growth was poor in areas where irrigation was low. When soil moisture sensor values decrease and no rainfall is expected in the near future, drone images can be utilized to identify specific areas experiencing crop moisture stress. This suggests the potential for drones to support irrigation decision-making.Acknowledgments: This research was funded by the Rural Development Administration, grant number RS-2022-RD009999.
Why it matches plant phenotyping methodsドローンのマルチスペクトル・ハイパースペクトル・熱画像を用いて、作物生育、水分状態、蒸散に伴う葉温、乾燥ストレスを空間的に推定する方法が研究の中心であり、単なる灌漑試験の routine 測定ではない。
abstractThe purpose in this study is to identify spatial variations of crop growth and moisture conditions using drone images and weather data.
The compact, high-throughput phenotyping platform, characterized by its portability and small size, is well-suited for crop phenotyping across diverse environments. However, integrating multi-source sensors to achieve synchronized data acquisition and analysis poses significant challenges due to constraints in load capacity and available space. To address these issues, we developed a robotic platform specifically designed for phenotyping greenhouse strawberries. This system integrates an RGB-D camera, a multispectral camera, a thermal camera, and a LiDAR sensor, enabling the unified analysis of data from these sources. The platform accurately extracted key phenotypic parameters, including canopy width (R² = 0.9864, RMSE = 0.0185 m) and average temperature (R2 = 0.8056, RMSE = 0.1732 °C), with errors maintained below 5%. Furthermore, it effectively distinguished between different strawberry varieties, achieving an Adjusted Rand Index of 0.94, underscoring the value of detailed phenotyping in variety differentiation. Compared to conventional UGV-LiDAR systems, the proposed platform is more cost-effective, efficient, and scalable, with enhanced data consistency, making it a promising solution for agricultural applications.
Why it matches plant phenotyping methods複数センサーを統合したロボット植物フェノタイピング基盤を開発し、キャノピー幅と平均温度の抽出精度を検証しているため、フェノタイピング手法が中心である。
abstractwe developed a robotic platform specifically designed for phenotyping greenhouse strawberries.
ABSTRACT Implementation of context‐specific solutions, including cultivation of varieties adapted to current and future climatic conditions, have been found to be effective in establishing resilient, climate‐smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesizing that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world's largest banana gene bank in terms of base temperature ( T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. One hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype‐specific T base were modelled. Growth responses of two genotypes were validated under greenhouse conditions, and gas exchange capacity measurements were made. The model confirmed genotype‐specific T base , with 30% of the accessions showing a T base below the reference of 14°C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low temperature sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, with this case study on banana, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate‐smart varieties.
Why it matches plant phenotyping methodsバナナ遺伝資源の温度応答を高スループットに定量化し、遺伝子型別の基底温度をモデル化・温室で検証しており、表現型取得法と検証が研究の中心です。
abstractOne hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation.
LeafPhysiological trait estimationPlant / canopy temperature
The excellent stretchability and biocompatibility of flexible sensors have inspired an emerging field of plant wearables that enable intimate contact with plants to monitor growth status and local microclimate in real-time continuously. Flexible plant wearables provide a promising platform for the development of plant phenotypes. In this work, we have fabricated several plant wearables that can harmlessly and continuously monitor leaf temperature/humidity, which are critical parameters for analyzing the physiological status and microclimate of plants. Credited to the unique semi-embedded design and the selected biocompatible materials, we achieve ultrathin sensors with a conductive layer thickness of 7 μm, exhibiting excellent electrical conductivity with 9.43×10⁶ S/m, bendability and fatigue resistance with 0.99% change rate after 10,000 cyclic bending tests, enabling on-site and non-destructive testing. These plant wearables can be used as a non-invasive, high-throughput, low-cost toolbox with excellent potential for phenotypic analysis.
Why it matches plant phenotyping methods植物の葉温・湿度を連続測定するウェアラブルセンサーを開発し、植物表現型解析への利用を明示した研究であり、センサー手法が中心的です。
abstractIn this work, we have fabricated several plant wearables that can harmlessly and continuously monitor leaf temperature/humidity
Abstract Water scarcity poses a significant challenge to wheat cultivation, especially in Egypt, where wheat is a staple crop. The study aimed to enhance water stress tolerance in wheat by using advanced phenotyping methods to improve genotype selection in arid regions. The objective was to evaluate various wheat genotypes for their drought tolerance and to explore the effectiveness of high‐throughput phenotyping methods such as canopy temperature (CT), normalized difference vegetation index (NDVI), and water‐soluble carbohydrates (WSC) as non‐destructive tools for selection. Two field trials were conducted using 15 wheat genotypes in a randomized complete block design with three replications under well‐watered and water‐stressed conditions. Phenotyping methodologies, including NDVI, CT, and WSC, were used to assess the plants. The results showed strong correlations between these phenotyping methods and wheat yield, with genotype 10 showing the highest yield under stress conditions. The study reported broad‐sense heritability values of 0.89 for CT, 0.78 for NDVI, 0.96 for WSC, and 0.69 for grain yield, indicating the robustness of these traits as indicators for selecting water stress‐tolerant wheat genotypes. In conclusion, advanced phenotyping methods, such as CT, NDVI, and WSC, proved to be quick and reliable indicators for screening wheat genotypes in water‐stressed environments. This approach can significantly contribute to breeding programs to improve wheat yield and water productivity in arid regions. These findings underscore the importance of integrating high‐throughput phenotyping in crop improvement strategies for drought‐prone areas. Key Points Water‐soluble carbohydrates, canopy temperature, and NDVI effectively predict water‐stress‐tolerant genotypes. Genotypes 8 and 10 exhibited the highest grain yield and water productivity under water‐stressed environments. High‐throughput phenotyping enables rapid, non‐destructive evaluation of wheat genotypes for water stress tolerance.
Why it matches plant phenotyping methodsNDVI、冠层温度、可溶性炭水化合物を用いた非破壊・高スループット表現型評価が、乾燥耐性選抜の有効性評価として研究の中心である。
abstractThe objective was to evaluate various wheat genotypes for their drought tolerance and to explore the effectiveness of high‐throughput phenotyping methods such as canopy temperature (CT), normalized difference vegetation index (NDVI), and water‐soluble carbohydrates (WSC) as non‐destructive tools for selection.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models. This study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana), a key crop in Oregon, the leading hazelnut-producing state in the United States. Utilizing low-cost, open-source infrared thermometers and data loggers, we aim to provide hazelnut farmers with a practical tool for improving irrigation efficiency and enhancing yields. The CWSI model was validated against plant water status metrics such as stem water potential and gas exchange measurements. Our results show that when stem water potential is below −6 bar, the CWSI remains under 0.2, indicating low plant stress, with corresponding leaf conductance rates ranging between 0.1 and 0.4 mol m2 s−1. Additionally, un-irrigated hazelnuts were stressed (CWSI > 0.2) from mid-July through the end of the season, while irrigated plants remained unstressed. The findings suggest that farmers can adopt a leaf conductance threshold of 0.2 mol m2 s−1 or a water potential threshold of −6 bar for irrigation management. This research introduces a new CWSI model for hazelnuts and highlights the potential of low-cost technology to improve agricultural monitoring and decision-making.
Why it matches plant phenotyping methods低コスト赤外線サーモメータでヘーゼルナッツの水ストレス指標を開発し、植物水分状態およびガス交換で検証しており、植物生理状態の取得手法が中心である。
abstractThis study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana)
Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI.
Why it matches plant phenotyping methodsキャノピー温度とCWSIを用いた植物水ストレス推定法が中心で、ニューラルネットワークおよび物理モデルの開発・検証と、収量・品質との関連評価を行っている。
abstractthe objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling
Global agricultural productivity is affected by plant stresses every year; as a consequence, monitoring and preventing plant stresses is a significant measure to protect the agro-ecological environment. Similar to the adoption of wearable devices to appraise human physiological information and disease diagnosis, however, in situ nondestructive monitoring of complex and weak physiological information in plants is an enormous challenge for the development of wearable sensors. Herein, to accurately analyze the changes of tomato internal information under multiple abiotic stresses in real-time, we introduce the covalent organic framework (COF) film synthesized by self-assembly layer by layer through the oil/water interface as a sensitive material to develop a multifilm-integrated wearable sensor capable of monitoring leaf surface humidity and leaf temperature. The flexible substrate can stretch with leaf growth to ensure the accuracy of long-term monitoring. Benefiting from the performance characteristics, such as ultrahigh sensitivity ( S ) of 0.8399 nA/%RH and an extremely low-resolution (ΔRH) value of 0.0564%, which could amplify the conducted signal, and the long-term stability of COF MOP-TAPB , the transpiration information on tomatoes under 10 abiotic stresses can be monitored continuously and with high precision over a long period by applying the COF-based sensor on the lower surface of the leaf at the upper end of the stem morphology. Finally, we employ metaheuristic optimization algorithms to predict the time series of the internal physiological change trend of tomatoes in the future so that farmers can take corresponding preventive measures in time to ensure the healthy growth of tomatoes.
Why it matches plant phenotyping methodsトマト葉の湿度・温度と蒸散情報を長期・非破壊に取得するウェアラブルセンサーを開発し、性能を示しているため、植物表現型取得法が研究の中心です。
abstractthe transpiration information on tomatoes under 10 abiotic stresses can be monitored continuously and with high precision over a long period
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Implementation of context-specific solutions, including cultivation of varieties adapted to current and future climatic conditions, were found to be effective in establishing resilient, climate-smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesising that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world’s largest banana gene bank in terms of base temperature (T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. 116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype-specific T base were modelled. Growth response of two genotypes was validated under greenhouse conditions, and gas exchange capacity measurements were made. The model revealed genotype-specific T base , with 30 % of the accessions showing a T base below the reference of 14 °C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low T sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate-smart varieties.
Why it matches plant phenotyping methodsバナナの温度応答と成長を定量化する高スループット表現型計測設備・手法を提示し、温室条件で検証しているため、表現型取得法が中心的です。
abstract116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation.
Thermal imaging has been used in recent years to determine salinity stress in certain crops. However, no information is available on this regarding black cumin (Nigella sativa L.). The main purpose of this study was to investigate the determinability of the salt stress on black cumin by thermal imaging. The leaf temperature values were obtained by acquiring the thermal images for black cumin grown under different irrigation water salinity levels (0.6 as control, 1.5, 2.5, and 5.0 dS/m) prepared for different salt sources (CaCl2, MgCl2, NaCl, Ca(NO3)2, MgSO,4 and Na2SO4). Plant leaf temperatures averaged over all irrigation water salinity levels did not show a significant difference among salt sources. On the other hand, plant leaf temperatures independent of the salt sources significantly increased with increasing salinity levels. The order for salinity levels in terms of their effect on the leaf temperature of black cumin was determined as 5.0 dS/m > 2.5 dS/m > 1.5 dS/m ≥ 0.6 dS/m. The highest leaf temperature was obtained under 5.0 dS/m salinity level with CaCl2, MgCl2, Ca(NO3)2, MgSO4, and Na2SO4 salt sources, but the values for MgSO4 and Na2SO4 salt sources were not significantly different from those under 2.5 dS/m salinity. In addition, a strong-negative relationship between plant water consumption and leaf temperature under CaCl2 and MgSO4 and a moderate-negative relationship under MgCl2 and NaCl salt sources were determined. The results of this study showed that the plant leaf temperature values obtained by thermal imaging reflected salt stress conditions, especially under high salinity levels.
Why it matches plant phenotyping methods熱画像から葉温を取得し、黒クミンの塩ストレス状態を判定できるかを主目的として評価しており、植物表現型取得法の実質的な適用研究である。
abstractThe main purpose of this study was to investigate the determinability of the salt stress on black cumin by thermal imaging.
In semi-arid and arid regions, crops face elevated atmospheric demands and endure prolonged periods of moderate to severe water scarcity. In this context, this study investigated the effectiveness of the photochemical reflectance index (PRI) and a normalized surface temperature index (Tₙₒᵣₘ) for proxy detection of the water stress of winter wheat crops. Furthermore, the potential of PRI for characterizing water, atmospheric or photo-inhibition stress, and wheat transpiration was assessed over experimental drip-irrigated crop fields in the Haouz plain, central Morocco. In practice, PRI observations were compared to agro-environmental variables such as Leaf Area Index (LAI), Available Water Content (AWC) at a root zone depth, net Radiation (Rₙ), Vapor Pressure Deficit (VPD) and the wheat transpiration derived from sap flows, lysimeters and a crop water balance model. Due to the strong relationship between PRI and LAI (R² = 0.91), another index named PRIⱼ was derived to correct for this effect. The PRIⱼ was found to be independent of structural effects related to LAI and significantly correlated with AWC (R² = 0.85). Using the PRIⱼ index, we can reflect the level of water stress experienced by the wheat field throughout the experiment with an R² of 0.69 for a FAO-56 water stress coefficient (Kₛ) of less than 1. Under dry conditions, for an AWC below 30%, the correlation between AWC and Tₙₒᵣₘ gives an R² of 0.29. However, comparison of PRIⱼ with the Tₙₒᵣₘ index showed that PRIⱼ is an early water stress index and provides information on the state of the vegetation cover at all stages of wheat development. The study's findings can have a significant impact on the use of the PRI as a water stress indicator, helping in the optimal irrigation of crops.
Why it matches plant phenotyping methodsPRIと地表面温度からコムギの水ストレスを推定する指標を開発・補正し、LAIや水ストレス係数などで検証しており、植物状態の取得手法が中心である。
abstractthis study investigated the effectiveness of the photochemical reflectance index (PRI) and a normalized surface temperature index (Tₙₒᵣₘ) for proxy detection of the water stress of winter wheat crops.
• Breeding for drought tolerance is becoming a necessity for most of the main crops, including soybean. • Phenotyping for physiological traits is considered unfeasible in breeding strategy focuses on abiotic stress tolerance. • Spectroscopy data can be used for high-throughput phenotyping methodology for hard-to-measure traits. • PLSR models successfully predicted physiological parameters at leaf-level. • Hyperspectral data can be used as a selection methodology for physiological traits in soybean cultivars under drought conditions. Understanding cultivars' physiological traits variations under abiotic stresses is critical to improve phenotyping and selections of resistant crop varieties. Traditional methods of accessing physiological traits in plants are costly and time consuming, which prevents their use in breeding programs. Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing phenotyping several genotypes in a high-throughput manner. We collected spectroscopy data of twenty soybean cultivars planted under well-watered and drought conditions during the reproductive phase. At 20 days after drought was imposed, we measured leaf pigments content (chlorophyll a and b, and carotenoids), specific leaf area, electrons transfer rate, and photosynthetic active radiation. At 28 days after drought imposition, we measured leaf pigments content, specific leaf area, relative water content, and leaf temperature. Partial least square regression models accurately predicted leaf pigments content, specific leaf area, and leaf temperature (cross-validation R 2 ranging from 0.56 to 0.84). Discriminant analysis using 54 wavelengths was able to select the best-performance cultivars regarding all evaluated physiological traits. We showed the great potential of using spectroscopy as a feasible, non-destructive, and accurate method to estimate physiological traits and screening of superior genotypes.
Why it matches plant phenotyping methods葉スペクトロスコピーとPLSRを用いて生理形質を非破壊・高スループットに推定する手法を開発・適用し、予測性能も評価しているため、フェノタイピング手法が中心である。
abstractSpectroscopy data can be used for high-throughput phenotyping methodology for hard-to-measure traits.
MaizeRaspberryTomatoThermalLeafSegmentationStress / disease detectionStress response / tolerancePlant / canopy temperature
Plant stress in California has become a significant issue in recent years due to a combination of drought, malnutrition, and infections. There is an urgent need to develop a cost-effective, time-efficient, and reliable method to address this issue. This paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves. The design of experiment (DOE) is divided into two phases. Phase one involved selecting three types of plants that represent significant stress factors in California - drought, infections, and malnutrition. The plants selected were raspberry, cherries, corn tomato, eggplant, and oleander. For each type of plant, three areas were chosen that each represented a stage of the plant’s stress: no stress (healthy), early stress, and fully stressed. Twenty points of surface thermal temperature were taken from each area of the plant leaf, and t-tests were conducted to calculate the p-value. The experiment indicates that thermal imaging techniques can be used for early detection in raspberry (drought and malnutrition) (p< 0.0001), cherries (drought) (p= 0.2996), corn (drought) (p< 0.0001), tomato (infections) (p< 0.0001), and eggplant (infections) (p< 0.0001), oleander (infections) (p< 0.0001). Phase two focused on developing a method to monitor the progressive development of plant stress throughout the entire drought process. A corresponding thermal model was built to understand the stress mechanism better for management of irrigation scheduling and plant phenotyping. The plants chosen were gardenia, tomato, and cucumber. In addition to recognizing thermal patterns throughout the process, an image processor was also created by code to calculate the percentage of healthy versus diseased area of the plant. The immediate application for this research is that it offers an advanced, non-invasive method for early detection of various plant stressors. This provides an effective solution for farmers to combat climate change and reduce plant losses due to infections, promoting much more sustainable agriculture.
Why it matches plant phenotyping methods植物葉のストレス状態を赤外線熱画像で検出し、さらに画像処理で健全・罹病領域を定量化する手法の開発が主題であり、植物フェノタイピング手法が中心である。
abstractThis paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves.
Urban green infrastructure (UGI) plays a vital role in mitigating climate change risks, including urban development-induced warming. The effective maintenance and monitoring of UGI are essential for detecting early signs of water stress and preventing potential fire hazards. Recent research shows that plants close their stomata under limited soil moisture availability, leading to an increase in leaf temperature. Multi-spectral cameras can detect thermal differentiation during periods of water stress and well-watered conditions. This paper examines the thermography of five characteristic green wall and green roof plant types (Pachysandra terminalis, Lonicera nit. Hohenheimer, Rubus tricolor, Liriope muscari Big Blue, and Hedera algeriensis Bellecour) under different levels of water stress compared to a well-watered reference group measured by thermal cameras. The experiment consists of a (1) pre-test experiment identifying the suitable number of days to create three different levels of water stress, and (2) the main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages. The thermal images were captured analyzed from three different types of green infrastructure. The method was suitable to detect temperature differences between plant types, between levels of water stress, and between GI types. The results show that leaf temperatures were approximately 1–3 °C warmer for water-stressed plants on the green walls, and around 3–6 °C warmer on the green roof compared to reference plants with differences among plant types. These insights are particularly relevant for UGI maintenance strategies and regulations, offering valuable information for sustainable urban planning.
Why it matches plant phenotyping methods熱画像・ドローンを用いて植物の水ストレスを検出する方法の適用性を実験的に評価しており、植物状態の取得・判定が研究の中心である。
abstractthe main experiment tested the suitability of thermal imaging with a drone to detect water stress in plants across three different dehydration stages.
FlowerPhysiological trait estimationGrowth / time-series analysisPlant / canopy temperature
The high biosynthetic and energetic demands of floral thermogenesis render thermogenic plants the ideal systems to characterize energy metabolism in plants, but real-time tracking of energy metabolism in plant cells remains challenging. In this study, a new method was developed for tracking the mitochondrial energy metabolism at the single mitochondria level by real-time imaging of mitochondrial superoxide production (i.e., mitoflash). Using this method, we observed the increased mitoflash frequencies in the receptacles of Nelumbo nucifera Gaertn. at the thermogenic stages. This increase, combined with the higher expression of antioxidant response-related genes identified through time-series transcriptomics at the same stages, shows us a new regulatory mechanism for plant redox balance. Furthermore, we found that the upregulation of respiratory metabolism-related genes during the thermogenic stages not only correlates with changes in mitoflash frequency but also underscores the critical roles of these pathways in ensuring adequate substrate supply for thermogenesis. Metabolite analysis revealed that sugars are likely one of the substrates for thermogenesis and may be transported over long distances by sugar transporters. Taken together, our findings demonstrate that mitoflash is a reliable tool for tracking energy metabolism in thermogenic plants and contributes to our understanding of the regulatory mechanisms underlying floral thermogenesis.
Why it matches plant phenotyping methods植物細胞のミトコンドリアエネルギー代謝をリアルタイム画像化する手法を開発し、熱産生状態の追跡に適用しており、表現型取得法が研究の中心である。
abstracta new method was developed for tracking the mitochondrial energy metabolism at the single mitochondria level by real-time imaging of mitochondrial superoxide production (i.e., mitoflash).
Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to improving water efficiency and achieving sustainable agriculture. Accurate and rapid access to crop water information is an essential prerequisite for precise irrigation decisions. Traditional moisture detection methods based on soil moisture and crop physiological parameters are faced with the problems of variable field conditions, low efficiency and lack of spatial information, which can be extremely limited in practical applications. By contrast, unmanned aerial vehicle (UAV) remote sensing has the advantages of low cost, small size, flexible data acquisition time, and easy acquisition of high-resolution image data. Therefore, UAV remote sensing has become an easy and efficient method for crop water information monitoring. This study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology. First, the mechanism of crop water stress analysed by UAV is elaborated, focusing on the relationship between canopy temperature, evapotranspiration, sun-induced chlorophyll fluorescence (SIF) and crop water stress. Next, various UAV imaging technologies for crop water stress monitoring are presented, including optical sensing systems, red, green and blue (RGB) images, multi-spectral sensing systems, and hyper-spectral sensing systems. Subsequently, the application of machine learning algorithms in the field of UAV monitoring of crop water information is outlined, demonstrating their potential for data processing and analysis. Finally, new directions and challenges in UAV-based crop water information acquisition and processing are synthesised and discussed, with special emphasis on the prospects of data assimilation algorithms and non-stomatal restriction in monitoring crop water information in the future. This study provides a comprehensive comparison and assessment of the mechanisms, technologies and challenges of UAV-based crop water information monitoring, providing insights and references for researchers in related fields. ● Unmanned aerial vehicle (UAV) remote sensing is a vital method for crop water information monitoring. ● The paper focused on the principles and methods of crop water stress analysis using the UAV technology. ● The application of machine learning algorithms was discussed. ● The challenges and future directions were expounded.
Why it matches plant phenotyping methodsUAVセンサーによる作物の水ストレスという植物状態の取得・解析手法を体系的に整理したレビューであり、フェノタイピング手法が中心である。
abstractThis study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology.
PURPOSE: High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes. METHODS: A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (gₛ), leaf (Ψₗₑₐf) and stem (Ψₛₜₑₘ) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements. RESULTS: Results revealed the importance of TIR variables to estimate physiological proxies (gₛ, Ψₗₑₐf, Ψₛₜₑₘ) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period. CONCLUSION: This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.
Why it matches plant phenotyping methodsUAV搭載の熱・マルチスペクトル・3D画像を組み合わせ、ブドウの水ストレス、生理指標、LAIを推定する手法を評価・検証しており、表現型取得とモデル性能評価が研究の中心である。
abstractthis study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods
The use of non-destructive, continuous, and rapid canopy temperature (Tc) indices for crop stress diagnosis is of significant importance for improving crop water productivity (WP). However, the comprehensive applicability of the crop water stress index (CWSI), grounded in Tc, in diagnosing both single and combined water and salt stress, as well as characterizing physiological and growth traits, remains inadequately explored. We aim to investigate the ability of CWSI to diagnose single and combined water and salt stress and to test whether a non-water stress baseline (NWSB) with or without growth stage and genotype differences influences CWSI to characterise maize leaf physiological and growth traits. Here, we measured the Tc using infrared radiation thermometers of two maize genotypes (XY335 and ZD958) under both single and combined water and salt stress over two growing seasons, compared the differences of NWSB in three growth stages, and established CWSI. Our analysis involved scrutinizing the differences in characterizing crop physiology and growth traits between CWSI calculated using NWSB with and without growth stage differentiations. Our findings indicated that Tc is modulated by an interplay of soil water content, VPD, and soil salinity. The NWSB exhibited variations with both growth stage (pₛₗₒₚₑ < 0.001) and genotype (pₛₗₒₚₑ or pᵢₙₜₑᵣcₑₚₜ < 0.01). The CWSI can diagnose single and combined water and salt stress suffered by maize. Under no stress, and single and combined water and salt stress, CWSI was significantly correlated with stomatal conductance (R² ≥ 0.31, p < 0.1) and net photosynthetic rate (R² ≥ 0.38, p < 0.1), rather than with hydraulic traits. The mean CWSI across the entire growth period closely correlated with leaf area index (LAI), canopy photosynthetically active radiation interception, biomass, yield, and evapotranspiration across varying treatments (R² ≥ 0.54, p < 0.1). Contrary to CWSI derived from NWSB without growth stage variations, utilizing CWSI with growth stage distinctions better characterized physiological traits, while the former was more suitable for delineating yield and WP. This research underscores the efficacy of CWSI for stress diagnosis and the evaluation of gas exchange and productivity in maize under both single and combined soil water-salt stress. This investigation significantly propels forward the implementation of crop-centric irrigation strategies aimed at optimizing water utilization efficiency.
Why it matches plant phenotyping methods赤外線によるキャノピー温度からCWSIを算出し、成長段階・遺伝子型別の基準線を比較して、水・塩ストレスおよび生理・成長形質の評価性能を検証しているため、フェノタイピング手法が中心である。
abstractWe aim to investigate the ability of CWSI to diagnose single and combined water and salt stress and to test whether a non-water stress baseline (NWSB) with or without growth stage and genotype differences influences CWSI to characterise maize leaf physiological and growth traits.
Common beanField / plotThermalLeafStress / disease detectionPlant / canopy temperature
Among the factors causing yield losses in agricultural fields, plant diseases are known to be one of the most significant. For many years, pesticides have been used to combat these diseases. However, due to the unintended toxic effects of pesticides on non-target organisms in recent years, there have been restrictions on their usage. Therefore, there has been an increased interest in alternative methods to chemical control in combating plant diseases. Among these alternative methods, thermal imaging, widely used within the scope of precision agriculture practices, holds a significant position. This study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods. According to the obtained results, it has been determined that leaves infected with the pathogen have a temperature approximately 2 ºC lower than healthy leaves. Surface temperatures of healthy and infected leaves were measured at 60-minute intervals for three weeks. Throughout this three-week period, it was observed that the average daily temperatures of infected leaves and healthy leaves were below ambient temperatures. Thermal imaging is considered to play a crucial role in the potential early detection of plant diseases.
Why it matches plant phenotyping methods熱画像を用いて感染葉と健全葉の温度差から植物病害を早期検出する方法を評価しており、植物状態の取得・判定が研究の中心です。
abstractThis study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods.
RiceThermalLeafStress / disease detectionStress response / tolerancePlant / canopy temperature
Diatraea saccharalis (Fabricius) is one of the main pests of rice crops and its early detection, that is, before the plants show damage, is essential to avoid yield losses and define effective and rational control. This work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels. Between 2019 and 2020, two experiments were conducted in a protected environment with the cultivars IR 40 and BR IRGA 409, which presented, in a previous study, different resistance reactions. Rice plants grown in pots were manually infested with first-instar larvae of D. saccharalis, from 0 to 10 caterpillars/plant, with the plants kept in cages covered with voile fabric throughout the test. With the adjustment of regression models, it was noticed that the leaf surface temperature is related to the level of infestation and could be used to detect which IR 40 is susceptible.
Why it matches plant phenotyping methods赤外線サーモグラフィーでイネ葉面温度から害虫感染レベルと感受性を推定する方法が研究の中心であり、植物状態の取得・推定に該当する。
abstractThis work aimed to model the infrared-thermal responses of rice cultivars to D. saccharalis infestation levels.
Reproduction assets foundThe preprint's Data Availability Statement points to the authors' experimental dataset (leaf temperature, infestation, and resistance trait measurements) deposited in Harvard Dataverse under DOI 10.7910/DVN/Q1DRVV. This is a paper-specific, publicly accessible phenotype dataset. No author analysis code or trained modelDataset · publicn (AIC) and the root-mean-squared-error (RMSE) were used to choose and evaluate the
goodness-of-fit of models. The analyses were performed with the R software (www.r-project.org).
Data Availability Statement: The experimental data that support the results and findings of this study are
openly available in Harvard DataverseV1 at https://doi.org/10.7910/DVN/Q1DRVV.
References
1. Bortoli, S. A. D., Dória, H. O. S., Albergaria, N. M. M. S., & Botti, M. V. (2005). Biological aspects and damage
of Diatraea saccharalis (Lepidoptera: Pyralidae) in sorghum, under different doses of nitrogen and
potassium. Ciência e Agrotecnologia, 29(2), 267-273. https://doi.org/10.1590/S1413-70542005000200001Open asset ↗Harvard Dataverse · 10.7910/DVN/Q1DRVVpdf-layout-page:7 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
AppleField / plotLiDAR / point cloudThermalFruitPhysiological trait estimation2D/3D reconstructionSegmentationPlant / canopy temperatureWater status / transpiration
In applied ecophysiological studies related to global warming and water scarcity, the water status of fruit is of increasing importance in the context of fresh food production. In the present work, a fruit water stress index ( FWSI ) is introduced for close analysis of the relationship between fruit and air temperatures. A sensor system consisting of light detection and ranging (LiDAR) sensor and thermal camera was employed to remotely analyze apple trees ( Malus x domestica Borkh. "Gala") by means of 3D point clouds. After geometric calibration of the sensor system, the temperature values were assigned in the corresponding 3D point cloud to reconstruct a thermal point cloud of the entire canopy. The annotated points belonging to the fruit were segmented, providing annotated fruit point clouds. Such estimated 3D distribution of fruit surface temperature ( T Est ) was highly correlated to manually recorded reference temperature ( r 2 = 0.93). As methodological innovation, based on T Est , the fruit water stress index ( FWSI Est ) was introduced, potentially providing more detailed information on the fruit compared to the crop water stress index of whole canopy obtained from established 2D thermal imaging. FWSI Est showed low error when compared to manual reference data. Considering in total 302 apples, FWSI Est increased during the season. Additional diel measurements on 50 apples, each at 6 measurements per day (in total 600 apples), were performed in the commercial harvest window. FWSI Est calculated with air temperature plus 5 °C appeared as diel hysteresis. Such diurnal changes of FWSI Est and those throughout fruit development provide a new ecophysiological tool aimed at 3D spatiotemporal fruit analysis and particularly more efficient, capturing more samples, insight in the specific requests of crop management.
Why it matches plant phenotyping methodsLiDAR・熱画像を幾何較正して温度注釈付き3D点群を構築し、果実表面温度と水ストレス指数を抽出・検証する方法が研究の中心である。
abstractA sensor system consisting of light detection and ranging (LiDAR) sensor and thermal camera was employed to remotely analyze apple trees
Frost is an extreme temperature event that significantly impacts crops, particularly in Mediterranean-type climates. Current frost damage assessment techniques are heavily dependent on traditional temperature logger data and manual inspection of the crops after a suspected frost event, an approach that can be erroneous, labour-intensive and can lead to delayed management decisions. This study investigates a new technique to automatically detect two crucial stages of frost in on-field plants, i.e., exposure to freezing temperatures with and without ice formation (crystallisation and supercooling), using machine learning (ML) models trained on infrared thermal (IRT) images. Our dataset consists of IRT images of on-field wheat plants collected during the winter growing season. We demonstrate that our approach based on classification accuracy curves, can detect ice nucleation and freezing point temperatures with four ML models, extreme gradient boosting (XGBoost), random forest (RF), convolutional neural networks (CNN) and ResNet-50. We find that RF detects frost events, i.e., crystallisation for frost and supercooling for non-frost night from the accuracy curves with fastest classification time (approx. 17 ms per image). Our study provides important insights into a primary building block for the future development of automatic and real-time on-field plant frost monitoring systems.
Why it matches plant phenotyping methods赤外線熱画像と機械学習により、圃場のコムギ植物の凍結・過冷却状態を自動検出する手法を開発・比較しており、植物状態の取得が中心的です。
abstractThis study investigates a new technique to automatically detect two crucial stages of frost in on-field plants
Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Tₛ). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Tₛ, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Tₛ, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Tₛ became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R²), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.
Why it matches plant phenotyping methods作物のLAIという植物形質を赤縁クロロフィル指数から推定するモデルを開発し、複数作物・生育段階で検証している。ET推定への応用も含むが、LAI取得・推定手法が中心的な技術貢献である。
abstractIn this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ) considering the hysteresis between them.
If damaged potatoes are detected promptly during harvest, the spread of potato decay in potato storage warehouses is reduced and timely loss control is achieved. Therefore, damaged potatoes must be identified and removed during harvest. A field collaborative recognition method for thermal infrared imaging of damaged potatoes is proposed in this work. An experimental device was developed to detect and identify three commonly damaged potatoes using this method. First, a heat transfer model for damaged potatoes based on the field synergy theory was established and heat transfer analysis was conducted. The reason for the temperature difference between the damaged and intact parts of potatoes was the difference in convective heat transfer intensity between the two and the hot air, as well as the thermal properties of the potato skin and flesh. Then, the surface temperature distribution of the damaged potato under various hot air inlet angles was obtained using finite element simulation tests. According to the imaging effect of the thermal image, the optimal inlet angle was determined to be 90° vertically. Finally, the operating parameters of the potato-screening device were optimized and analyzed. The optimal parameter combination obtained is as follows: The conveyor belt speed was 0.37 m/s, the hot air speed was 3.5 m/s, and the hot air temperature was 45 °C. Actual experiments were conducted on this device. The experimental results indicated that the accuracy, precision, recall, and F-score evaluation values of detecting damaged potatoes were 96%, 94.6%, 97.6%, and 0.961 respectively. The potato thermal infrared damage detection method can meet the technical requirements of grading detection during potato harvest.
Why it matches plant phenotyping methods熱赤外画像を用いてジャガイモの損傷状態を検出する装置・手法を開発し、実験で性能評価しており、植物状態の取得が中心的です。
abstractA field collaborative recognition method for thermal infrared imaging of damaged potatoes is proposed in this work.
Abstract Thermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.
Why it matches plant phenotyping methodsUAS熱画像からブドウの水分状態を推定する複数の熱指標を比較・検証しており、植物生理状態の取得方法が研究の中心である。
abstractThe aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS).
By means of a unique, low vibration circular conveyor system, plant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees, with seven Malus x domestica Borkh. 'Gala' apple trees in each block. Measurements took place four times during the season. Additionally at harvest, diurnal courses were recorded with 18 readings during three days. The data are provided as [i] raw data (3D point clouds of 3 blocks of trees scanned from right and left sides and thermal images), [ii] processed 3D point clouds of canopies annotated with temperature data from the thermal camera, and [iii] manually segmented 3D point clouds of fruit, representing the spatially-resolved fruit surface temperature (FST). Manual FST readings are provided on each measuring date and during diurnal courses. The fruit data are capturing 1236 FST, providing temperature distribution as 3D point cloud and one manually recorded reference FST per fruit. Additionally, fruit size and colour were measured for each fruit, despite for the first date, when fruit were too small for colour readings. Weather data are provided from a station located in the orchard. Usage of data could be (a) in developing methodology for 3D point cloud processing based on raw data, accomplished with reference FST data. Furthermore, (b) the pre-processed point clouds of fruit surface temperature can be reused in ecophysiological studies related to global warming, optimizing fruit production systems, and other. Because the sensors and trees were measured from the same angle and distance, time series analysis of the canopies would be possible.
Why it matches plant phenotyping methodsLiDARと熱画像を統合し、果実表面温度を3D点群として取得・注釈化した再利用可能なデータセットであり、植物表現型取得手法とデータ提供が中心です。
abstractplant sensors capturing light detection and ranging (LiDAR) unit and thermal camera were moved on the same route, around blocks of apple trees
Reproduction assets foundThe paper is a Data in Brief article describing a public Zenodo deposit containing the paper's own phenotyping measurements: raw LiDAR point clouds, thermal images, temperature-annotated 3D point clouds of apple canopies, 1236 manually segmented fruit point clouds with FST reference readings, fruit size/colour data, anDataset · publicocation
The conveyor system is located 52.4673340479, 12.9606589643 in the experimental station of Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany (ATB). Data repository is stored on Zenodo server [ 1 ]
Data accessibility
Repository name: Zenodo
Doi: https://doi.org/10.5281/zenodo.10792723
url: https://zenodo.org/records/10792723
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The stationary conveyor system enabled repeated readings of apple tree canopies, with minimum vibration due to electric engine of the conveyor, and equal geometry between sensors and samples in all measurements. The value of 3D point clouds obtained with LiDAR sensor was enhanced bOpen asset ↗Zenodo · 10.5281/zenodo.10792723lines:46-71Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 7 Sept 2026
Application of infrared thermography (IRT) for real-time plant stress detection has grown rapidly in recent years. Although the technology has been well established for crops grown in fields and glasshouses, its feasibility for vertical farms has not been tested extensively. In this study, temporal monitoring of stress induced by root dehydration in purple basil plantlets inside a vertical farm was performed to identify bottlenecks in real-time stress detection via IRT. Subsequently, potential solutions were investigated via machine learning by implementing support vector machines for supervised classification. Edge effects as well as proximity to air vents were identified as the major causes of positional variation in plant temperature that could lead to misprediction of stress. Binary, ternary, and quaternary classification models were trained using thermal images from two, three, and four levels of stress, respectively, to assess model performance. Binary classification models trained with plants experiencing medial and high levels of stress were able to identify stressed plants with high accuracy (81–94%). Further, binary models trained using plants under medial levels of stress generated a continuous probability distribution for stress prediction when plotted against plant temperature. In contrast, models trained using samples experiencing high stress generated distinct probabilistic clusters for the unstressed and highly stressed plants, but were unable to classify medial stress samples reliably. Similarly, ternary and quaternary models were able to better predict very high and very low levels of stress than intermediate stress levels. Hence, our findings suggest that binary classification models trained using samples under medial levels of stress would be helpful in overcoming spatiotemporal variations in canopy thermal profile by providing reliable probabilistic estimates of plant stress within a vertical farming system. Key points Plant stress detection in vertical farms via thermal imaging may be challenging because perceptible plant temperature can be strongly influenced by its microenvironment. Thermal image analysis via supervised machine learning allows the development of robust prediction models that can overcome such factors to identify stressed plants. Binary classification machine learning models can reliably identify stressed plants as well as provide probabilistic estimates for the degree of stress.
Why it matches plant phenotyping methods赤外線熱画像による植物ストレス状態の検出と、機械学習による分類・確率推定が研究の中心であり、垂直農場での技術的課題と性能を評価している。
abstractApplication of infrared thermography (IRT) for real-time plant stress detection has grown rapidly in recent years.
Field / plotWhole plant / canopy / plot / fieldStress / disease detectionPlant / canopy temperatureWater status / transpiration
The lower limit temperature in the crop water stress index (CWSI) model refers to the canopy temperature ( T c ) or the canopy-air temperature differences ( dT ) under well-watered conditions, which has significant impacts on the accuracy of the model in quantifying plant water status. At present, the direct estimation of lower limit temperature based on data-driven method has been successfully used in crops, but its applicability has not been tes-ted in forest ecosystems. We collected continuously and synchronously T c and meteorological data in a Quercus variabilis plantation at the southern foot of Taihang Mountain to evaluate the feasibility of multiple linear regression model and BP neural network model for estimating the lower limit temperature and the accuracy of the CWSI indicating water status of the plantation. The results showed that, in the forest ecosystem without irrigation conditions, the lower limit temperature could be obtained by setting soil moisture as saturation in the multiple linear regression mo-del and the BP neural network model with soil water content, wind speed, net radiation, vapor pressure deficit and air temperature as input parameters. Combining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured T c and dT could realize the non-destructive, rapid, and automatic diagnosis of the water status of Q. variabilis plantation. Among them, the CWSI obtained by combining the lower limit temperature determined by the dT under well-watered condition calculated by the BP neural network model and the upper limit temperature was the most suitable for accurate monitoring water status of the plantation. The coefficient of determination, root mean square error, and index of agreement between the calculated CWSI and measured CWSI were 0.81, 0.08, and 0.90, respectively. This study could provide a reference method for efficient and accurate monitoring of forest ecosystem water status.
Why it matches plant phenotyping methodsQuercus variabilis plantationの樹冠温度と気象データからCWSIを推定し、水分状態を非破壊・自動診断する手法を開発・評価しており、植物状態の取得方法が中心である。
abstractCombining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured T c and dT could realize the non-destructive, rapid, and automatic diagnosis of the water status of Q. variabilis plantation.
When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44-70, 32-44, 42-58, and 76-97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.
Why it matches plant phenotyping methods冬小麦の冠温度を用いたCWSIによる光合成パラメータ・水ストレス推定について、時間遅れ補正の手法開発とモデル改良、機械学習による予測精度評価が研究の中心であり、植物生理状態のセンシング手法に該当する。
abstractBased on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters.
To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.
Why it matches plant phenotyping methodsPlantEyeを用いたデジタルフェノタイピングの適用と精度評価が研究上の主要要素であり、植物のキャノピー温度や成長状態を測定し、植物サイズによる測定精度低下も検討している。
abstractPlants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this potato pot-binding phenotyping study.Dataset · publicThe datasets generated and analysed for this study can be found in the Zendo repository at https://doi.org/10.5281/zenodo.10707587 .Open asset ↗Zenodo · 10.5281/zenodo.10707587lines:845-856Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.
Why it matches plant phenotyping methods高スループット画像・複数センサーによる形態・生理形質の取得と、最適化したストレス処理およびフェノタイピングプロトコルが研究の中心的手法として記述されているため、実質的なフェノタイピング手法の適用に該当する。
titleHigh Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods高解像度画像・深層学習による器官レベル老化モニタリングと熱画像による連続的なキャノピー温度測定を開発・適用し、stay-green機能の表現型評価法として検証しているため、方法が中心的である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Reproduction assets foundThe paper publicly deposits its manually annotated segmentation datasets (target-domain patches for the off-nadir stem/ear segmentation model) via the ETH Zurich research repository. All other raw phenotyping data (thermal images, physiological measurements) is only available on request from the authors. Generic tools/Dataset · publicd through logical operations to obtain the fractions of green, chlorotic, and necrotic tissues for each vegetation component. For details, refer to ( Anderegg et al., 2023 ). The annotated data sets representing the target domain will be made freely available via the Repository for Publications and Research data of ETH Zürich ( https://doi.org/10.3929/ethz-b-000668219 ).
Figure 2
Effects of canopy shading on agronomic traits and canopy characteristics. Effects of shading on (A) grain yield, (B) above ground vegetative dry biomass (total above ground biomass after threshing), (C) peduncle length, (D) plant height, (E) spike volume, (F) thousand kernel weight, (G) grain protein concentration.Open asset ↗Repository for Publications and Research data of ETH Zürich · 10.3929/ethz-b-000668219lines:58-67Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Canopy temperature and new shoot counts are pivotal traits for evaluating the adaptability and productivity of slash pine (Pinus elliottii Engelm.). Traditional methods for assessing these traits are labor-intensive and lack the required accuracy. Despite their ecological and economic importance, the genetic and molecular underpinnings of these traits remain largely unexplored. In this study, we utilized UAV remote sensing technology for conducting high-throughput phenotyping of canopy temperature and new shoot counts and performed a Genome-Wide Association Study (GWAS) to identify key candidate genes associated with these traits. Our Manhattan plot analysis revealed intriguing patterns. The GWAS analysis of both July and August identified the same four key candidate genes for new shoot counts including scaffold119881_56626, scaffold119881_56642, scaffold96999_96025, and scaffold7133_48553 respectively. However, for canopy temperature, no key candidate genes were identified in the overall monthly analysis. Interestingly, diurnal variations in canopy temperature influenced the identification of key genes. While no key genes were identified during the afternoon and evening, two were found in the morning: scaffold33143_278946 and super3157_662643. Our UAV-based approach proved to be highly accurate and portable, with significant heritability estimates for both traits. This study represents the first GWAS analysis on these traits and underscores the importance of integrating high-throughput phenotyping and genotyping technologies for advancing forest genetic improvement programs, particularly in the selection of key candidate genes influenced by diurnal and monthly variations.
Why it matches plant phenotyping methodsUAVリモートセンシングによるキャノピー温度と新梢数の高スループット表現型取得が研究の主要な技術的要素であり、精度・携帯性・遺伝率も評価しているため、GWAS併用でも方法適用研究として収載する。
abstractwe utilized UAV remote sensing technology for conducting high-throughput phenotyping of canopy temperature and new shoot counts
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Why it matches plant phenotyping methods赤外線熱画像から pepper 葉の温度分布指標を抽出し、無症状期の病徴を検出・評価する手法が研究の中心であるため、植物病害フェノタイピング手法として含める。
abstractThe present study confirms the feasibility of the identification of presymptomatic features in pepper early blight by infrared thermography during the incubation period.
Advances in retrieval of solar-induced chlorophyll fluorescence (SIF) provide a promising and independent approach for quantifying gross primary production (GPP) across spatial scales. Recent studies have highlighted the prominent role of qL, the fraction of open Photosystem II (PSII) reaction centers, in mechanistically modeling GPP from remote sensing SIF. However, due to the limited availability of simulated and experimental data, a comprehensive understanding of qL responses to environmental and physiological variations has yet to emerge, and as a consequence, prediction of qL across leaf and canopy scales is still in an early stage. Based on a global sensitivity analysis of a recently developed mechanical model of photosynthesis, we find that the broadband total SIF emitted from PSII (SIFTOT_FULL_PSII) and leaf temperature (TLₑₐf) are the two major predictors of qL. A leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLₑₐf over a wide range of environmental conditions. From these measurements, we show that qL can be modelled as a hyperbolic function of SIFTOT_FULL_PSII with only one temperature-related parameter m which increases with temperature, but decreases rapidly as temperatures exceed the optimum temperature. It is suggested that m can be mathematically modelled by a peaked function. The results of the leaf-level experiments on winter wheat demonstrate that the proposed model predicts qL with high accuracy (R² ≥ 0.91, rRMSE ≤ 8.46%) under diverse light and temperature conditions. The essential steps necessary to apply it at canopy scale, including estimating the escape fraction, removing fluorescence emitted from Photosystem I, and reconstructing SIFTOT_FULL_PSII from top-of-canopy (TOC) narrowband SIF, are also presented. Our results confirm that estimated GPP using SIF-informed qL agrees well with measured GPP at a winter wheat site (R² = 0.81, rRMSE = 12.03%). The key benefit of SIF-informed approach is that SIFTOT_FULL_PSII provides critical information on the collective influence of the sub-canopy light environment on qL, avoiding the requirement to explicitly estimate qL at different canopy depths, potentially promoting the ability of SIF to mechanistically quantify photosynthetic CO₂ assimilation at large scales.
Why it matches plant phenotyping methodsSIFと温度の同時計測およびモデル化により、植物の生理状態qLとキャノピー光合成を定量推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA leaf-level instrument is designed to obtain concurrent measurements of qL, SIFTOT_FULL_PSII, and TLₑₐf over a wide range of environmental conditions.
ABSTRACT An integrated sensing device for irrigation scheduling was developed to assess the soil–plant–atmosphere continuum for irrigation scheduling. A field experiment was carried out to evaluate ISDI performance and CWSI estimation across various irrigation regimes in wheat crop at WTC farm, ICAR-IARI, New Delhi, India. The experiment considered were full irrigation (FI) and various deficit irrigation levels (DI-15, DI-30, DI-45, and DI-60), receiving 15, 30, 45, and 60% less water in comparison to FI, respectively. The calibration and performance of the ISDI sensor probes was done with gravimetric methods along with time domain reflectometry (TDR) and a handheld infrared thermometer. The field calibration of the ISDI's soil moisture probe and TDR gave promising results, with R2 values ranging from 0.76 to 0.81 and 0.81 to 0.86, respectively, for soil depths up to 45 cm. ISDI's infrared sensor probe also demonstrated strong alignment with a handheld infrared thermometer (R2: 0.95), indicating reliable methods. Furthermore, a regression equation of lower baseline and upper threshold for CWSI computation was derived as (Tc–Ta)ll = 1.97 × VPD – 1.43 (R2:0.86) and 1.93 °C, respectively. It was recommended to initiate irrigation when CWSI ≥ 0.35 for wheat to achieve optimal crop yields.
Why it matches plant phenotyping methods小麦の水ストレス状態を推定する統合センシング装置を開発し、土壌水分・赤外温度センサーとCWSI推定を校正・検証しており、植物状態の取得手法が中心である。
abstractAn integrated sensing device for irrigation scheduling was developed to assess the soil–plant–atmosphere continuum for irrigation scheduling.
Abstract Aims Considering time lag effects between atmospheric temperature (Ta) and canopy temperature (Tc) may improve the accuracy of Crop Water Stress Index (CWSI) inversions of photosynthetic parameters, which is crucial for enhancing the precision in monitoring crop water stress conditions. Methods In this study, four moisture treatments were set up, T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and grey time-lag correlation analysis; Based on the time lag parameter, we modified CWSI theoretical and empirical model, and assessed the impact of time lag effects on the accuracy of CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation of CWSI after time-lag correction. Results The results showed that: (1) The time lag parameter calculated using the time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and grey time-lag correlation an-alysis were 44–70, 32–44, 42–58, and 76–97 min. (2) CWSI empirical model corrected by the time-lag mutual information method had the highest correlation with photosynthetic parameters. (3) GA-SVM had the highest prediction accuracy for CWSI empirical model corrected by the time-lag mutual information method. Conclusions Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters,which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.
Why it matches plant phenotyping methods冬小麦の冠温度を用いたCWSI推定について、時間遅れ補正手法を開発・比較し、光合成パラメータおよび水ストレス診断への精度を評価しており、植物状態の取得・推定法が中心である。
abstractWe quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and grey time-lag correlation analysis; Based on the time lag parameter, we modified CWSI theoretical and empirical model, and assessed the impact of time lag effects on the accuracy of CWSI inversion of photosynthesis parameters.
LeafPhysiological trait estimationGrowth / development / phenologyStress response / tolerancePlant / canopy temperatureWater status / transpiration
Abstract This study presents a wearable plant tattoo sensor array designed for continuous monitoring of leaf temperature, relative water content, and biopotential. Current plant wearable sensor technologies often require relatively bulky substrates for sensor support and adhesives for leaf attachment, which potentially can hinder plant growth and affect long‐term measurements. The multifunctional tattoo sensor array overcomes these issues by adhering directly to the leaf surface without the need for additional supporting structures or glues. This array includes a biopotential electrode, a resistive temperature sensor, and an impedimetric water content sensor, all constructed using laminated gold‐on‐polymer thin‐film patterns. Due to their mechanical flexibility, stretchability, and conformability, the sensors can seamlessly attach to leaves via van der Waals force. Performances of these sensors are evaluated to explore plant responses under diverse growth environments. This sensor array is capable of both short‐term and long‐term monitoring, offering continuous data and detailed insights into plant physiological responses to various stress conditions.
Why it matches plant phenotyping methods葉の温度、含水量、バイオポテンシャルを連続取得するウェアラブルセンサーアレイの開発であり、植物生理状態の表現型取得が中心的な技術貢献である。
abstractThis study presents a wearable plant tattoo sensor array designed for continuous monitoring of leaf temperature, relative water content, and biopotential.
Leaks and clogs in drip-irrigated orchards lead to variable yields, reduced efficiency and profitability. Frequent monitoring of irrigation systems by farmers is important but costly, labor-intensive, and not easily implementable on a regular basis. Moreover, in subsurface drip-irrigation systems, it is difficult to visually detect malfunctions. The objective of this study was to develop processing methodologies based on thermal remote sensing, to produce classification models for detecting irrigation malfunctions in orchards, and distinguish between different types of malfunctions. A thermal camera mounted on an unmanned aerial vehicle platform was used to acquire thermal images in three commercial almond and jojoba plantations with subsurface drip irrigation. An image-processing pipeline was developed to extract plant-specific features, and classification models were used to detect malfunctions in individual plants. Plants were segmented using four algorithms: Otsu, continuous max-flow and min-cut, full-width-half-max, and watershed. Thirty-two features were extracted from the canopy temperature of each plant and normalized with meteorological data. The most significant features were selected using a recursive feature elimination method. Three classification models (multiclass, binary, hierarchical) were constructed using five classification algorithms. Performance was evaluated with k-fold cross-validation and an independent test set. In the almond plants orchard, the hierarchical classification approach with support vector machine (SVM) algorithms yielded 68% accuracy and 33% false-positive rate (FPR) for clog detection and 2.8% FPR for leak detection. In the jojoba plantation, the multiclass classification approach with SVM algorithms gave 82% accuracy for clog and leak detection with 0% FPR.
Why it matches plant phenotyping methodsUAV熱画像から個体別の樹冠温度特徴を抽出し、植物の灌漑異常を分類する画像処理・機械学習パイプラインを開発し、交差検証と独立テストで評価しているため、植物状態の取得・推定手法が中心である。
abstractThe objective of this study was to develop processing methodologies based on thermal remote sensing, to produce classification models for detecting irrigation malfunctions in orchards, and distinguish between different types of malfunctions.
Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p p < 0.05), providing interesting information for their early prediction.
Why it matches plant phenotyping methods赤外線サーモグラフィーを用いた小麦の圃場表現型取得が中心で、熱データから新規指標を開発し、収量予測との関連を検証している。
abstractTwo new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced.
Unmanned aerial vehicle (UAV) multispectral and thermal images, combined with machine learning models, have been widely used for high-throughput phenotyping of crop traits and have great potential for evaluating the drought tolerance of winter wheat cultivars. In order to extract the wheat canopy information from UAV images, noise removal is an essential step. Currently, soil and shadow are two of the most common noises in UAV images influencing the extraction of the canopy information, which have been widely studied in previous studies. However, the noise caused by the abnormal canopy temperature in the thermal images has yet to be addressed. Besides, the machine learning-based methods are data-intensive and cannot meet the requirements for rapid evaluation of the drought tolerance of winter wheat cultivars. In order to rapidly evaluate the drought tolerance of winter wheat cultivars, this study proposed a drought tolerance evaluation method for winter wheat cultivars based on multi-criteria comprehensive evaluation and automatic noise removal. The thermal affected zone (TAZ), in which the canopy temperature was abnormally elevated due to thermal radiation from adjacent bare soil, was proposed in this study, and an effective noise removal method was proposed by comparing the accuracy of six automatic image segmentation methods. Canopy vegetation, texture, and temperature indices were extracted from the UAV multispectral and thermal images and selected based on their correlation with the measured yield stability index (YSI). Based on the multiple canopy indices, two multi-criteria comprehensive evaluation methods, i.e., weighted sum based on principal components analysis (PCA-WS) and technique for order preference by similarity to ideal solution based on entropy weight (Entropy-TOPSIS), were used to evaluate the drought tolerance of winter wheat cultivars. The results showed that the automatic image segmentation methods could effectively remove the noises of soil, shadow, and TAZ. Removing the TAZ resulted in a significant decrease in canopy temperature for each irrigation treatment. The total score (TS) and comprehensive evaluation index (CEI) showed a significant linear relationship with the measured YSI, with a maximum R² of 0.637 and 0.636, respectively. The top five cultivars ranked by the TS and CEI had a consistency ratio of 60–80% with those selected by the measured YSI. This study indicates that the automatic noise removal and multi-criteria comprehensive evaluation have great potential in rapid evaluation of drought tolerance of winter wheat cultivars for large breeding trials.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から作物キャノピー形質を抽出するノイズ除去法と、複数形質に基づく干ばつ耐性評価ワークフローを開発・検証しており、フェノタイピング手法が中心である。
abstractan effective noise removal method was proposed by comparing the accuracy of six automatic image segmentation methods
Stomatal conductance (gs) is an indicator that allows for direct evaluation of plant water status, but it is challenging to achieve rapid monitoring in large-scale fields due to limitations in observation methods. Here this study was conducted to identify the thresholds of gs with different target yields and develop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery for buffalograss in 2022 and 2023. The results of the field experiment demonstrated that the gs rapidly response to changes in the water stress status of buffalograss. The thresholds of gs were 403 and 385 mmol m−2 s−1 for the vegetative and reproductive growth stages, respectively, with the target seed yield of 1224 kg ha−1. The gs values were classified into three levels for the vegetative growth and four levels for the in reproductive growth stage of buffalograss, respectively. The canopy temperature depression response to water stress is consistent with the gs. Based on this relationship, this study developed a gs-based diagnostic model with a random forest algorithm for buffalograss. Furthermore, a spital map of gs was created using UAV thermal infrared imagery. The modification test results indicated that the model made a good estimation of gs were good with normalized root mean square errors of 15% in the vegetative stage and 11% in the reproductive stage, respectively. Therefore, it is feasible to use thermal infrared imagery for monitoring gs and evaluating the water stress of plants in buffalograss fields.
Why it matches plant phenotyping methodsUAV熱赤外画像から気孔コンダクタンスと植物の水ストレスを推定する診断モデルを開発・検証しており、植物生理状態の取得手法が中心である。
abstractdevelop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery
Sugarcane breeding is resource-intensive and time-consuming, and could benefit substantially from the integration of aerial phenotyping (AP) for rapidly identifying genotypes with superior yield traits. The study aimed to assess the feasibility of using AP to enhance sugarcane breeding by rapidly identifying genotypes with superior yield traits. The specific objectives of the study were to: (1) assess the impacts of canopy cover and stomatal conductance on stalk dry mass yield (SDM); (2) assess the feasibility of estimating these traits with aerially sensed normalized difference vegetation index (NDVI) and canopy temperature (Tc); (3) evaluate the potential for predicting SDM from NDVI and Tc; (4) formulate best AP procedures. The study comprised a replicated field trial near Komatipoort, South Africa, with 54 genotypes grown under well-watered and water deficit conditions. Traits were measured on the ground (canopy cover and stomatal conductance) and remotely sensed from the air with a drone (NDVI and Tc) throughout the plant and first ratoon crops, and SDM was measured at harvest. Measurements were categorized by crop water status and extent of canopy cover, and phenotypic trait correlations were analyzed for these different categories. The study confirmed canopy cover and stomatal conductance as influential traits for determining SDM. Canopy cover could be used as a proxy for identifying high- and low-yielding genotypes early on in water stress-free crops. Findings suggest that high stomatal conductance benefits well-watered crops, while relatively low conductance could be advantageous in dry environments, though further investigation is needed. Canopy cover was predicted well from NDVI at partial canopy for well-watered crops, while the prediction of stomatal conductance from Tc lacked reliability. It was concluded that NDVI and Tc could be used to identify high- and low-yielding genotypes when measured earlier on in the growth cycle for well-watered crops. Results also showed potential for using water treatment differences in Tc and SDM to identify drought tolerant genotypes. Lastly, the study highlighted methodological challenges and insights for future agronomic trait prediction using AP techniques. The findings of this study will be used in further testing in the early stages of the breeding programme along with the breeding populations, ultimately helping to manage breeding strategies for target environments. This has the potential to enhance breeding efficiency and ultimately genetic gains towards productive sugarcane cultivars for the future.
Why it matches plant phenotyping methods航空センシングによるNDVI・群落温度から植物形質を推定し、予測性能と育種利用性を評価することが中心であるため。
abstractassess the feasibility of estimating these traits with aerially sensed normalized difference vegetation index (NDVI) and canopy temperature (Tc)
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Brassica vegetablesLeafPhysiological trait estimationGrowth / time-series analysisVisualization / data managementGrowth / development / phenologyStress response / tolerancePlant / canopy temperature
Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.
Why it matches plant phenotyping methods植物に非侵襲的に装着する有機e-skinセンサーを開発し、葉のひずみと表面温度を取得して表現型解析プラットフォームとして実証しているため、方法が中心的である。
abstractHere, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate.
Use of canopy temperature for deficit irrigation (DI) scheduling of sugar beet was evaluated in a 3-year plot study in a semi-arid climate. Four irrigation treatments were evaluated; full irrigation and three deficit irrigation treatments where an irrigation application of 25 mm was applied when an average daily crop water stress index (CWSI) threshold of 0.2, 0.35, and 0.55 was exceeded. There were significant irrigation treatment differences in seasonal evapotranspiration, soil water extraction, seasonal average CWSI, root yield, estimated recoverable sugar (ERS) yield, and water use efficiencies. Seasonal soil water extraction of the full irrigation treatment was significantly less than for the DI treatments in all study years. In 2021 and 2022, soil water balanced based evapotranspiration was significantly less for the DI treatments compared to the full irrigation treatment. However, there was no significant difference in root yield, ERS yield, and water use efficiencies between the full irrigation treatment and irrigating when daily CWSI exceeded 0.2 in any study year. The results indicate that irrigating when average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Calculation of sugar beet CWSI was an integral part of this study. A neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet. Calculation of non-transpiring canopy temperature is also demonstrated in the spreadsheet. The spreadsheet computes CWSI based on five inputs: solar radiation, air temperature, relative humidity, wind speed, and measured canopy temperature.
Why it matches plant phenotyping methodsキャノピー温度からCWSIを算出し、ニューラルネットワークで非水ストレス時のキャノピー温度を推定する再利用可能なExcelワークフローを提示しており、植物の水ストレス状態の取得・推定が方法論的に中心である。
abstractCalculation of sugar beet CWSI was an integral part of this study.
Use of canopy temperature for deficit irrigation (DI) scheduling of sugar beet was evaluated in a 3-year plot study in a semi-arid climate. Four irrigation treatments were evaluated; full irrigation and three deficit irrigation treatments where an irrigation application of 25 mm was applied when an average daily crop water stress index (CWSI) threshold of 0.2, 0.35, and 0.55 was exceeded. There were significant irrigation treatment differences in seasonal evapotranspiration, soil water extraction, seasonal average CWSI, root yield, estimated recoverable sugar (ERS) yield, and water use efficiencies. Seasonal soil water extraction of the full irrigation treatment was significantly less than for the DI treatments in all study years. In 2021 and 2022, soil water balanced based evapotranspiration was significantly less for the DI treatments compared to the full irrigation treatment. However, there was no significant difference in root yield, ERS yield, and water use efficiencies between the full irrigation treatment and irrigating when daily CWSI exceeded 0.2 in any study year. The results indicate that irrigating when average daily CWSI sugar beet exceeds 0.2 is an effective means for mild deficit irrigation scheduling to reduce seasonal irrigation requirements with no significant effect on root and ERS yield. Calculation of sugar beet CWSI was an integral part of this study. A neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet. Calculation of non-transpiring canopy temperature is also demonstrated in the spreadsheet. The spreadsheet computes CWSI based on five inputs: solar radiation, air temperature, relative humidity, wind speed, and measured canopy temperature.
Why it matches plant phenotyping methodsキャノピー温度からCWSI（植物の水ストレス状態）を推定する計算手法と、非水ストレス温度を推定するニューラルネットワークおよびExcel実装を具体的に文書化しており、再利用可能な表現型取得・推定ワークフローが中心的に含まれる。
abstractA neural network model was used to estimate non-water stressed canopy temperature. Documentation of the neural network model and its use to estimate non-water stressed canopy temperature are demonstrated in a Microsoft Excel spreadsheet.
PotatoThermalClassificationStress / disease detectionDisease symptoms / severityPlant / canopy temperature
This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu's threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers.
Why it matches plant phenotyping methodsサーモグラフィー画像と画像特徴量・機械学習により、ジャガイモ塊茎の乾腐病 severity を非破壊推定する方法が研究の中心である。
abstractThis study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development.
Summary Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non‐traditional top‐down approach via canopy photosynthesis modelling to identify leaf‐level photosynthetic traits that are key to differences in crop‐level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C 4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO 2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf‐level photosynthetic low‐light response and its temperature dependency are key drivers of the performance of crop‐level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.
Why it matches plant phenotyping methods新規の光合成応答測定法とベイズモデルを開発し、葉の光合成形質を推定して作物レベルRUEを説明しており、表現型取得・抽出法が研究の中心である。
abstractA novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure
Infrared thermography offers a rapid, noninvasive method for measuring plant temperature, which provides a proxy for stomatal conductance and plant water status and can therefore be used as an index for plant stress. Thermal imaging can provide an efficient method for high-throughput screening of large numbers of plants. This chapter provides guidelines for using thermal imaging equipment and illustrative methodologies, coupled with essential considerations, to access plant physiological processes.
Why it matches plant phenotyping methods植物の温度・水分状態・ストレスを赤外線サーモグラフィで測定する高スループット表現型解析のガイドラインと実施方法を扱っており、方法が中心である。
titleUsing Infrared Thermography for High-Throughput Plant Phenotyping.
Abstract Agricultural operators can predict the yield of wheat at different stages of growth, development, and harvesting and take different measures to realize precise management. The purpose of this paper is to apply agricultural mechanical engineering automation to wheat yield prediction, and a UAV multimodal data wheat yield prediction model is developed using the RMGF algorithm. Different data sources, such as vertical distribution of terrain and spatial variability, canopy height and wheat plant height, canopy temperature difference, vegetation spectral characteristics, and vegetation index, were extracted using an agricultural UAV. Then GF decomposition algorithm based on MSD decomposes the multimodal image into an approximate image and detail image, and after optimization of the fused weight map using RSA, the fused image is obtained by IMST according to the optimized weight map. The model was used to carry out regression analysis of yield prediction for three types of wheat, heat-tolerant, medium heat-tolerant, and high-temperature-sensitive, and finally predicted the wheat yield from 2015 to 2024 in a production area. It was found that the R² of the RMGF multimodal model in this paper predicted the three kinds of wheat yields as 0.7936, 0.8609, and 0.9262 with excellent accuracy results. The predicted yields were basically in line with the actual yields in the high-yield portion, with large prediction errors above 9000 kg/ha. The prediction error for wheat was within 0-2.26%, and the predicted yield in a main wheat production area was 7050 kg/ha in 2024. This study provides a feasible method for large-scale yield estimation in the main production area, which contributes to high-throughput plant phenotyping and agricultural precision reform.
Why it matches plant phenotyping methodsUAVマルチモーダルデータから植物形質を抽出・融合し、小麦収量を推定するモデルが研究の中心であり、技術精度も評価しているため。
abstracta UAV multimodal data wheat yield prediction model is developed using the RMGF algorithm
Elevated temperatures during the flowing stage contribute to heat-induced spikelet sterility in rice, posing a major threat to production considering climate change projections. Developing effective strategies for stable rice production through breeding and crop management is critical; however, our understanding of regional, seasonal, and long-term trends in rice heat exposure remains limited. Previous studies on spikelet sterility revealed that panicle temperature, estimated using a micrometeorological model and common meteorological factors, serves as a reliable indicator of rice heat exposure. In this study, we employed this model to identify the differences between panicle and air temperatures (DPAT) and their causes over the past 45 years in Japan. A gridded daily meteorological dataset covering Japan was interpolated at an hourly time step and used as input data of the micrometeorology model for estimating panicle temperatures during flowering. Statistical analysis of the resulting data revealed an increasing trend in the frequency of rice panicle heat exposure over time across many locations in Japan. During heat-receptive periods, panicle temperature generally exceeded air temperature, indicating the inadequacy of relying solely on air temperature to gauge rice heat stress. DPAT values showed substantial inter-regional variations in both mean values (from -0.5 to 3.0) and seasonality. Through machine learning and statistical methods, the relationship between DPAT and meteorological factors was characterized, delineating the effects of the meteorological factors on regional and seasonal DPAT variations. Focusing on major high-risk regions, we show that mitigation strategies should be adapted to consider regional characteristics and avoid high DPAT conditions during rice heading periods.
Why it matches plant phenotyping methods水稲穂温という植物状態を微気象モデルで推定し、推定手法を用いた長期・地域比較と機械学習解析が研究の中心であるため、計算型フェノタイピングの応用として含める。
abstractpanicle temperature, estimated using a micrometeorological model and common meteorological factors, serves as a reliable indicator of rice heat exposure.
CucumberPepper / chilliTomatoThermalLeafPhysiological trait estimationPlant / canopy temperature
Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.
Why it matches plant phenotyping methods熱画像と葉エネルギーバランスモデルにより、植物キャノピー内の吸収光量を迅速に推定・可視化するフェノタイピング手法を開発し、点測定で検証しているため。
abstractWe developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature.
Reproduction assets foundThe paper's authors explicitly state that all analysis code (R/STAN energy-balance fitting and Julia absorbed-irradiance mapping) is publicly available on their GitHub repository, which directly reproduces the paper's phenotyping computations. No separate phenotype dataset or image deposit is stated in the supplied.Code · publicData analysis
Calculations to solve Eqns 2 and 3 were run in R (R project,
v.4.2.0). The absorbed irradiance map was calculated in JULIA
(v.1.40.1; https://julialang.org/). All codes are available on
GitHub (https://github.com/jiayu0903/leaf-absorbed-irradiance.git). Statistical analysis was performed using a Student’s t-test for
paired samples to determine significant differences (P < 0.05)
between means.
Results
The temperature of sweet pepper (Fig. 2a), cucumber (Fig. S6a),
and tomato leaves (Fig. S7) showed a near-linear increase when
exposed to a brief 30 s period of irradiance fromOpen asset ↗https://github.com/jiayu0903/leaf-absorbed-irradiance.gitpdf-raw-page:7 lines:1-87Plant phenotyping relevance match · UnverifiedCrossref · checked 7 Sept 2026
The Scholander-type pressure chamber to measure midday stem water potential (MSWP) has been widely used to schedule irrigation in commercial vineyards. However, the limited number of sites that can be evaluated using the pressure chamber makes it difficult to evaluate the spatial variability of vineyard water status. As an alternative, several authors have suggested using the crop water stress index (CWSI) based on low-cost thermal infrared (TIR) sensors to estimate the MSWP. Therefore, this study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs in a drip-irrigated Cabernet Sauvignon vineyard under two levels of water stress. For this study, the MLX90614 sensor was used to measure canopy temperature (Tc), and thus compute the CWSI. The results indicated that good performance of the MLX90614 infrared thermometers was observed under laboratory and vineyard conditions with root mean square error (RMSE) and mean absolute error (MAE) values being less than 1.0 °C. Finally, a good nonlinear correlation between the MSWP and CWSI (R2 = 0.72) was observed, allowing the development of intra-vineyard spatial variability maps of MSWP using the low-cost wireless infrared sensor network.
Why it matches plant phenotyping methods低コスト熱赤外センサーネットワークを開発・検証し、ブドウ樹の水分状態（MSWP）をCWSIから推定する手法が研究の中心であるため。
abstractthis study aimed to develop a low-cost wireless infrared sensor network (WISN) to monitor the spatial variability of MSWPs
Botrytis cinerea ( B. cinerea ) causes gray mold disease (GMD), which results in physiological disorders in plants that decrease the longevity and economic value of horticultural crops. To prevent the spread of GMD during distribution, a rapid, early detection technique is necessary. Thermal imaging has been used for GMD detection in various plants, including potted roses; however, its application to cut roses, which have a high global demand, has not been established. In this study, we investigated the utility of thermal imaging for the early detection of B. cinerea infection in cut roses by monitoring changes in petal temperature after fungal inoculation. We examined the effects of GMD on the postharvest quality and petal temperature of cut roses treated with different concentrations of fungal conidial suspensions and chemicals. B. cinerea infection decreased the flower opening, disrupted the water balance, and decreased the vase life of cut roses. Additionally, the average temperature of rose petals was higher for infected flowers than for non-inoculated flowers. One day before the appearance of necrotic symptoms (day 1 of the vase period), the petal temperature in infected flowers was significantly higher, by 1.1 °C, than that of non-inoculated flowers. The GMD-induced increase in petal temperature was associated with the mRNA levels of genes related to ethylene, reactive oxygen species, and water transport. Furthermore, the increase in temperature caused by GMD was strongly correlated with symptom severity and fungal biomass. A multiple regression analysis revealed that the disease incidence in the petals was positively related to the petal temperature one day before the appearance of necrotic symptoms. These results show that thermography is an effective technique for evaluating changes in petal temperature and a possible method for early GMD detection in the cut flower industry.
Why it matches plant phenotyping methods熱画像法により感染したバラ花弁の温度という植物状態を測定し、病徴出現前の灰色かび病を検出・評価する方法を中心的に検証しているため。
abstractIn this study, we investigated the utility of thermal imaging for the early detection of B. cinerea infection in cut roses by monitoring changes in petal temperature after fungal inoculation.
Jalapeño peppers (Capsicum annuum L.) are an important agricultural product worldwide. Despite its high demand in recent years, there are few studies on its production under adverse conditions caused by environmental phenomena. Crops in protected environments such as aeroponics offer greater control of these phenomena and enable efficient use of resources. However, it is necessary to validate the cultivation technique for which new technologies are being used. One of these is the analysis of images captured in the visible and near and far infrared spectrum which involves using non-invasive techniques in order to characterize the growth of a plant and diagnose if it presents any type of stress. This study presents the characterization of vegetative growth and fruiting of jalapeño pepper plants in an aeroponic system, where the root, leaf development parameters and fruits were measured in four jalapeño pepper crops through images of plants captured in the visible (VIS), near infrared (NIR) and far infrared (IR) spectrums. Four crops of thirty jalapeño pepper plants were sown to obtain a total of one hundred and twenty plants which were characterized in the different phases of growth and fruiting. Each of the four jalapeño pepper crops were monitored for sixty days in an aeroponic system in a greenhouse. The first crop was intended to carry out tests to establish the appropriate fertigation times, the next three crops were grown under favorable conditions. Algorithms were developed in Matlab to obtain, over ten image capture sessions, the morphometric and thermal parameters of the roots (perimeter, area, length and average temperature), plants (perimeter, area, height and average temperature), and fruiting. (yield and number of fruits). The statistical analysis was carried out using the ANOVA and Tukey tests considering a value of p ≤ 0.05. The results obtained indicate that there is no significant difference between the characterizations of the four crops. This statement is also supported by the visual analysis of the growth curves parameters of the four crops. In addition, the temperature inside the aeroponic system was contrasted with the ambient temperature and it was verified that the temperature to which the roots are exposed was in the range of 10°C – 20°C. The thermal analysis determined that a plant that presents water stress and is also exposed to high temperatures has an average leaf temperature of 3.7 to 5 °C above the optimal condition for the plant, while a plant with stress at normal temperatures was 1.3 °C higher than the plant without stress.
Why it matches plant phenotyping methodsVIS・NIR・IR画像から根・葉・植物体・果実の形態および温度形質を抽出する手法が研究の中心であり、画像解析アルゴリズムも開発しているため、植物フェノタイピング手法として適格です。
abstractOne of these is the analysis of images captured in the visible and near and far infrared spectrum which involves using non-invasive techniques in order to characterize the growth of a plant and diagnose if it presents any type of stress.
BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング環境で、熱画像・ハイパースペクトル画像による干ばつストレス、水利用、蒸散速度の推定手法を評価・モデル比較しており、フェノタイピング手法が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe article's Availability of data and materials statement deposits the datasets generated and analyzed in this study (thermal/hyperspectral phenotyping data and analyses) in three Zenodo repositories with public DOIs. These are paper-specific, publicly accessible assets. No author analysis code with an explicit publicDataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.7807989lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8164473lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8033640lines:198-347Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
LeafPhysiological trait estimationPhotosynthesis / fluorescencePlant / canopy temperature
We present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods. The FAsTeR method subjects plant leaves to a linearly increasing temperature ramp while taking rapid, nonequilibrium measurements of gas exchange variables. Two postprocessing steps are employed to correct measured assimilation rates for nonequilibrium effects and sensor calibration drift. Results obtained with the new method are compared with those from two conventional stepwise methods. Our new method accurately reproduces results obtained from conventional methods, reduces measurement time by a factor of c. 3.3 (from c. 90 to 27 min), and increases data density by a factor of c. 55 (from c. 10 to c. 550 observations). Simulation results demonstrate that increased data density substantially improves confidence in parameter estimates and drastically reduces the influence of noise. By improving measurement speed and data density, the FAsTeR method enables users to ask fundamentally new kinds of ecological and physiological questions, expediting data collection in short-field campaigns, and improving the representativeness of data across species in the literature.
Why it matches plant phenotyping methods葉レベル光合成の温度応答を高速・高密度に測定する新手法を開発し、従来法と比較検証しているため、植物フェノタイピング手法が中心です。
abstractWe present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods.
Reproduction assets foundThe paper's Data availability statement explicitly provides full data and R code (postmeasurement corrections, analyses, figures, and FAsTeR protocol) at the authors' public GitHub repository.Code · publicSTM. JCG wrote the first
draft of the manuscript, and JCG and STM revised the manu-
script.
ORCID
Josef C. Garen https://orcid.org/0000-0002-3338-6662
Sean T. Michaletz https://orcid.org/0000-0003-2158-6525
Data availability
Full data and code used for the production of figures and statis-
tics in this article are available at https://github.com/garenj/Faster-method.New Phytologist (2024) 241: 1361–1372
www.newphytologist.com
Ó 2023 The Authors
New Phytologist Ó 2023 New Phytologist Foundation
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[20/12/Open asset ↗garenj/Faster-methodpdf-raw-page:10 lines:89-144Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 7 Sept 2026
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in leaf activity stemming from differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8{degrees}C and 1.5{degrees}C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods熱画像によるキャノピー温度測定と、高解像度時系列画像・深層学習セグメンテーションによる器官レベル老化モニタリングを、stay-green機能評価のために技術的に適用・評価しており、植物表現型取得が中心である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Plant phenotyping relevance match · UnverifiedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Predicting plant development, a longstanding goal in plant physiology, involves two interwoven components: continuous growth and the progression of growth stages (phenology). Current models, like thermal time, assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses significantly affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific non-linear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.
Why it matches plant phenotyping methods冬小麦・ダイズの生育／フェノロジーをフィールドフェノタイピングで取得し、品種別の温度応答モデルを比較しているため、形質取得と計算的推定が研究の中心的要素と判断する。
abstractTo investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years.
Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.
Why it matches plant phenotyping methods熱画像・RGB指標と生物物理パラメータを機械学習で統合し、黄さび病下の収量を予測する手法の開発・検証が中心であり、単なる生物学的測定ではない。
abstractto develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters
Plant phenotyping plays a crucial part in the development of new crop genotypes. In this study, the applicability of relatively simple commercially available digital phenotyping devices was tested and improved in the context of wheat variety testing. Aerial thermography is used to evaluate the performance of genotypes by measuring canopy temperature (CT). Because lightweight thermal cameras for drones are prone to significant thermal drift effects due to a lack of a signal stabilizing cooling, we propose a new approach to analyze drone based thermal images. Through the inclusion of covariates such as trigger timing and the position of the drone relative to measured plots, temporal trends and viewing-geometry related effects could be mitigated, which improved the CT measurements. Correlations between measurements on 270 experimental wheat plots taken within 20 min were very strong (R = 0.99) and highly genotype specific with generalized heritabilities > 0.95 in many cases. In a second experiment, autonomous PhenoCams mounted on poles 12 m above the field were evaluated for their suitability to track main phenological stages and senescence as a replacement for time consuming manual field scorings. Senescence and maturity of wheat could be tracked reliably in the field for three subsequent seasons with strong correlations between field-scorings and image-based estimates (R > 0.8). For emergence and heading, achieved correlations were poor. Both experiments demonstrated how image-based phenotyping with a comparably simple setup can be used to derive high quality data relevant in the evaluation of the performance of wheat genotypes in the field.
Why it matches plant phenotyping methodsコムギ品種試験における熱画像とPhenoCamによる形質取得法を改善・検証し、測定精度と画像推定を評価しており、フェノタイピング手法が中心である。
abstractthe applicability of relatively simple commercially available digital phenotyping devices was tested and improved in the context of wheat variety testing.
Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O 2 A and O 2 B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions. Small values of spatial SIF_A averages (around 0.5 mW m -2 nm -1 sr -1 ) under strong early summer drought increased (to around 2 mW m -2 nm -1 sr -1 ) due recovery upon rain arrivals, showing high (R 2 : 0.8-0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns. While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.
Why it matches plant phenotyping methodsSIFとキャノピー温度を組み合わせ、植物群落の干ばつ・熱ストレスを推定する測定・判定手法が研究の中心であり、単なる生理測定ではない。
abstractDrought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records.
Field / plotThermalLeafWhole plant / canopy / plot / fieldVisualization / data managementGrowth / development / phenologyPlant / canopy temperature
Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 × 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters.
Why it matches plant phenotyping methods植物葉温を低コスト熱カメラで測定し、IoT-SCADAによる連続モニタリング基盤と通信・可視化手法を開発しており、植物表現型取得法が中心である。
abstractThis paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition).
Xylella fastidiosa , a gram-negative bacterium vectored to plants via feeding of infected insects, causes a number of notorious plant diseases throughout the world, such as Pierce's disease (grapes), olive quick decline syndrome, and coffee leaf scorch. Detection of Xf in infected plants can be challenging because the early foliar disease symptoms are subtle and may be attributed to multiple minor physiological stresses and/or borderline nutrient deficiencies. Furthermore, Xf may reside within an infected plant for one or more growing seasons before traditional visible diagnostic disease symptoms emerge. Any method that can identify infection during the latent period or pre-diagnostic disease progress state could substantially improve the outcome of disease control interventions. Because Xf locally and gradually impairs water movement through infected plant stems and leaves over time, infected plants may not be able to effectively dissipate heat through transpiration-assisted cooling, and this heat signature may be an important pre-diagnostic disease trait. Here, we report on the association between thermal imaging, the early stages of Xf infection, and disease development in blueberry plants, and discuss the benefits and limitations of using thermal imaging to detect bacterial leaf scorch of blueberries.
Why it matches plant phenotyping methods熱画像を用いてブルーベリーの感染初期状態と病害進展を検出する方法を評価しており、植物病態の表現型取得が研究の中心である。
abstractHere, we report on the association between thermal imaging, the early stages of Xf infection, and disease development in blueberry plants, and discuss the benefits and limitations of using thermal imaging to detect bacterial leaf scorch of blueberries.
Common beanCowpeaSorghumField / plotThermalLeafPhysiological trait estimationStomatal traitsPlant / canopy temperature
Stomatal conductance ( g ) is a critical plant biophysical variable that reflects plant regulation of CO uptake and associated water loss, yet its direct measurement is often prohibitively time-consuming. Estimating the impacts of g indirectly through leaf temperature ( T ) is a common practice, but is complicated by confounding factors such as ambient conditions, measurement aggregation scale, sample size, and measurement time. Using T measurements to instead determine parameters of a model for g that can remove these external factors can provide quasi-traits that are more reliable and heritable. Our objective was to develop an automated pipeline for g model parameterization using thermal data, which could be applied within a 3D biophysical model to predict the impacts of trait variation on canopy-level processes related to water-use efficiency. Field experiments were conducted on common bean, cowpea, and sorghum crops, involving high-resolution thermal measurements obtained from a robotic sensing platform. Subsequently, a deep learning algorithm was trained using synthetic thermography data generated using Helios 3D model simulations encompassing canopy structure, ambient conditions, and T , enabling the prediction of long-wave radiation and incident shortwave radiation for each thermal image pixel. Following this, a leaf-surface energy budget analysis was applied to the collected field thermal data to predict g parameters. Validation of these predictions was performed through comparisons with ground-truth leaf-level gas exchange data. This pipeline offers a promising pathway to predictive simulations of water status and transpiration-related traits, regardless of environmental variation, ultimately enhancing our understanding of plant responses to changing environmental conditions.
Why it matches plant phenotyping methods熱画像とロボットセンシングを用いて気孔コンダクタンス関連形質を推定する自動パイプラインを開発し、葉レベルのガス交換データで検証しており、植物表現型取得法が中心である。
abstractOur objective was to develop an automated pipeline for g model parameterization using thermal data
ArabidopsisLeafMorphology / geometry measurementGrowth / time-series analysisTrackingArchitecture / morphology / geometryGrowth / development / phenologyPlant / canopy temperature
Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality, as well as molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity and their interactions. Tracking both elongation and angle of petiole, lamina and entire leaf revealed insight into R:FR sensitivities of leaf growth and movement dynamics, and its interactions with background light intensity. We also identified spatial separation in hyponastic response regulation for the petiole and the lamina of the leaf, depending on the light conditions.
Why it matches plant phenotyping methods低コスト撮像プラットフォームとオープンソース画像解析パイプラインを開発し、葉の伸長・角度・運動を高時間分解能で定量することが中心である。
abstractHere, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution.
Why it matches plant phenotyping methods熱画像と可視-短波赤外分光から植物の病害状態を推定する手法を開発・評価し、検証データで精度を報告しており、病害フェノタイピングが研究の中心です。
abstractUsing predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (F1 score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared
Abstract Background Thermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. Results The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants’ water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. Conclusion Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング基盤で、熱画像・ハイパースペクトル画像から乾燥ストレス、蒸散速度、気孔コンダクタンスを推定する手法の評価・モデル開発が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe paper's declarations state that the datasets generated and analyzed during the study (thermal/hyperspectral imaging, environmental, and transpiration data from the maize drought phenotyping experiment) are publicly available in three Zenodo deposits with explicit DOIs. These are paper-specific, public, and directlyDataset · publicyield of photosystem II
ψ water potential
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750
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Availability of data and materials
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The datasets generated and analyzed during the current study are available in the zenodo repository
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(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. wOpen asset ↗zenodo · 10.5281/zenodo.7807989pdf-raw-page:30 lines:1-62Dataset · publicl
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750
751
Availability of data and materials
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The datasets generated and analyzed during the current study are available in the zenodo repository
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(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
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The datasets generated and analyzed during the current study are available in the zenodo repository
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(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
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https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
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This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
Why it matches plant phenotyping methods植物フェノタイピングにおける熱画像法を体系的に整理し、地上・空中プラットフォーム、画像処理アルゴリズム、成長・ストレス・病害・収量などの形質評価を扱う方法論レビューであり、方法が中心的である。
abstractHere we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses.
Abstract Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions.
Why it matches plant phenotyping methodsUAS搭載熱カメラによる葉・樹冠温度の高解像度推定と、遺伝型・構造・微気候との関係評価が研究の中心であり、植物表現型の取得手法を実質的に適用している。
abstractThermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions.
Drought adaptation for water-limited environments relies on traits that optimize plant water budgets. Limited transpiration (LT) reduces water demand under high vapor pressure deficit (VPD) (i.e., dry air condition), conserving water for efficient use during the reproductive stage. Although studies in controlled environments report genetic variation for LT, confirming its replicability in field conditions is critical for developing water-resilient crops. Here we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait. We phenotyped transpiration response to VPD (TR-VPD) via stomatal conductance (gs), canopy temperature (TC) from fixed IRT sensors (TCirt), and unoccupied aerial system thermal imagery (TCimg) in 11 genotypes. Replicability among phenomic approaches for three genotypes revealed genetic variability for TR-VPD. Genotypes BTx2752 and SC979 carry the LT trait, while genotype DKS54-00 has the non-LT trait. TC can determine differences in TR-VPD. However, the broad sense heritability (H2) and correlations suggest that canopy architecture and stand count hampers TCirt and TCimg measurement. Unexpectedly, observations of gs and VPD showed non-linear patterns for genotypes with LT and non-LT traits. Our findings provide further insights into the genetics of plant water dynamics.
Why it matches plant phenotyping methods圃場での蒸散応答という植物生理形質を、固定式赤外線センサーとUAS熱画像で測定し、手法間の再現性と代理指標としての妥当性を評価しており、フェノタイピング手法が中心である。
abstractHere we test the existence of genetic variation for LT in sorghum in field trials and whether canopy temperature (TC) is a surrogate method to discriminate this trait.
ABSTRACT The objective of this study was to analyze the feasibility of using thermal images to estimate the water status of melon plants (Cucumis melo L.) in tropical semi-arid climates. The study was conducted in a randomized block design with a split-plot arrangement. The plots comprised of soil cover (with and without mulching), and subplots were constructed using five irrigation regimes (120, 100, 80, 60, and 40% crop evapotranspiration), with five replicates. The following variables were evaluated: canopy temperature (Tcanopy), leaf water potential, air temperature (Tair), soil moisture, crop yield, and thermal index (ΔT), which is defined as the difference between Tcanopy and Tair. ΔT exhibited high correlations with crop yield and water consumption, indicating that thermography is an efficient tool for identifying the water status of melon plants, which could be employed for proper irrigation scheduling under tropical semi-arid scenarios. Moreover, thermal images identified the beneficial effects of soil cover on leaf water status and crop yield, primarily under moderate deficit irrigation. These results demonstrate that mulching is essential for increasing melon yield and water productivity in tropical regions.
Why it matches plant phenotyping methodsメロンの水分状態という植物生理形質を熱画像から推定する実現可能性を評価し、熱指標と収量・水消費との相関で手法を検証しているため、熱画像法が中心的である。
abstractThe objective of this study was to analyze the feasibility of using thermal images to estimate the water status of melon plants (Cucumis melo L.) in tropical semi-arid climates.
This work is mostly devoted to the search for effective solutions to the problem of early diagnosis of plant stress (given an example of wheat and its drought stress), which would be based on explainable artificial intelligence (XAI). The main idea is to combine the benefits of two of the most popular agricultural data sources, hyperspectral images (HSI) and thermal infrared images (TIR), in a single XAI model. Our own dataset of a 25-day experiment was used, which was created via both (1) an HSI camera Specim IQ (400-1000 nm, 204, 512 × 512) and (2) a TIR camera Testo 885-2 (320 × 240, res. 0.1 °C). The HSI were a source of the k -dimensional high-level features of plants ( k ≤ K, where K is the number of HSI channels) for the learning process. Such combination was implemented as a single-layer perceptron (SLP) regressor, which is the main feature of the XAI model and receives as input an HSI pixel-signature belonging to the plant mask, which then automatically through the mask receives a mark from the TIR. The correlation of HSI channels with the TIR image on the plant's mask on the days of the experiment was studied. It was established that HSI channel 143 (820 nm) was the most correlated with TIR. The problem of training the HSI signatures of plants with their corresponding temperature value via the XAI model was solved. The RMSE of plant temperature prediction is 0.2-0.3 °C, which is acceptable for early diagnostics. Each HSI pixel was represented in training by a number ( k ) of channels ( k ≤ K = 204 in our case). The number of channels used for training was minimized by a factor of 25-30, from 204 to eight or seven, while maintaining the RMSE value. The model is computationally efficient in training; the average training time was much less than one minute (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). This XAI model can be considered a research-aimed model (R-XAI), which allows the transfer of knowledge about plants from the TIR domain to the HSI domain, with their contrasting onto only a few from hundreds of HSI channels.
Why it matches plant phenotyping methodsHSIとTIR画像から植物の温度・ストレス状態を推定するXAI手法を開発し、予測精度とチャネル削減を評価しており、表現型取得・抽出手法が中心である。
abstractThe main idea is to combine the benefits of two of the most popular agricultural data sources, hyperspectral images (HSI) and thermal infrared images (TIR), in a single XAI model.
Knowledge of the water status in commercial vineyards is of great importance when defining the production objectives and the composition of the grape must. Determining the appropriate irrigation doses allows for adjusting the balance between vigour and productive capacity of the vineyard. However, to accurately know the hydration status of the vines, it is necessary to use equipment such as pressure chambers that are hardly replicable. Much effort has been invested in finding a more straightforward simpler methodology that allows knowing the hydration of plants. In this respect, remote sensing technology is presented as an appropriate tool to obtain information from large areas quickly and efficiently. This work aimed to evaluate the accuracy of water stress detection based on thermal sensors onboard UAVs.The study was carried out in the Merlot vineyard located in Toledo-Spain; arranged on a trellis with a 2.60 x 1.10 m planting frame and established in 2002. High-resolution thermal images were obtained on different dates during the 2021 and 2022 irrigation campaign and at two intervals of the day (9:00 and 12:00 solar hours). Stem water potential (Ψm) and chlorophyll were measured at the same time.The results indicate that there are statistically significant differences between the different irrigation treatments. These differences were mainly observed in the water-steam potential measurements made in the morning.ReferencesAcevedo-Opazo, C., Tisseyre, B., Guillaume, S., & Ojeda, H. (2008). The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precision Agriculture, 9(5), 285–302. https://doi.org/10.1007/s11119-008-9073-1.Jackson, R. D. (1982). Canopy Temperature and Crop Water Stress. 1, 43–85. https://doi.org/10.1016/b978-0-12-024301-3.50009-5.Poblete-Echeverría, C., Sepulveda-Reyes, D., Ortega-Farias, S., Zuñiga, M., & Fuentes, S. (2016). Plant water stress detection based on aerial and terrestrial infrared thermography: A study case from vineyard and olive orchard. Acta Horticulturae, 1112, 141–146. https://doi.org/10.17660/ActaHortic.2016.1112.20. Acknowledgements:The authors want to thank Bodegas y Viñas Casa del Valle for allowing us to work in their vineyards and the company UTW for supply the drone images. Financial support provided by Comunidad de Madrid through calls for grants for the completion of Industrial Doctorates IND2020/AMB-17341 is greatly appreciated.
Why it matches plant phenotyping methodsUAV搭載熱センサーによるブドウ樹の水ストレス検出精度を評価しており、植物の生理状態を推定するセンシング手法の検証が中心である。
abstractThis work aimed to evaluate the accuracy of water stress detection based on thermal sensors onboard UAVs.
We deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population. Sensors mounted on an unmanned aerial vehicle (UAV) were used to derive various vegetative indices as well as canopy temperatures. A combination of aerial imaging and manual scoring showed that CSSL 100, CSSL 84, CSSL 111, and CSSL 15 had remarkably low tomato spotted wilt virus (TSWV) incidence, a devastating disease in South Georgia, USA. The four lines also performed well under leaf spot pressure. The vegetative indices showed strong correlations of up to 0.94 with visual disease scores, indicating that aerial phenotyping is a reliable way of selecting under disease pressure. Since the yield components of peanut are below the soil surface, we deployed ground penetrating radar (GPR) technology to detect pods non-destructively. Moderate correlations of up to 0.5 between pod weight and data acquired from GPR signals were observed. Both the manually acquired pod data and GPR variables highlighted the three lines, CSSL 84, CSSL 100, and CSSL 111, as the best-performing lines, with pod weights comparable to the cultivated check Tifguard. Through the combined application of manual and HTP techniques, this study reinforces the premise that chromosome segments from peanut wild relatives may be a potential source of valuable agronomic traits.
Why it matches plant phenotyping methodsUAV画像・センサーによる地上部形質およびGPRによる地下ポッド形質の取得と、目視評価との相関検証が研究の中心であるため、実質的な植物フェノタイピング手法の適用・検証に該当する。
abstractWe deployed field-based high-throughput phenotyping (HTP) techniques to acquire trait data for a subset of a peanut chromosome segment substitution line (CSSL) population.
Frost damage to winter wheat during stem elongation frequently occurred in the Huang-Huai plain of China, leading to considerable yield losses. Minimum Stevenson screen temperature (ST min ) and minimum grass temperature (GT min ) have long been used to quantify frost damage. Although GT min has higher accuracy than ST min , it is limited in application due to the lack of data. Therefore, this study aimed to select appropriate environmental variables to estimate GT min , as well as to quantify the frost damage. Shangqiu, a frost-prone winter wheat area in the central Huang-Hui plain, was selected as the study area. From the descriptive statistics of ST, air relative humidity (RH), wind speed (WS), cloud fraction (CF), and volumetric soil water content (VWC) during temperature decreasing and increasing, seven variables significantly correlated with GT min were selected, including ST min , maximum reduction of ST (RST), maximum increase of ST (IST), minimum RH during temperature increasing (RH min ), WS at ST min occurrence (WS), minimum VWC during temperature decreasing (VWC min ), and nightly CF. Multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and K-nearest neighbor (KNN) were adopted for estimating GT min based on the various combinations of the variables. Results showed the more variables, the higher the accuracy for the MLR and SVR. However, this pattern was not always true for the KNN and RF. The KNN based on ST min , RST, IST, RH min , and WS achieved the highest accuracy, with R 2 of 0.9992, RMSE of 0.14 ℃, and MAE of 0.076 ℃. The overall classification accuracy for frost damage identified by the estimated GT min reached 97.1% during stem elongation of winter wheat from 2017 to 2021. The integrated frost stress (IFS) index calculated by the estimated and measured GT min maintained high linear fitting accuracy. The KNN with fewer variables demonstrated good applicability at the regional scale.
Why it matches plant phenotyping methods冬小麦の霜害状態を推定するためのGTmin推定モデルを開発・比較し、精度検証と地域適用性評価を行っており、植物状態の取得・推定法が中心である。
abstractMultiple linear regression (MLR), support vector regression (SVR), random forest (RF), and K-nearest neighbor (KNN) were adopted for estimating GT min based on the various combinations of the variables.
Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gₛ) of a large number of individual plants. Several methods to measure E and gₛ exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min⁻¹) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gₛ measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gₛ. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.
Why it matches plant phenotyping methods植物の蒸散速度と気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換システムとの相関で検証しているため、方法が中心的である。
abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment
Future crop varieties must be higher yielding, stress resilient and climate agile to feed a larger population, and overcome the effects of climate change. This will only be achieved by a fusion of plant breeding with multiple “omic” sciences. Field-based, proximal phenomics assesses plant growth and responses to stress and agronomic treatments, in a given environment, over time and requires instruments capable of capturing data, quickly and reliably. We designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution. The platform consisted of a telescoping, square carbon fiber unipod, which allowed for data collection from many heights. A folding arm held the sensor head at the nadir position over the plot, and an accelerometer in the arm ensured the sensor head was level at the time of data acquisition. A computer mounted on the unipod ran custom software for data collection. RGB images were taken with an 18 MP, WiFi controlled camera, infrared thermography data was captured with a 0.3 MP infrared camera, and canopy height measured with a 0.3 MP stereo depth camera. Incoming light and air temperature were logged with every image. New operators were quickly trained to gather reliable and repeatable data and an experienced operator could image up to 300 plots per hour. The PlotCam platform was not limited by field design or topography. Multiple identical PlotCams permitted the study of larger populations generating phenomic information useful in variety improvement. We present examples of data collected with the PlotCam over field soybean experiments to show the effectiveness of the platform.
Why it matches plant phenotyping methods作物の形質取得を目的とした携帯型近接フェノミクス基盤を設計・提示しており、複数センサー、データ収集、再現性、処理速度を含むプラットフォーム自体が研究の中心です。
abstractWe designed the PlotCam following the concepts of cost effective phenomics, being low-cost, light-weight (6.8 kg in total) and portable with rapid and repeatable data collection at high spatial resolution.
Field / plotThermalFruitWhole plant / canopy / plot / fieldPhysiological trait estimationPlant / canopy temperatureWater status / transpiration
Water scarcity in arid and semi-arid areas has led to the development of regulated deficit irrigation (RDI) strategies on most species of fruit trees in order to improve water productivity. For a successful implementation, these strategies require continuous feedback of the soil and crop water status. This feedback is provided by physical indicators from the soil–plant–atmosphere continuum, as is the case of the crop canopy temperature, which can be used for the indirect estimation of crop water stress. Infrared Radiometers (IRs) are considered as the reference tool for temperature-based water status monitoring in crops. Alternatively, in this paper, we assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose. The thermal sensor was tested in field conditions by performing continuous measurements on pomegranate trees (Punica granatum L. ‘Wonderful’) and was compared with a commercial IR. A strong correlation (R2 = 0.976) between the two sensors was obtained, demonstrating the suitability of the experimental thermal sensor to monitor the crop canopy temperature for irrigation management.
Why it matches plant phenotyping methods低コスト熱画像センサーによる植物群落温度の取得法を開発・評価し、市販赤外線放射計との比較検証を行っているため、植物フェノタイピング手法が中心である。
abstractwe assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose.
Associated with climate change, the frequency, duration, and intensity of heatwaves are increasing in most of the key wine regions worldwide. Depending on timing, intensity, and duration, heatwaves can impact grapevine yield and berry composition, with implications for wine quality. To overcome these negative effects, two types of mitigation practices have been proposed (i) to enhance transpiration and (ii) to reduce the radiation load on the canopy. Here we use a biophysical model to quantify the impact of these practices on canopy gas exchange, vine water status, and leaf temperature (Tₗ). Model validation was performed in a commercial vineyard. Modelled Tₗ from 14 to 43 °C, and transpiration, from 0.1 to 5.4 mm d⁻¹, aligned around the identity line with measurements in field-grown vines; the RMSD was 2.6 ºC for temperature and 0.96 mm day⁻¹ for transpiration. Trellis system and row orientation modulate Tₗ. A sprawling single wire trellis with an EW orientation maintained the canopy around 1ºC cooler than a Vertical Shoot Positioned canopy with NS for the same range of total fraction of soil available water (TFAW). Although irrigation before a heatwave is a recommended practice, maximum transpiration can be sustained even when TFAW is reduced, limiting the heat dampening effect of irrigation. Alternatively, canopy cooling can be achieved through Kaolin application, the installation of shade cloth placement, or canopy trimming. Shade cloth produced a greater cooling than Kaolin in all the simulated scenarios; however, Tₗ differences between them varied. Trimming reduced Tₗ from 2 ºC to almost 8 ºC compared to its non-trimmed counterpart. Our analysis presents new insights to design heat wave mitigation strategies and supports agronomically meaningful definitions of heat waves that include not only temperature, but also wind, VPD, and radiation load as these factors influence crop physiology under heat stress.
Why it matches plant phenotyping methodsブドウの葉温・蒸散・水状態を推定する生物物理モデルを開発的に適用し、圃場測定で検証しているため、植物表現型の取得・推定が中心的です。
abstractHere we use a biophysical model to quantify the impact of these practices on canopy gas exchange, vine water status, and leaf temperature (Tₗ).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min−1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30–50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.
Why it matches plant phenotyping methods植物の蒸散速度・気孔コンダクタンスを推定する熱画像ベースの表現型計測法を開発し、ガス交換測定と比較検証しているため、方法が研究の中心である。
abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment
Crop yield data is critical for precision agriculture, breeding programs, and other activities, but collecting this data at fine scales is labor-intensive. Unmanned aerial systems (UAS) allow collection of imagery with unprecedented temporal, spatial, and spectral resolutions and could be better leveraged to estimate or predict yield while limiting labor requirements. Therefore, the objectives of this study were to develop a relatively simple pixel-based multispectral image classification technique for cotton (Gossypium hirsutum L.) yield estimation, termed “Boll Area Index” or BAI, which is collected after defoliation, and to identify in-season co-predictors derived from multispectral and thermal imagery to improve estimate accuracy. A field study was conducted over four growing seasons (2017–2020) at College Station, TX. The experimental treatments included three irrigation rates (0%, 40%, and 80% ETc replacement) and eight commercial cotton cultivars each year. Multispectral and thermal infrared imagery were captured biweekly. In addition to BAI, three vegetation indices (Normalized Difference Vegetation Index or NDVI, Normalized Difference Red Edge or NDRE, and Optimized Soil Adjusted Vegetation Index or OSAVI) and canopy temperature were derived from orthomosaics and analyzed. There were positive linear relationships between BAI and seed cotton yield each year (R² = 0.61–0.79). Multiple linear regression including BAI, vegetation indices, and/or canopy temperature from two flight dates produced better yield estimates (R² = 0.79–0.89) than BAI alone. Cameras or payloads with both optical and thermal sensors are ideal for strictly in-season yield estimation endeavors, but thermal was not necessary when BAI was included in the models because canopy temperature provided minimal improvement as a third predictor. Multiple regressions involving NDVI and BAI already had quite strong relationships with yield (R² = 0.7–0.87) without including canopy temperature. Cross validation of multiple linear regression models derived from BAI and NDVI, using data from two years to predict yield in a third, had R² values that varied from 0.51 to 0.88 and RMSE varied from 273 to 508 kg ha⁻¹. This is a level of error that may be acceptable for some purposes, such as screening lines in early stages of cotton breeding selection, but may be unacceptable for screening of advanced lines when greater accuracy is crucial. Overall, the results indicate that derivatives from just two or three UAS flights presents a detailed dataset for cotton yield prediction, while limiting labor and required computational resources.
Why it matches plant phenotyping methods綿花の収量形質を推定するため、BAIという画像分類手法とUASマルチスペクトル・熱画像由来の予測ワークフローを開発し、複数年データで検証している。植物フェノタイピング手法が研究の中心である。
abstractthe objectives of this study were to develop a relatively simple pixel-based multispectral image classification technique for cotton (Gossypium hirsutum L.) yield estimation, termed “Boll Area Index” or BAI
Maize production in Thailand is increasingly suffering from drought periods along the cropping season. This creates the need for rapid and accurate methods to detect crop water stress to prevent yield loss. The study was, therefore, conducted to improve the efficacy of thermal imaging for assessing maize water stress and yield prediction. The experiment was carried out under controlled and field conditions in Phitsanulok, Thailand. Five treatments were applied, including (T1) fully irrigated treatment with 100% of crop water requirement (CWR) as control; (T2) early stress with 50% of CWR from 20 days after sowing (DAS) until anthesis and subsequent rewatering; (T3) sustained deficit at 50% of CWR from 20 DAS until harvest; (T4) late stress with 100% of CWR until anthesis and 50% of CWR after anthesis until harvest; (T5) late stress with 100% of CWR until anthesis and no irrigation after anthesis. Canopy temperature (FLIR), crop growth and soil moisture were measured at 5‐day‐intervals. Under controlled conditions, early water stress significantly reduced maize growth and yield. Water deficit after anthesis had no significant effect. A new combination of wet/dry sponge type reference surfaces was used for the determination of the Crop Water Stress Index (CWSI). There was a strong relationship between CWSI and stomatal conductance (R² = 0.90), with a CWSI of 0.35 being correlated to a 64%‐yield loss. Assessing CWSI at 55 DAS, that is, at tasseling, under greenhouse conditions corresponded best to the final maize yield. This linear regression model validated well in both maize lowland (R² = 0.94) and maize upland fields (R² = 0.97) under the prevailing variety, soil and climate conditions. The results demonstrate that, using improved standardized references and data acquisition protocols, thermal imaging CWSI monitoring according to critical phenological stages enables yield prediction under drought stress.
Why it matches plant phenotyping methods熱画像によるCWSI測定について、標準化リファレンスとデータ取得プロトコルを改良し、水ストレスおよび収量予測への妥当性を検証しており、植物表現型取得法が中心である。
abstractThe study was, therefore, conducted to improve the efficacy of thermal imaging for assessing maize water stress and yield prediction.
As the largest component of crops, water has an important impact on the growth and development of crops. Timely, rapid, continuous, and non-destructive detection of crop water stress status is crucial for crop water-saving irrigation, production, and breeding. Indices based on leaf or canopy temperature acquired by thermal imaging are widely used for crop water stress diagnosis. However, most studies fail to achieve high-throughput, continuous water stress detection and mostly focus on two-dimension measurements. This study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data. A method is developed to obtain 3D plant fusion point cloud with depth, temperature, and RGB color information using the acquired thermal and binocular RGB data. Firstly, the developed system is used to automatically collect the data of the potato plants in the scene. Secondly, the collected data was processed, and the green canopy was extracted from the color image, which is convenient for the speeded-up robust features algorithm to detect more effective matching features. Photogrammetry combined with structural similarity index was applied to calculate the optimal homography transform matrix between thermal and color images and used for image registration. Thirdly, based on the registration of the two images, 3D reconstruction was carried out using binocular stereo vision technology to generate the original 3D point cloud with temperature information. The original 3D point cloud data were further processed through canopy extraction, denoising, and k-means based temperature clustering steps to optimize the data. Finally, the crop water stress index (CWSI) of each point and average CWSI in the canopy were calculated, and its daily variation and influencing factors were analyzed in combination with environmental parameters. The developed system and the proposed method can effectively detect the water stress status of potato plants in 3D, which can provide support for analyzing the differences in the three-dimensional distribution and spatial and temporal variation patterns of CWSI in potato.
Why it matches plant phenotyping methods熱画像と双眼ステレオビジョンを統合した3D植物表現型取得システムと、温度付き点群からCWSIを算出する手法が研究の中心であるため。
abstractThis study developed a low-cost three-dimension (3D) motion robotic system, which is equipped with a designed 3D imaging system to automatically collect potato plant data, including thermal and binocular RGB data.
Precision Irrigation (PI) is a promising technique for monitoring and controlling water use that allows for meeting crop water requirements based on site-specific data. However, implementing the PI needs precise data on water evapotranspiration. The detection and monitoring of crop water stress can be achieved by several methods, one of the most interesting being the use of infra-red (IR) thermometry combined with the estimate of the Crop Water Stress Index (CWSI). However, conventional IR equipment is expensive, so the objective of this paper is to present the development of a new low-cost water stress detection system using TL indices obtained by crossing the responses of infrared sensors with image processing. The results demonstrated that it is possible to use low-cost IR sensors with a directional Field of Vision (FoV) to measure plant temperature, generate thermal maps, and identify water stress conditions. The Leaf Temperature Maps, generated by the IR sensor readings of the plant segmentation in the RGB image, were validated by thermal images. Furthermore, the estimated CWSI is consistent with the literature results.
Why it matches plant phenotyping methods低コスト赤外線センサーと画像処理による植物の水ストレス検出・葉温マップ生成法を開発し、熱画像で検証しているため、植物フェノタイピング手法が中心です。
abstractthe objective of this paper is to present the development of a new low-cost water stress detection system using TL indices obtained by crossing the responses of infrared sensors with image processing.
In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes ( Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control ( C ), ii) water stress ( WS ), and iii) combined water and heat shock ( WS+T ). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions ( WS and WS+T ); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.
Why it matches plant phenotyping methods葉レベルのスペクトル反射変化を用いて遺伝子型のストレス応答を識別する測定・スクリーニング手法が研究の中心であり、育種向けの再利用可能な表現型取得法として提示されている。
abstractdaily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes
The work is devoted to the search for effective solutions to the applied problem of early diagnostics of plant stress in the conditions of smart farming and based on modern explicable artificial intelligence (XAI). The study mostly oriented on the theory and practice of XAI, focused on the use of hyperspectral imagery (HSI) and Thermal Infra-Red (TIR) sensor data at the input of a neural network. The first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'. In the middle is SLP-regressor which solves the universal problem of training HSI pixels to temperatures of plants, needed for early plant stress diagnostic. The result can be considered as prototype of a special XAI explanator which is assigned to transform explanator specialized on area 1 onto explanator specialized on area 2. Using this HSI-TIR explanator we ensured the follows: extend HSI data by TIR attribute; providing TIR data for early diagnostic of plant stress; reducing dimensionality HSI needed for TIR training 25 times (from 204 to 8) preserving the same accuracy of temperature prediction (RMSE=0.2-0.3C). This reducing was achieved without using PCA methods. The constructed model is computationally efficient in training: the average training time is significantly less then 1 min (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). One of the 8 channels, 820 nm, is the leader in correlation with TIR, what allows building local linear temperature prediction functions.
Why it matches plant phenotyping methodsHSI・TIRデータから植物温度とストレスを推定する説明可能なニューラルネットワークを開発し、次元削減と予測精度を評価しており、植物表現型取得手法が中心である。
abstractThe first our goal is to build an XAI neural network, explainable due to its structure, the input of which is a datascientist oriented HSI 'explanator', and the output is a biologist oriented TIR 'explanator'.
Irrigation water management starts with quantifying irrigation prescriptions based on crop water requirements at a spatial scale. The most accepted and effective method for estimating this water need is to use the canopy temperature of the plants. The use of small unmanned aerial systems (sUAS) with a thermal infrared camera has long been established as an effective method of measuring plant canopy temperatures at a spatial scale. However, concerns still exist about the accuracy of canopy temperatures collected by these systems and how imagery collection altitude or camera viewing angle affects the temperature estimation. To address these concerns, this study was designed to evaluate the effects of flying altitude of airborne thermal infrared (TIR) imagery and camera view angle on corn-canopy temperature sensing accuracy and image quality. Three different thermal cameras with focal lengths of 9mm, 13mm, and 19mm were used, and each camera was flown at an altitude of 30m, 50m, and 70m. Thermal cameras were mounted on an sUAS and flown over a 6000 m² cornfield. The sUAS was flown at a speed of 3m/s on autopilot mode guided by pre-planned missions. The thermal camera was triggered once per second, and imagery was geotagged using an external GPS unit. Images obtained using different combinations of focal lengths and flight altitudes were processed and converted into orthomosaics. Calibration was conducted using ground-based temperature reference panels and temperature maps were created from the orthomosaics. The orthomosaics were examined for the accuracy of the corn-canopy temperature sensing, ability to differentiate between hot and cold surfaces, ease of image stitching/developing orthomosaics, geometric accuracy, image quality, and spatial resolution. The results indicated that with the combination of appropriate camera focal length, altitude, and image calibration techniques, a canopy temperature map of crops with a temperature error of less than 2° C from the actual canopy temperature can be produced. A narrow-angle thermal camera flying at low altitudes (<50m) was found to be the least suitable combination for corn canopy temperature sensing. The most appropriate combination for temperature estimation of corn canopies was with a 13 mm focal length camera flying at an altitude of 50m above ground level.
Why it matches plant phenotyping methodsトウモロコシの群落温度という植物生理形質を対象に、熱赤外 sUAS 測定の焦点距離・飛行高度・校正条件による精度を検証しており、フェノタイピング手法が中心である。
abstractthis study was designed to evaluate the effects of flying altitude of airborne thermal infrared (TIR) imagery and camera view angle on corn-canopy temperature sensing accuracy and image quality
This study integrated field‐level sensor data into the FAO‐56 Penman-Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman-Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R² = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman-Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field.
Why it matches plant phenotyping methods圃場レベルの作物センシング（分光反射、群落温度、草丈）を用いて作物蒸発散量を推定し、土壌水分収支法と比較検証している。単なる生物学的実験のルーチン測定ではなく、作物の水利用状態を取得・推定するセンシング手法が中心である。
abstractDaily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height).
Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions.
Why it matches plant phenotyping methods植物ストレス状態を、リモートセンシングによる光合成推定、葉温、微気候データと機械学習で検出する方法を開発・評価しており、表現型取得・推定が中心である。
abstractIn this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested.
Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ET c ]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (T c ), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest T c (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ET c , and highest T c (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ET c . The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, T c , and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress.
Why it matches plant phenotyping methods熱画像・RGB画像を用いてコムギの水ストレス状態を推定・分類するカスタム撮像システムと深層学習手法が研究の中心であり、植物状態の取得・抽出方法を実質的に評価している。
abstractnon-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs
Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.
Why it matches plant phenotyping methods冬小麦の光合成状態をSIFで推定する手法について、GPP追跡性能と温度応答を検証しており、センサーによる植物生理形質推定が研究の中心です。
abstractSolar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions.
In precision agriculture, the Normalized Difference Vegetative Index (NDVI) considers the spectral characteristics of healthy green vegetation. This index is an effective way of detecting the green state of plants. This is why we choose to use NDVI as a reference index to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines. These machines were used to estimate different physiological, biochemical, and agronomic parameters as indicators of stress (GA, GGA, SPAD, and canopy temperature). Leaf level pigments were measured using a handheld sensor (SPAD). Canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the NDVI was measured with a portable spectroradiometer (Greenseeker). The plant level water stress was assessed indirectly by plant temperature using an infrared thermometer. We conclude that the grafted plants were less stressed and more protected against nematode attack. The comparison of NDVI index predicted by AI models showed that artificial neural network MLP demonstrated the best prediction performance than the linear regression method. However, their R-squared decreased from 0.820 to 0.772, and NRMSE increased from 12.3% to 12.4%, respectively.
Why it matches plant phenotyping methods近接リモートセンシングとAIモデルにより、NDVI、バイオマス、樹冠温度などの植物ストレス関連形質を取得・推定し、予測性能も比較しているため、フェノタイピング手法の適用・技術評価が中心です。
abstractThese machines were used to estimate different physiological, biochemical, and agronomic parameters as indicators of stress (GA, GGA, SPAD, and canopy temperature).
The use of temperature as an indicator of water stress is gaining attention in agriculture. However, the need for reference temperature measurements in the field (Twₑₜ and Tdᵣy) for normalization purposes (calculation of the crop water stress index - CWSI) inhibits the practical implementation of this technique. Therefore, in this study, two new models namely the heat transfer (HT) model and the empirical (EMP) model, are presented and compared with the standard method of physical reference temperature measurements and leaf energy balance calculation as an exploratory case study on grapevines. The HT model is a novel method, based on physical heat transfer principles and uses only input data obtained from a conventional weather station to determine reference temperatures. The EMP model is a simple method based on wet- and dry-bulb temperatures, calculated from ambient temperature and relative humidity measurements. To develop and evaluate the new models, physical measurements of the reference temperatures were taken in a commercial vineyard cv. Cabernet Sauvignon under different levels of water stress at two times during the day on seven days over the growing season. These physical measurements were used to optimize unknown parameters in the new methods by using the particle swarm optimization procedure. Input data for the model was collected by a conventional weather station located nearby the experimental vineyard block. In the validation process, it was found that the HT model can accurately predict the reference temperatures to within 0.5 °C and 1.0 °C for Twₑₜ and Tdᵣy, respectively, reacting to environmental conditions as expected. The EMP model, requiring even less meteorological information, can accurately predict the reference temperatures to within 0.8 °C and 1.4 °C for Twₑₜ and Tdᵣy, respectively. These proposed methods can provide reference temperatures that do not require physical measurements which can make the use of CWSI more practical and easier to implement for determining plant water stress in vineyards.
Why it matches plant phenotyping methodsブドウの水ストレス表現型を推定するCWSI用の基準温度モデルを開発し、物理測定との比較で検証しており、フェノタイピング手法が研究の中心である。
abstracttwo new models namely the heat transfer (HT) model and the empirical (EMP) model, are presented and compared with the standard method of physical reference temperature measurements
About a decade ago, active optical crop canopy sensors are being used to manage in-season variable nitrogen (N) fertilization in cornfields to match the plant demand that occurs mid season, increasing the efficiency compared to broadcast N applications. There were also initiatives of using ultrasonic sensors to measure plant height on-the-go for N application and crop water demand estimation, but no studies have integrated the optical, ultrasonic and canopy temperature for crop water stress assessment. The objective of this chapter is to evaluate the crop water status using infrared thermometry integrated with optical and ultrasonic sensors. Specifics objectives are: (i) evaluate the corn canopy temperature under different previous crop, N rates and irrigation levels; (ii) test a procedure for water stress assessment in commercial cornfields using the integration of sensors, (iii) correlate plant based sensor measurements (N status, plant height and canopy temperature) with grain yield, soil attributes and detailed topographical features, and (iv) study the spatial dependence of canopy temperature. This study was conducted in one small plot study area and on three producer’s fields in 2010. The small plot experiment consisted of two irrigation levels (70 and 100% of evapotranspiration – ET), two previous crop schemes (corn after corn – CC and corn after soybeans – CS), and four N rates (0, 75, 150, 225 kg N ha-1). Canopy temperature, optical reflectance and plant height was measured from R2 until R6 in the small plots. At the producer’s fields, three long strips across center pivots were used to have a non-limited N and water crop and then continuous georeferenced sensors measurements were taken during side-dress (V11 growth stage) in about 10 hectares in each field. In the small plot study the crop canopy temperature was influenced by the irrigation levels and N rates. The procedure proposed could be used to identify zones in the producer’s field where water stress can be a yield limiting factor other than N derived. Inside the zones considered that water stress played a major whole, there were low correlations between plant height, plant N status and canopy temperature, indicating that the canopy temperature had more influence from water stress than vegetation cover. Concave and lower elevation areas had higher yields compared to convex and high elevation, showing that the detailed elevation mapping can be beneficial to delineate stables zones that possibly could be used in variable irrigation systems. The spatial dependence of canopy temperature was over 65 meters across producers’ sites, showing that the commercial high clearance applicator’s swath width was adequate to obtain accurate maps. The integration of plant N status, plant height and canopy temperature was beneficial to detect water stressed zones in the field. Opportunities can be foresee also for on-the-go N fertilization using integration of these sensors because is likely that water stress can be confounded with different N supply during the growing season and in different zones in the field.
Why it matches plant phenotyping methods光学・超音波・赤外線温度センサーを統合し、トウモロコシの草冠温度、草丈、N状態から水ストレスを評価する手順と空間的性能を検討しており、植物表現型取得法が中心である。
abstractThe objective of this chapter is to evaluate the crop water status using infrared thermometry integrated with optical and ultrasonic sensors.
Laboratory / benchtopFruitLeafObject detectionPhysiological trait estimationPlant / canopy temperature
Abstract Flexible plant sensors play a critical role in smart agriculture due to their advantages in real‐time monitoring physiological signals of plants, and are experiencing growth in recent years. Such devices are expected to be directly placed on surfaces of plant organs for better detection. However, most existing sensors based on the planar substrate are not able to adapt to nondevelopable surfaces of plants, and are unsatisfactory in biocompatibility. Herein, considering the complexity of the plant surface, flexible temperature sensors for leaves and fruits are developed. The leaf temperature sensor is based on the porous substrate, which is designed to minimize its effect on plant respiration, and is demonstrated to measure temperature changes accurately after long‐time integration with a leaf. By mechanical design, the fruit temperature sensor realizes the transformation from a planar shape to a tridimensional shape, and is demonstrated to work on a variety of complex curved surfaces without loss of performance. The proposed shape‐morphing structure expands the capabilities of current planar electronics, and links thin‐film technology to spatial deformable devices. Results of the in vitro experiments show that these two proposed sensors hold promise to monitor microenvironment temperature in plant biology.
Why it matches plant phenotyping methods植物器官の温度を長時間・複雑曲面上で測定する柔軟センサーを開発し、性能実証しており、植物表現型取得法が研究の中心である。
abstractflexible temperature sensors for leaves and fruits are developed
Aerial / UAVField / plotFruitCountingPhysiological trait estimationSegmentationPlant / canopy temperature
Computer vision and AI for smart agriculture have exciting potential in optimizing crop yield while reducing resource use for better environmental and commercial outcomes. The goal of this work is to develop state-of-the-art computer vision algorithms for image-based crop evaluation and weather-related risk assessment to support real-time decision-making for growers. We develop a cranberry bog monitoring system that maps cranberry density and also predicts short-term cranberry internal temperatures. We have two important algorithm contributions. First, we develop a method for cranberry instance segmentation that provides the number of sun-exposed cranberries (not covered by the crop canopy) that are at risk of overheating. The algorithm is based on a novel weakly supervised framework using inexpensive point-click annotations, avoiding time-consuming annotations of fully-supervised methods. The second algorithmic contribution is an in-field joint solar irradiation and berry temperature prediction in an end-to-end differentiable network. The combined system enables over-heating risk assessment to inform irrigation decisions. To support these algorithms, we employ drone-based crop imaging and ground-based sky imaging systems to obtain a large-scale dataset at multiple time points. Through extensive experimental evaluation, we demonstrate high accuracy in cranberry segmentation, irradiance prediction and internal berry temperature prediction. This work is a pioneering step in using computer vision and machine learning for rapid, short-term decision-making that can assist growers in irrigation decisions in response to complex time-sensitive risk factors. Datasets collected over two growing seasons are made publicly available to support further research. The methods can be extended to additional crops beyond cranberries, such as grapes, olives, and grain, where irrigation management is increasingly challenging as climate changes.
Why it matches plant phenotyping methodsクランベリーの密度・個体数・果実温度を画像から推定する手法を開発し、精度評価と公開データセット提供まで行っており、植物表現型取得が中心である。
abstractWe develop a cranberry bog monitoring system that maps cranberry density and also predicts short-term cranberry internal temperatures.
The way of quantitatively expressing mesophyll conductance (gₘ) in the Farquhar-von Caemmerer-Berry (FvCB) photosynthesis model and its impacts on plant gas exchange estimations have not been well explored, primarily due to huge uncertainties in gₘ parameterization. Here, a peaked Arrhenius function to depict gₘ temperature response was introduced into the FvCB model and parameterized through evaluating four different gₘ estimation methods in 19 C₃ species at 31 experimental treatments. Results indicated that the FvCB model without explicitly considering gₘ cannot perform well in eight species/treatments, while the model that considers gₘ estimated by the chlorophyll fluorescence–gas exchange method and biochemical parameters estimated by the Bayesian retrieval algorithm was superior. Overall modeling accuracy was not further ameliorated when taking anatomy-based gₘ into consideration. The increasing Arrhenius function without considering the suboptimal stage of gₘ temperature response caused significant overestimations in photosynthesis under high leaf temperatures by 2–3 folds. The gₘ explicit expression had equally important effects on photosynthesis and transpiration estimations, which disagreed with “the asymmetric effects on photosynthesis and transpiration estimations” hypothesis proposed by Knauer et al. (2020). Literature survey plus our data indicated that observed variations of photosynthesis optimal temperature (TₒₚₜA) were primarily explained by the gₘ optimal temperature (Tₒₚₜ_gₘ) (58%) rather than biochemical limitations, which disagreed with “the JVr biochemical limitations” hypothesis proposed by Kumarathunge et al. (2019).
Why it matches plant phenotyping methods複数の葉肉コンダクタンス推定法を比較評価し、FvCBモデルによる光合成・蒸散推定への影響とモデリング精度を検証しており、植物生理形質の取得・推定方法が中心である。
abstractparameterized through evaluating four different gₘ estimation methods in 19 C₃ species at 31 experimental treatments
Field / plotThermalLeafWhole plant / canopy / plot / fieldSegmentationStress / disease detectionPlant / canopy temperatureWater status / transpiration
So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.
Why it matches plant phenotyping methods低コスト赤外線センサーと画像処理を組み合わせ、植物の水ストレスをCWSIとして検出する測定システムを開発・評価しており、植物状態の取得方法が研究の中心です。
abstractThe responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI.
With the development of water detection instruments, it is feasible to detect the stem liquid water content. However, real-time and non-destructive monitoring of liquid water and ice content of plant stems in winter remains challenging. Here, we developed a living wood freeze–thaw detection (LWFTD) sensor to detect the liquid water and ice content of plant stems in situ, in real time, and micro-destructively. First, based on the latent heat effect and using a retractable ring-type shrapnel probe, we monitored the freeze–thaw front of the stem water in real time and micro-destructively. Second, using calibration data and infrared detection data, the reliability of the LWFTD sensor and the feasibility of stem freeze–thaw detection based on the latent heat effect were proven. Finally, by simulating a freeze–thaw cycle and analyzing the changes in the stem liquid water content and stem temperature, an ice content model was constructed to calculate the ice content and freeze–thaw fronts. In field experiments, we recorded stem water content and freeze–thaw data of Pachira glabra, Populus tomentosa, and Lagerstroemia indica during an overwintering period. The results showed that the LWFTD sensor can effectively detect the changes in water-related physiological parameters during plant freeze–thawing in real time and that the ice content in the stem volume exhibits diurnal changes, with a single-peak, single-valley wave pattern. Our study provides a reference for evaluating the effects of freeze–thaw on plant metabolism and vitality. Furthermore, this study provides an advanced technical tool for in situ, real-time, and micro-destructive monitoring of the water and ice content in plant stems, which will help further our understanding of the water transport process and freeze–thaw-induced embolism of woody plants during winter.
Why it matches plant phenotyping methods植物茎内の液水・氷含量と凍結融解前線を測定するセンサーを開発し、校正・赤外検出による信頼性検証と実地適用を行っており、植物生理状態の取得手法が中心である。
abstractwe developed a living wood freeze–thaw detection (LWFTD) sensor to detect the liquid water and ice content of plant stems in situ, in real time, and micro-destructively.
Surface temperatures are mechanistically linked to vegetation biophysical and physiological processes. Although remote sensing in the thermal infrared (TIR) domain can offer novel insights into the impacts of changing surface temperatures on vegetation, the transformative potential of remote sensing for plant ecology has not yet been realized. Remotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time. Plant species, traits and structural characteristics can be evaluated with high spectral resolution TIR emissivity. Beyond canopy scales, thermal remote sensing can enhance the inferences obtained from manipulative experiments and empirical evidence, providing unique insight into shifts in species ranges and phenology with changing climate conditions. Scaling leaf traits, canopy structure and regional patterns require an integrated understanding of both process and technology. Theory linking surface temperatures to vegetation dynamics is summarized from an energy balance perspective. We outline scaling considerations including the impacts of morphology on leaf energy balance, canopy structure influences on convective heat exchange and potential confounding impacts of non‐vegetated surfaces. Synthesis. We introduce a unifying framework to link leaf to globe through thermal remote sensing. Recent and emerging advances in sensors, data availability and analytics, together with synergies between TIR remote sensing and other data sources, present a timely opportunity for ecologists to advance our understanding of plant physiology, ecology and biogeography with thermal remote sensing.
Why it matches plant phenotyping methods熱赤外リモートセンシングにより植物の温度、気孔挙動、形質、生理状態を推定する技術を葉から全球規模まで体系化した方法論レビューであり、植物表現型の取得・推定が中心である。
abstractRemotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time.
Chlorophyll fluorescenceCell / cellular structurePhysiological trait estimationPlant / canopy temperature
Abstract All aspects of plant physiology are influenced by temperature. Changes in environmental temperature alter the temperatures of plant tissues and cells, which then affect various cellular activities, such as gene expression, protein stability and enzyme activities. In turn, changes in cellular activities, which are associated with either exothermic or endothermic reactions, can change the local temperature in cells and tissues. In the past 10 years, a number of fluorescent probes that detect temperature and enable intracellular temperature imaging have been reported. Intracellular temperature imaging has revealed that there is a temperature difference >1°C inside cells and that the treatment of cells with mitochondrial uncoupler or ionomycin can cause more than a 1°C intracellular temperature increase in mammalian cultured cells. Thermogenesis mechanisms in brown adipocytes have been revealed with the aid of intracellular temperature imaging. While there have been no reports on plant intracellular temperature imaging thus far, intracellular temperature imaging is expected to provide a new way to analyze the mechanisms underlying the various activities of plant cells. In this review, I will first summarize the recent progress in the development of fluorescent thermometers and their biological applications. I will then discuss the selection of fluorescent thermometers and experimental setup for the adaptation of intracellular temperature imaging to plant cells. Finally, possible applications of intracellular temperature imaging to investigate plant cell functions will be discussed.
Why it matches plant phenotyping methods植物細胞内温度を蛍光サーモメーターで画像化する手法の選択、実験系への適応、応用を中心に扱う方法論レビューであり、植物の生理状態を測定する手法が主題である。
titleA Guide to Plant Intracellular Temperature Imaging using Fluorescent Thermometers
Abstract Canopy temperature is generally accepted as an indirect but rapid, accurate, and large-scale indicator of crop water status and is, therefore, proposed to monitor irrigation needs. Crop Water Stress Index (CWSI) is the most widely used among the existing thermal-based indicators, and its links with water stress have been demonstrated. When calculating CWSI using the empirical approach, the differential between canopy and air temperature is normalized by two thresholds, also known as baselines. The Non-water stress baseline (NWSB) in the empirical approach is calculated as the relationship between T c – T a (°C) and the vapor pressured deficit (VPD, kPa) for well-irrigated crops. The baselines display different slopes depending on the species, which have a significant impact on the computed CWSI. This study analyzed the resulting errors on CWSI due to the measurement errors of critical inputs needed for its calculation. Six crop species were selected according to their NWSB with slopes that range from − 0.5 to − 3 °C·kPa −1 and used for this analysis, assuming measurement errors ranging 0.2–1 °C for T a , 0.25–2 °C for T c , and 5–10% for relative humidity (RH). It was concluded that the effects observed on CWSI are heavily dependent on the slope of the NWSB and therefore vary across species. The calculation was very sensitive to the bias in air and canopy temperature. These errors were maximal as the slope of the NWSB was less steep. When the VPD ranged from 2 to 6.6 kPa, an error of 1 °C in measuring the air temperature affected CWSI between 28 and 83% in orange, which is the species displaying the minimum slope (− 0.5 °C kPa −1 ). On the contrary, crops with steeper baseline slopes such as squash (− 3 °C kPa −1 ) showed errors ranging between 2 and 8% for the same VPD interval. This differences among the different crops species considered in this study may be related to the contrasting coupling of the species to the atmosphere, that determines the influence of vapor pressure on the transpiration rate. This study highlights the importance of reliable climatic data and the need for accurate calibrated thermal sensors to calculate CWSI accurately.
Why it matches plant phenotyping methods作物の水分状態を表すCWSIについて、温度・湿度測定誤差の影響を解析し、熱センサーの校正精度を評価する方法検証研究である。
titleAssessing the impact of measurement errors in the calculation of CWSI for characterizing the water status of several crop species
Crop wild relatives have been used as a source of genetic diversity for over one hundred years. The wild tomato relative Solanum galapagense accession LA1141 demonstrates the ability to tolerate deficit irrigation, making it a potential resource for crop improvement. Accessing traits from LA1141 through introgression may improve the response of cultivated tomatoes grown in water-limited environments. Canopy temperature is a proxy for physiological traits which are challenging to measure efficiently and may be related to water deficit tolerance. We optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance. The objectives of this work were to improve phenotyping workflows for measuring canopy temperature, mapping quantitative trait loci (QTLs) from LA1141 that contribute to water deficit tolerance and comparing selection strategies. The phenotypic variance attributed to genetic causes for canopy temperature was higher when estimated from thermal images relative to estimates based on an infrared thermometer. Composite interval mapping using BC 2 S 3 families, genotyped with single nucleotide polymorphisms, suggested that accession LA1141 contributed alleles that lower canopy temperature and increase plant turgor under water deficit. QTLs for lower canopy temperature were mapped to chromosomes 1 and 6 and explained between 6.6 and 9.5% of the total phenotypic variance. QTLs for higher leaf turgor were detected on chromosomes 5 and 7 and explained between 6.8 and 9.1% of the variance. We advanced tolerant BC 2 S 3 families to the BC 2 S 5 generation using selection indices based on phenotypic values and genomic estimated breeding values (GEBVs). Phenotypic, genomic, and combined selection strategies demonstrated gain under selection and improved performance compared to randomly advanced BC 2 S 5 progenies. Leaf turgor, canopy temperature, stomatal conductance, and vapor pressure deficit (VPD) were evaluated and compared in BC 2 S 5 progenies grown under deficit irrigation. Progenies co-selected for phenotypic values and GEBVs wilted less, had significantly lower canopy temperature, higher stomatal conductance, and lower VPD than randomly advanced lines. The fruit size of water deficit tolerant selections was small compared to the recurrent parent. However, lines with acceptable yield, canopy width, and quality parameters were recovered. These results suggest that we can create selection indices to improve water deficit tolerance in a recurrent parent background, and additional crossing and evaluation are warranted.
Why it matches plant phenotyping methods熱画像と赤外線温度計による植物キャノピー温度測定を比較し、表現型評価ワークフローを最適化しているため、表現型取得法が育種研究の中心的技術要素となっている。
abstractWe optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance.
The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface vegetation, so remote sensing can be used to detect micro-leakage indirectly. This study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019. The correlation analysis between natural gas concentration and canopy temperature showed that the canopy temperature of vegetation increased under gas stress. A GoogLeNet model with Bilinear pooling (GLNB) was proposed for the classification of different vegetation varieties under natural gas micro-leakage stress. Further, transfer learning is used to improve the model training process and classification efficiency. The proposed methods achieved 95.33% average accuracy, 95.02% average recall and 95.52% average specificity of stress classification for four vegetation varieties. Finally, based on Grad-Cam and the quasi-circular spatial distribution rules of gas stressed areas, the range of natural gas micro-leakage stress areas under different vegetation and stress durations was detected. Taken together, this study demonstrated the potential of using thermal infrared imaging and deep learning in identifying gas-stressed vegetation, which was of great value for detecting the location of natural gas micro-leakage.
Why it matches plant phenotyping methods熱赤外画像と深層学習により、植物のストレス状態および樹冠温度を推定・分類する手法が研究の中心であり、植物表現型の取得・解析手法として適格。
abstractThis study used infrared thermal imaging combined with deep learning methods to detect natural gas micro-leakage areas and revealed the different canopy temperature characteristics of four vegetation varieties (grass, soybean, corn and wheat) under natural gas stress from 2017 to 2019.
Integration of plant phenotyping and irrigation is particularly advantageous for identifying genetic variation associated with crop productivity. Collecting phenotypic data and water management under controlled or open environment can be expensive and laborious. This study aims to design a cost-effective solution for high-throughput phenotyping (HTP) and automated irrigation using open-source electronics. A portable HTP system was developed using a microcontroller and a single-board computer Raspberry Pi and was extended to include soil water monitoring and water pump control. An Arduino board was integrated with a multispectral camera, mini LiDAR sensors, infrared thermometers, soil moisture sensors, water pumps, and a temperature/humidity sensor. Sensor calibration and power management enhanced the accuracy and reliability of the system. Two genotypes (CAM212 and Giessen#4) of camelina were used to evaluate the system to measure phenotypic responses to abiotic stress in growth chambers under two temperatures (25 °C and 35 °C) and two water treatments (40% and 90% water holding capacity). The HTP system monitored 24 plants periodically, and data were wirelessly accessed by a smartphone and transferred to a computer for further analyses. The system revealed that camelina genotype 1 (CAM212) showed superior resistance to heat and drought stress. The results showed that the developed HTP system offers a cost-effective and portable solution for phenotyping and water management in controlled environment and can be modified for field applications.
Why it matches plant phenotyping methods植物表現型取得システムの開発と校正・評価が研究の中心であり、センサーを統合した高スループット表現型解析基盤を構築している。
abstractThis study aims to design a cost-effective solution for high-throughput phenotyping (HTP) and automated irrigation using open-source electronics.
A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI 1 -DBI 3 ), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.
Why it matches plant phenotyping methodsブロッコリー葉の感染状態を画像センサー、分光反射、葉温度、植生指数、機械学習で推定する検出法を開発・評価しており、植物病害状態のフェノタイピングが中心である。
abstractThe main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Background The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system. Result We selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient (R2) is greater than 0.7156. The root mean square error (RSME) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber. Conclusion It is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.
Why it matches plant phenotyping methods可動式人工気候室と画像・センサーによる作物表現型取得システムを開発し、葉面積・草丈・群落温度を手動測定と比較検証しており、表現型取得法が研究の中心である。
abstractWe propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own drawbacks and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min− 1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30 to 50% and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.
Why it matches plant phenotyping methods植物の蒸散・気孔コンダクタンスを熱画像から推定する新規手法を開発し、ガス交換測定との相関で検証しており、フェノタイピング手法が研究の中心である。
abstractwe developed a method that uses whole-plant thermal imaging in a controlled environment
The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.
Why it matches plant phenotyping methods赤外線カメラによる作物水ストレス指数の非接触・リアルタイム推定が研究の中心であり、従来のCWSIとの相関検証と実用性評価を行っている。
abstractThis research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants.
Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. The aim of this study is to compare the RGB and multispectral data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant, and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected was divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Third, we assessed plant level water stress, as a consequence of the root damage by nematodes, using stomatal conductance measured with a porometer and indirectly using plant temperature with an infrared thermometer, and also the stable carbon isotope composition of leaf dry matter.. It was found that the interaction between treatments and crops (ANOVA) was statistically different for only four of seventeen parameters: flavonoid (p
Why it matches plant phenotyping methods植物の健康状態を複数の近接リモートセンシング機器で測定・比較し、葉・群落・個体レベルの形質抽出を技術的に評価しているため、手法の実質的応用に該当します。
abstractProximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics
AppleLiDAR / point cloudThermalFruitCalibration / preprocessingSegmentationPlant / canopy temperature
Heat and excessive solar radiation can produce abiotic stresses during apple maturation, resulting fruit quality. Therefore, the monitoring of temperature on fruit surface (FST) over the growing period can allow to identify thresholds, above of which several physiological disorders such as sunburn may occur in apple. The current approaches neglect spatial variation of FST and have reduced repeatability, resulting in unreliable predictions. In this study, LiDAR laser scanning and thermal imaging were employed to detect the temperature on fruit surface by means of 3D point cloud. A process for calibrating the two sensors based on an active board target and producing a 3D thermal point cloud was suggested. After calibration, the sensor system was utilised to scan the fruit trees, while temperature values assigned in the corresponding 3D point cloud were based on the extrinsic calibration. Whereas a fruit detection algorithm was performed to segment the FST from each apple.•The approach allows the calibration of LiDAR laser scanner with thermal camera in order to produce a 3D thermal point cloud.•The method can be applied in apple trees for segmenting FST in 3D. Whereas the approach can be utilised to predict several physiological disorders including sunburn on fruit surface.
Why it matches plant phenotyping methodsLiDARと熱画像を校正して3D熱点群を生成し、リンゴ果実表面温度を抽出・分割する手法が研究の中心であるため、植物表現型計測方法として含める。
abstractLiDAR laser scanning and thermal imaging were employed to detect the temperature on fruit surface by means of 3D point cloud.
Rice is well adapted to a wide range of climates, but is highly susceptible to heat during flowering. However, there are uncertainties in assessing the occurrence of heat-induced spikelet sterility (HISS) and the impact of climate change. One reason is the gap between the ambient air temperature and the panicle temperature, which determines the magnitude of HISS in field studies. To improve our understanding of this gap, we established a multi-site monitoring network (MINCERnet) to measure canopy micrometeorology and heat stress in the major rice growing regions (Sub-Saharan Africa; South, Southeast, and East Asia; and the USA). MINCERnet assessed the processes that determine panicle temperature and the resulting HISS in open fields using the same cultivars (‘IR64’, ‘N22’, and ‘IR52’) and a standard system (MINCER) for micrometeorological monitoring under diverse climates. By using the MINCERnet data in the canopy heat-balance model (IM²PACT), we confirmed that the canopy and panicle transpiration and the resulting evaporative cooling strongly affected the gap between the ambient air temperature and the panicle temperature, and that the HISS rate in open fields could be predicted accurately in diverse climates by using the mean panicle temperature during the flowering hours. The “oasis effect” in the broad sense, that is, evaporative cooling and the increase of relative humidity, which is nested at the various levels along the continuum from the landscape to the panicle, formed temperature and relative humidity gradients along the continuum in response to different climatic conditions. The heat-balance characteristics (i.e., a stronger evaporative cooling under drier climate conditions) suggested that the risk of HISS caused by global warming will increase more in wetter climates, where panicle temperatures tended to increase. Thus, accurate relative humidity data as well as air temperature will be required, along with spatial downscaling, to permit accurate prediction of rice heat stress and yield. HISS prediction using an approach based on the panicle temperature as input for models and monitoring of canopy micrometeorology will reduce uncertainties in rice yield prediction and the response of yield to various climate change adaptation measures.
Why it matches plant phenotyping methodsMINCERnetによるキャノピー微気象・穂温の標準化モニタリングとIM²PACTモデルを用いた穂温および高温不稔率の予測が研究の中心であり、植物の生理状態を測定・推定する実質的なフェノタイピング手法である。
abstractwe established a multi-site monitoring network (MINCERnet) to measure canopy micrometeorology and heat stress
In the present study, individual and combined effects of drought and heat stress were investigated on key physiological parameters (canopy temperature, membrane stability index, chlorophyll content, relative water content, and chlorophyll fluorescence) in two popular sorghum cultivars (Sorghum bicolor cvs. Phule Revati and Phule Vasudha) during the seedling stage. Estimating canopy temperature through pixel-wise analysis of thermal images of plants differentiated the stress responses of sorghum cultivars more effectively than the conventional way of recording canopy temperature. Cultivar difference in maintaining the canopy temperature was also responsible for much of the variation found in critical plant physiological parameters such as cell membrane stability, chlorophyll content, and chlorophyll fluorescence in plants exposed to stress. Hence, the combined stress of drought and heat was more adverse than their individual impacts. The continued loss of water coupled with high-temperature exposure exacerbated the adverse effect of stresses with a remarkable increase in canopy temperature. However, Phule Vasudha, being a drought-tolerant variety, was relatively less affected by the imposed stress conditions than Phule Revati. Besides, the methodology of measuring and reporting plant canopy temperature, which emerged from this study, can effectively differentiate the sorghum genotypes under the combined stress of drought and heat. It can help select promising genotypes among the breeding lines and integrating the concept in the protocol for precision water management in crops like sorghum.
Why it matches plant phenotyping methods熱画像のピクセル単位解析による植物キャノピー温度の測定・報告法を開発し、ソルガム遺伝子型のストレス応答識別に応用しており、表現型取得法が実質的に扱われている。
abstractEstimating canopy temperature through pixel-wise analysis of thermal images of plants differentiated the stress responses of sorghum cultivars more effectively than the conventional way of recording canopy temperature.
Water stress mapping in crops and its spatial disparity study at field scale is important for precise management of irrigation. Results obtained from conventional airborne practice (balloons, airplanes, and satellites) are less acceptable for timely irrigation management due to lack in spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with multispectral (MS) and thermal cameras with higher spectral and temporal resolutions can be used as a promising tool for preparing water stress maps under different water deficit conditions. In this study, Water Deficit Index (WDI) maps are generated at different days after sowing (DAS) in wheat crops under three different water conditions (WI (well water), WS1(irrigation at 5 days’ interval), and WS2 (irrigation at 11 days’ interval)) using the concept of Vegetation Index Trapezoid (VIT) using UAV based thermal and MS imageries. The UAV is flown at 60m altitude during the Rabi season 2018-19. After pre-processing of images in Pix4dMapper, nine vegetation indices are calculated from MS images and one of the indices, Normalized Green Red Difference Index (NGRDI) is selected based on the higher correlation with ground truth data (R 2 greater than 0.5) and visual interpretation according to the real field condition to construct the VIT. Vegetation index and temperature values are calculated for four points of VIT by using four boundary conditions such as bare soil with (1) dry and (2) wet conditions, and full vegetation with (3) well-watered and (4) water stress conditions. By using the ArcGIS, geo-referencing of thermal images with respect to MS images is done to get the exact overlap of both images, and resampling of thermal and MS images are also carried out to get the same pixel size. WDI values are estimated using VIT of the surface-air temperature difference and NGRDI, and WDI maps are generated from the UAV-based thermal and MS imageries for potential detection of crop water stress. The conventional Crop Water Stress Index (CWSI) which is solely based on the crop canopy temperature is outperformed by the WDI, which is integration of composite land surface temperature (LST) and degree of greenness, and could be effective enough for irrigation water management. Keywords: UAV, Multispectral and Thermal imageries, NGRDI, WDI, and Wheat crop.
Why it matches plant phenotyping methodsUAVの熱画像・マルチスペクトル画像からWDIを算出し、コムギの水ストレスを地図化する手法が研究の中心であるため、植物フェノタイピング手法として含める。
titleWater Deficit Index (WDI) Mapping of Wheat Crop for Water Stress Detection Using UAV-based Remote Sensing
Pecans are a specialty crop in New Mexico’s Lower Rio Grande Valley (LRGV), a region that produces around 30% of pecans in USA. Pecans are also a major water consumer, requiring 1200–1300 mm depth for maximum yield in this region. The combination of prolonged drought and increasing competition for water among various water consumers has created an urgency for the efficient use of scarce water resources in the LRGV. More efficient water management through the real-time irrigation scheduling is one method to promote reduced water application in agriculture. This study was conducted to calibrate and validate a new modified model for estimating the pecan actual evapotranspiration (ETₐ) based on canopy temperature using thermal images taken from an Unmanned Aerial Vehicle (UAV) during three growing seasons in a drip irrigated pecan orchard. A capacity to estimate the relation between ETₐ and canopy temperature provides an important information to guide water management choices. The Simplified Surface Energy Balance (SSEBop) model was modified and used for calibration and validation. Applied irrigation water based on ETₐ was used to calibrate and validate the proposed modified model. The scaling factor of K in the SSEBop model was calculated as 0.75 through the calibration process. Findings showed a good agreement between estimated pecan ETₐ using modified SSEBop model and applied water based on ETₐ during calibration (R² = 0.72, RMSE = 0.6 mm/d, MAE = 0.48 mm/d) and validation period (R² = 0.90, RMSE = 0.24 mm/d, MAE = 0.22 mm/d). Also, findings confirmed the utility of modified model for estimating monthly pecan ETₐ (RMSE = 8.87 mm/month, MAE = 6.55 mm/month). The proposed modified model provides pecan farmers with a simple real-time irrigation scheduling tool where they can better practice precision irrigation. Although the modified model was calibrated and validated for irrigation scheduling in the LRGV, it has potential to see application for other locations with different crops using similar calibration approach.
Why it matches plant phenotyping methodsUAV熱画像からペカン樹冠温度を用いて蒸発散量を推定するモデルを開発・較正・検証しており、植物の生理状態（水利用）を取得する方法が中心である。
abstractcalibrate and validate a new modified model for estimating the pecan actual evapotranspiration (ETₐ) based on canopy temperature using thermal images taken from an Unmanned Aerial Vehicle (UAV)
To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum ( Brassica chinensis var. parachinensis ) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90-100% FC as no water stress (NWS); 80-90% FC for light water stress (LWS); 70-80% FC for moderate water stress (MWS); and 60-70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSI W ) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSI W , VIs, and water stress indicators were statistically analyzed with R 2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSI W obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSI W and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.
Why it matches plant phenotyping methods赤外線カメラによるCWSIと分光反射指数を用いた植物の水ストレス推定が研究の中心であり、非破壊的な表現型取得法の有効性を評価している。
abstractThis study explores the effectiveness of estimating water stress in choy sum ( Brassica chinensis var. parachinensis ) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators
Plants modify the climate and provide natural cooling through transpiration. However, plant response is not only dependent on the atmospheric evaporative demand due to the combined effects of wind speed, air temperature, humidity, and solar radiation, but is also dependent on the water transport within the plant leaf-xylem-root system. These interactions result in a dynamic response of the plant where transpiration hysteresis can influence the cooling provided by the plant. Therefore, a detailed understanding of such dynamics is key to the development of appropriate mitigation strategies and numerical models. In this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques. The wake flow field is measured using stereoscopic particle image velocimetry, the spatiotemporal leaf temperature history is obtained using infrared thermography, and additionally, the plant porosity is obtained using X-ray tomography. We find that the wake velocity statistics are not directly linked with the distribution of the porosity but depends mainly on the geometry of the plant foliage which generates the shear flow. The interaction between the shear regions and the upstream boundary layer profile is seen to have a dominant effect on the wake turbulent kinetic energy distribution. Furthermore, the leaf area density distribution has a direct impact on the short-wave radiative heat flux absorption inside the foliage where 50% of the radiation is absorbed in the top 20% of the foliage. This localized radiation absorption results in a high local leaf and air temperature. Furthermore, a comparison of the diurnal variation of leaf temperature and the net plant transpiration rate enabled us to quantify the diurnal hysteresis resulting from the stomatal response lag. The day of this plant is seen to comprise of four stages of climatic conditions: no-cooling, high-cooling, equilibrium, and decaying-cooling stages.
Why it matches plant phenotyping methods複数の高解像度非侵襲イメージング技術を中核として、葉温、植物空隙率、葉面積密度、蒸散応答などの植物形質・状態を取得・解析しており、単なるルーチン測定ではない。
abstractIn this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques.
The quality of wine grapes in dry climates greatly depends on utilizing optimal amounts of irrigation water during the growing season. Robust and accurate techniques are essential for assessing crop water status in grapevines so that both over-irrigation and excessive water deficits can be avoided. This study proposes a robust strategy to assess crop water status in grapevines. Experiments were performed on Riesling grapevines (Vitis vinfera L.) planted in rows oriented north–south and subjected to three irrigation regimes in a vineyard maintained at an experimental farm in southeastern Washington, USA. Thermal and red–green–blue (RGB) images were acquired during the growing season, using a thermal imaging sensor and digital camera installed on a ground-based platform such that both cameras were oriented orthogonally to the crop canopy. A custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images. The relationship between leaf water potential (Ψleaf) and CWSI was investigated. The results revealed that the proposed algorithm combining thermal and RGB images to determine CWSI can be used for assessing crop water status of grapevines. There was a correlation between CWSI and Ψleaf with an R-squared value of 0.67 for the measurements in the growing season. It was also found that CWSI from the shaded (east) side of the canopy achieved a better correlation with Ψleaf compared to that from the sunlit (west) side around solar noon. The created algorithm allowed real-time assessment of crop water status in commercial vineyards and may be used in decision support systems for grapevine irrigation management.
Why it matches plant phenotyping methods熱画像とRGB画像からブドウ樹の葉温・作物水ストレス指数を自動抽出するアルゴリズムを開発・検証しており、植物状態の取得手法が研究の中心である。
abstractA custom-developed algorithm was created to automatically derive canopy temperature (Tc) and calculate crop water stress index (CWSI) from the acquired thermal-RGB images.
Soybean is most often grown under rainfed conditions and negatively impacted by drought stress in the upper mid-south of the United States. Therefore, identification of drought-tolerance traits and their corresponding genetic components are required to minimize drought impacts on productivity. Limited transpiration (TR lim ) under high vapor pressure deficit (VPD) is one trait that can help conserve soybean water-use during late-season drought. The main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait. A secondary objective was to undertake a genetic marker/quantitative trait locus (QTL) genetic analysis using the AgNO 3 phenotyping results. A set of 122 soybean genotypes (120-RILs and parents) were grown in controlled environments (32/25-d/n °C). The transpiration rate (TR) responses of derooted soybean shoots before and after application of AgNO 3 were measured under 37°C and >3.0 kPa VPD. Then, the decrease in transpiration rate (DTR) for each genotype was determined. Based on DTR rate, a diverse group (slow, moderate, and high wilting) of 26 RILs were selected and tested for the whole plant TRs under varying levels of VPD (0.0-4.0 kPa) at 32 and 37°C. The phenotyping results showed that 88% of slow, 50% of moderate, and 11% of high wilting genotypes expressed the TR lim trait at 32°C and 43, 10, and 0% at 37°C, respectively. Genetic mapping with the phenotypic data we collected revealed three QTL across two chromosomes, two associated with TR lim traits and one associated with leaf temperature. Analysis of Gene Ontologies of genes within QTL regions identified several intriguing candidate genes, including one gene that when overexpressed had previously been shown to confer enhanced tolerance to abiotic stress. Collectively these results will inform and guide ongoing efforts to understand how to deploy genetic tolerance for drought stress.
Why it matches plant phenotyping methodsAgNO3を用いた高スループット測定法でダイズの蒸散制限形質を取得し、表現型データをQTL解析に用いており、フェノタイピング手法の適用が研究の中心です。
abstractThe main research objective was to evaluate a recombinant inbred line (RIL) population, from crossing two mid-south soybean lines ("Jackson" × "KS4895"), using a high-throughput technique with an aquaporin inhibitor, AgNO 3 , for the TR lim trait.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary File 3
R/QTL package file containing genotypic and phenotypic data used for genetic mapping.Open asset ↗lines:1432-1530Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Tree architecture shows large genotypic variability, but how this affects water-deficit responses is poorly understood. To assess the possibility of reaching ideotypes with adequate combinations of architectural and functional traits in the face of climate change, we combined high-throughput field phenotyping and genome-wide association studies (GWAS) on an apple tree (Malus domestica) core-collection. We used terrestrial light detection and ranging (T-LiDAR) scanning and airborne multispectral and thermal imagery to monitor tree architecture, canopy shape, light interception, vegetation indices and transpiration on 241 apple cultivars submitted to progressive field soil drying. GWAS was performed with single nucleotide polymorphism (SNP)-by-SNP and multi-SNP methods. Large phenotypic and genetic variability was observed for all traits examined within the collection, especially canopy surface temperature in both well-watered and water deficit conditions, suggesting control of water loss was largely genotype-dependent. Robust genomic associations revealed independent genetic control for the architectural and functional traits. Screening associated genomic regions revealed candidate genes involved in relevant pathways for each trait. We show that multiple allelic combinations exist for all studied traits within this collection. This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted.
Why it matches plant phenotyping methods高スループット圃場フェノタイピングを中核として、T-LiDAR、マルチスペクトル・熱画像から樹体構造、光 interception、蒸散などの植物形質を測定しているため。
abstractwe combined high-throughput field phenotyping and genome-wide association studies (GWAS) on an apple tree (Malus domestica) core-collection.
Reproduction assets foundThe paper's raw phenotypes and BLUPs (T-LiDAR architectural traits, thermal/multispectral indices, water potentials) are publicly deposited on Portail Data INRAE at https://doi.org/10.15454/C8IPII, explicitly stated in the Data availability section. The SNP genotyping deposit (10.15454/F5XIVJ) is a molecular omics-typeDataset · publicRaw data and BLUPs of phenotypes together with the list of the 241 cultivars with the recently attributed MUNQ codes (for Malus UNiQue genotype code, Denancé et al ., 2020 ) are publicly available in Coupel‐Ledru et al . ( 2022 ) at this site: https://doi.org/10.15454/C8IPIIOpen asset ↗10.15454/C8IPIIlines:663-812Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Background : The deficiencies of traditional artificial climate chambers in phenotypic collection and analysis were improved to achieve the high-throughput acquisition of crop phenotypes during the growth period. This paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period. This research also established an environmental control system, a crop phenotype monitoring system and a crop phenotype acquisition system with environmental parameter adjustment and crop image collection. Phenotypic feature extraction and other functions were carried out in the cultivation experiment, and phenotype acquisition of wheat was performed under different nitrogen fertiliser application rates. Comparison and analyses were performed by the systematic and manual measurement values of crop phenotype characteristics, and the acquisition of wheat table was evaluated based on artificial intelligence climate cabin. The goodness of fit of the model was used to classify data. Results: During the different growth periods of wheat, the correlation analysis between the systematic and manual measurement values of its leaf area, plant height and canopy temperature showed that the obtained correlation coefficient r was greater than 1, and the fitting determination coefficient R 2 was greater than 0.7156, with errors. The coefficient root mean square error was less than 2.42, indicating that the two were positively correlated, and their correlation was excellent. Conclusion: The results verified the feasibility and applicability of the artificial intelligence climate cabin to study the phenotypic characteristics of crops.
Why it matches plant phenotyping methods作物表現型の高スループット取得を目的に、環境制御・画像収集・特徴抽出を統合した気候チャンバーを開発し、手動測定との比較で検証しているため、表現型手法が中心です。
abstractThis paper has developed an artificial intelligence climate cabin with functions of crop cultivation management and phenotype acquisition during the whole growth period.
The phenotypic diagnostics in food mycology exhibit hysteresis in fungal detection during the early stages since hyphal and fruiting structures become visible only in advanced growth stage. Aspergillus flavus is a saprotrophic and pathogenic fungus colonizing cereal grains, legumes, and tree nuts. This fungus produces significant quantities of aflatoxins which have nephrotoxic, hepatotoxic, teratogenic and immunosuppressive effects on humans. The spatial temperature heterogeneity of A. flavus in infected pistachios was assessed by means of thermal imaging along with the digital RGB imaging analysis. The image analysis in terms of hue angle, exhibited not significant variation of non-infected pistachios compared to the infected ones at the early stages of fungal invasion but it became significantly different 72 h since infection. The fungal growth rate was assessed by comparing the Weibull shape factor of pistachios inoculated with A. flavus against grapes inoculated with A. carbonarius. The latter showed that the first 6 h since inoculation showed similar growth rate (∂β¯/∂t=6.7) while between 6 and 20 h showed lesser but almost double growth rate of the infected grapes (∂β¯grapes/∂t=5.4) against the infected pistachios (∂β¯pistachios/∂t=2.2). Infected pistachios and grapes were both stored at 28 °C and 60% RH, hence, the noted variation in growth rate should be fungus and substrate dependent. The peak of the temporal variation of the Weibull shape factor in pistachios and grapes extends between 15 and 20 h since inoculation, while for the infected grapes it is 71% higher than the one of the infected pistachios, revealing a distinctive fungal growth rate.
Why it matches plant phenotyping methods熱画像とRGB画像を用いて、ピスタチオおよびブドウの感染状態・真菌生育速度を直接評価する手法が研究の中心であり、植物器官の病害状態を測定している。
abstractThe spatial temperature heterogeneity of A. flavus in infected pistachios was assessed by means of thermal imaging along with the digital RGB imaging analysis.
Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination ( R 2 ) = 0.527-0.667] compared to using a single multispectral sensor ( R 2 = 0.130-0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R 2 = 0.667, root mean square error (RMSE) = 0.881 t ha -1 , relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha -1 . The EWF model outperformed at all the individual growth stages with R 2 varying from 0.677 to 0.729. The best prediction result ( R 2 = 0.729, RMSE = 0.831 t ha -1 , RRMSE = 14.3%, and MAE = 0.684 t ha -1 ) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像から小麦の収量形質を推定する方法を開発・評価し、特徴選択、弾性ネット回帰、マルチステージ融合の精度を検証しているため、表現型取得・推定が中心です。
abstractWe evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments.
Global warming and water resource shortage greatly influence the crop growth and negatively affect the crop yield. Breeding water–stress resistant crop varieties is one of the effective ways to handle this problem. The surface temperatures of the crop are essential for assessing their water stress resistance. Thermal imaging has been widely used to acquire the crop surface temperatures. However, most studies focus on 2D measurement. A 3D thermal imaging system is designed, and a method is developed by using the thermal and RGB-D data to acquire 3D thermal information of maize under water stress conditions. First, thermal and RGB-D cameras were used to collect the thermal and color images, and depth data of maize at the jointing stage and the thermal and color images were processed to extract the edge images of maize. Second, the KAZE feature was selected to register the edge images. Testing results showed that the KAZE feature has better performance than the SURF and BRISK features in the thermal and color image registration of maize. On the basis of the thermal and color image registration, the depth data of maize were assigned with temperature values. Third, the depth data of maize were further processed with denoising, maize extraction, amplification, smoothing, and temperature correction steps to improve the data qualities. Finally, the crop water stress index and canopy–air temperature difference values of each point were calculated. The results demonstrated that the system and the proposed method can effectively detect the water stress characteristics of maize in 3D, which can be combined with the morphological traits of leaves to synthetically analyze the water stress resistance of the crop.
Why it matches plant phenotyping methodsトウモロコシの3D熱情報を取得し、水ストレス指標を算出する画像・センサ計測法の開発が中心であり、植物表現型測定法に該当する。
abstractA 3D thermal imaging system is designed, and a method is developed by using the thermal and RGB-D data to acquire 3D thermal information of maize under water stress conditions.
Most lowland rice in West Africa depends mainly on rainfall for water supply. Drought is consequently one of the major constraints on rice production, drastically affecting both plant growth and development. The objective of this work was to study the impact of water deficit both on canopy temperature and on chlorophyll fluorescence level, used as indicators of transpiration and photosynthetic activity. Measurements using infrared thermography and fluorimetry were taken on both 17 lines resulting from the cross IR64 X B6144F-MR-6-0-0 and their two parents plus one tolerant (APO) controls. These 20 lines were phenotyped after applying a drought constraint in a controlled laboratory environment in Montpellier (France) in 2013 and - 2014 and in field in the lowlands of Banfora and Farako-ba (INERA Burkina Faso) in 2014. Results showed that the drought stress sustained by the plants increased canopy temperature in all lines, entailing differential disturbance of the photosynthetic process, markedly depressed in susceptible lines. A classification of the lines with respect to their sensitivity to stress could be established by using the Drought Factor Index (DFI), and Crop Water Stress Index (CWSI) as was established a correlation between the phenotyping methods by infrared thermography and fluorimetry. This article propose an efficient application of combined imaging as a rapid and accurate phenotyping tool for crop yield improvement, in particular by monitoring the efficiency of plant responses to the fluctuating of environmental conditions. This study proved the efficiency of the method combining IR thermographie and fluorimetry as a field phenotyping tools for drought resistance.
Why it matches plant phenotyping methods赤外線サーモグラフィーと蛍光測定を組み合わせ、イネの干ばつ応答を高スループットに評価する手法の適用・有効性検証が研究の中心である。
abstractThis article propose an efficient application of combined imaging as a rapid and accurate phenotyping tool for crop yield improvement
Most lowland rice in West Africa depends mainly on rainfall for water supply. Drought is consequently one of the major constraints on rice production, drastically affecting both plant growth and development. The objective of this work was to study the impact of water deficit both on canopy temperature and on chlorophyll fluorescence level, used as indicators of transpiration and photosynthetic activity. Measurements using infrared thermography and fluorimetry were taken on both 17 lines resulting from the cross IR64 X B6144F-MR-6-0-0 and their two parents plus one tolerant (APO) controls. These 20 lines were phenotyped after applying a drought constraint in a controlled laboratory environment in Montpellier (France) in 2013 and - 2014 and in field in the lowlands of Banfora and Farako-ba (INERA Burkina Faso) in 2014. Results showed that the drought stress sustained by the plants increased canopy temperature in all lines, entailing differential disturbance of the photosynthetic process, markedly depressed in susceptible lines. A classification of the lines with respect to their sensitivity to stress could be established by using the Drought Factor Index (DFI), and Crop Water Stress Index (CWSI) as was established a correlation between the phenotyping methods by infrared thermography and fluorimetry. This article propose an efficient application of combined imaging as a rapid and accurate phenotyping tool for crop yield improvement, in particular by monitoring the efficiency of plant responses to the fluctuating of environmental conditions. This study proved the efficiency of the method combining IR thermographie and fluorimetry as a field phenotyping tools for drought resistance.
Why it matches plant phenotyping methods赤外線サーモグラフィーと蛍光測定を組み合わせたイネの乾燥耐性フェノタイピング手法を開発・評価し、手法間の相関と効率を検証しているため、方法が中心的である。
abstractA classification of the lines with respect to their sensitivity to stress could be established by using the Drought Factor Index (DFI), and Crop Water Stress Index (CWSI) as was established a correlation between the phenotyping methods by infrared thermography and fluorimetry.
An autonomous mobile ground-control point (AMGCP) was redesigned and refined for improved collaborative operation with an unmanned aerial vehicle (UAV) to enable calibration of image mosaics from multispectral (MS) and thermal cameras. The AMGCP has built-in reflectance panels and electronically controlled thermal panels that provide high and low reflectance and temperature references that can be used for calibration of reflectance measurements in MS images and temperature measurements in thermal-infrared images. The AMGCP also has an onboard temperature sensor that enables image-based temperature measurements to be compared to ambient temperature so that canopy temperature depression (CTD) can be calculated. The collaborative robotic system consists of the AMGCP and a UAV that have real-time kinematic (RTK) geographic positioning system (GPS) receivers onboard so their precise position can be determined in real time. The system also includes wireless communication capability between the AMGCP and UAV so they can transmit their position and other data to each other during a mission, in which the AMGCP positions itself at multiple locations under the flight path of the UAV, providing multiple instances of reflectance and temperature references in image mosaics collected by the UAV. Testing has shown that reflectance measurements can be calibrated to less than 1% reflectance error, and canopy temperatures of crop plants can be calibrated to within 1.0 C, enabling consistently accurate measurements to be made efficiently and without human intervention in various fields and regions and at different times and dates. This system is also suited to accurate measurement of CTD to facilitate genetic selection relative to various stresses and resilience characteristics like drought tolerance.
Why it matches plant phenotyping methodsUAVと自律移動基準点を連携させ、反射率・熱画像を校正して作物のキャノピー温度降下を測定するシステムの開発と精度検証が中心である。
abstractAn autonomous mobile ground-control point (AMGCP) was redesigned and refined for improved collaborative operation with an unmanned aerial vehicle (UAV) to enable calibration of image mosaics from multispectral (MS) and thermal cameras.
A field experiment was conducted with soybean to observe evapotranspiration (ET) and crop water stress index (CWSI) with three watering levels at Keszthely, Hungary, during the growing seasons 2017–2020. The three different watering levels were rainfed, unlimited, and water stress in flowering. Traditional and converted evapotranspirometers documented water stress levels in two soybean varieties (Sinara, Sigalia), with differing water demands. ET totals with no significant differences between varieties varied from 291.9 to 694.9 mm in dry, and from 205.5 to 615.6 mm in wet seasons. Theoretical CWSI, CWSIt was computed using the method of Jackson. One of the seasons, the wet 2020 had to be excluded from the CWSIt analysis because of uncertain canopy temperature, Tc data. Seasonal mean CWSIt and Tc were inversely related to water use efficiency. An unsupervised Kohonen self-organizing map (K-SOM) was developed to predict the CWSI, CWSIp based on easily accessible meteorological variables and Tc. In the prediction, the CWSIp of three watering levels and two varieties covered a wide range of index values. The results suggest that CWSIp modelling with the minimum amount of input data provided opportunity for reliable CWSIp predictions in every water treatment (R2 = 0.935–0.953; RMSE = 0.033–0.068 mm, MAE = 0.026–0.158, NSE = 0.336–0.901, SI = 0.095–0.182) that could be useful in water stress management of soybean. However, highly variable weather conditions in the mild continental climate of Hungary might limit the potential of CWSI application. The results in the study suggest that a less than 450 mm seasonal precipitation caused yield reduction. Therefore, a 100–160 mm additional water use could be recommended during the dry growing seasons of the country. The 150 year-long local meteorological data indicated that 6 growing seasons out of 10 are short of precipitation in rainfed soybean.
Why it matches plant phenotyping methods大豆の水ストレス状態を表すCWSIを、気象変数と葉冠温度から推定するK-SOMモデルを開発し、精度指標で検証しているため、植物フェノタイピング手法が中心的である。
abstractAn unsupervised Kohonen self-organizing map (K-SOM) was developed to predict the CWSI, CWSIp based on easily accessible meteorological variables and Tc.
The crop water stress index (CWSI), based on canopy temperature (Tc), has been widely used in evaluating plant water status and planning irrigation scheduling, but whether CWSI can diagnose the stress status of crops and predict the physiological traits and growth under combined water and salt stress remains to be further studied. Here, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB). The relationships between physiology, growth, and yield of maize with CWSI were analyzed. There were significant differences in NWSB between the two maize genotypes at the same and different growth stages; thus, growth stage-specific NWSBs were used. The difference in NWSB was due to the difference and change in effective leaf width. CWSI was closely related to leaf water potential, stomatal conductance, and net photosynthetic rate under different water and salt stress, and also explained the variations in leaf area index, biomass, water use, and yield. Collectively, CWSI can be used as a proxy indicator of high-throughput phenotyping maize performance under combined water and salt stress, which will be valuable for predicting yield and improving water use efficiency.
Why it matches plant phenotyping methods連続的な冠層温度からCWSIモデルと生育段階・遺伝子型別基準を構築し、ストレス状態や生理・生育・収量を推定する高スループット表現型手法が研究の中心である。
abstractHere, a model of CWSI was established based on the continuous measurements of Tc for two maize genotypes (ZD958 and XY335) under two water and salt conditions, combined with growth stage-specific non-water-stressed baselines (NWSB).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.
Why it matches plant phenotyping methodsUAVの光学・熱画像を用いて個体分割、植生抽出、植物温度および水ストレス指標を推定する方法を開発・評価しており、植物表現型取得が研究の中心です。
abstractOur study focuses on a phenotyping experiment
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The ability to avoid dehydration is a drought resistance mechanism becoming increasingly more important even in temperate regions. In wheat, dehydration avoidance can be associated with a maintained canopy cooling during dry periods. However, in an average year under temperate conditions, drought periods are rather short which makes it difficult to routinely screen for drought avoidance using canopy temperature (CT). Furthermore, confounding factors such as differences in height, shoot biomass, canopy structure and phenology complicate the interpretation of differences in CT. We aimed to use temporal trends in CT and canopy greenness during short-term drought and heat events in the early grain filling phase to circumvent these problems. During this phase, evaporative demand is high and phenology-driven senescence has yet little effect on CT. Diverse sets of 354 and 71 wheat genotypes where grown in the field phenotyping platform of ETH Zurich in 2018 and 2019, respectively. CT was repeatedly measured during early grain filling by means of drone-based imaging. The temporal trends in CT during early grain filling showed a moderate to high within-year heritability (h2 = 0.35 and h2 = 0.88 in 2018 and 2019, respectively). These trends were largely independent of confounding factors when compared to single time point measurements and likely represent genotype-specific reactions to decreasing water availability more directly than absolute CT. CT trends were also largely independent of the temporal trends in stay-green indices. Therefore, we used a combination of time-resolved CT and stay-green trends to identify genotypes combining both traits. Significant differences were observed in the combined time-resolved index among three replicated check varieties. We therefore propose to use the combined time-resolved index to identify genotypes with improved drought avoidance and functional stay green.
Why it matches plant phenotyping methodsドローン画像による時系列キャノピー温度・緑色度測定と、干ばつ回避・stay-greenの表現型指標の構築が研究の中心であり、遺伝子型間比較と再現性評価も行っている。
abstractCT was repeatedly measured during early grain filling by means of drone-based imaging.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 8 Sept 2026
O_LIAlmost all terrestrial biosphere models (TBMs) still assume infinite mesophyll conductance (gm) to estimate photosynthesis and transpiration. This assumption has caused low accuracy of TBMs to predict leaf gas exchange under certain conditions. C_LIO_LIIn this study, we developed a photosynthesis-transpiration coupled model that explicitly considers gm and designed an optimized parameterization solution through evaluating four different gm estimation methods in 19 C3 species at 31 experimental treatments. C_LIO_LIResults indicated that temperature responses of the maximum carboxylation rate (Fcmax) and the electron transport rate (Jmax) estimated by fusing the Bayesian retrieval algorithm and the Sharkey online calculator together with gm temperature response estimated by fusing the chlorophyll fluorescence-gas exchange method and anatomy method predicted leaf gas exchange more accurately. The gm temperature response exhibited activation energy ({Delta}Ha) of 63.13 {+/-} 36.89 kJ mol-1 and entropy ({Delta}S) of 654.49 {+/-} 11.36 J K-1 mol-1. The gm optimal temperature (Topt_gm) explained 58% of variations in photosynthesis optimal temperature (ToptA). The gm explicit expression has equally important effects on photosynthesis and transpiration estimations. C_LIO_LIResults advanced understandings of better representation of plant photosynthesis and transpiration in TBMs. C_LI
Why it matches plant phenotyping methods植物の光合成・蒸散という生理形質を推定する結合モデルを開発し、複数の推定法を評価して葉ガス交換予測精度を検証しているため、方法が中心である。
abstractwe developed a photosynthesis-transpiration coupled model that explicitly considers gm and designed an optimized parameterization solution through evaluating four different gm estimation methods in 19 C3 species at 31 experimental treatments.
A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
Why it matches plant phenotyping methods小麦育種試験で近接センシングによりNDVI・群落温度を取得し、統計モデルで収量を予測するワークフローを5年間検証しており、形質取得と予測手法が研究の中心である。
abstractThe objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh.
Reproduction assets foundThe paper's data availability statement explicitly deposits all phenotypic data (NDVI, CT, agronomic traits) and analysis code in the Dryad Digital Repository with a public DOI, making it a paper-specific, publicly actionable asset.Dataset · publicAll phenotypic data and code for analysis have been placed in the Dryad Digital Repository available at: https://doi.org/10.5061/dryad.vdncjsxrz .Open asset ↗Dryad Digital Repository · 10.5061/dryad.vdncjsxrzlines:724-739Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 8 Sept 2026
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.
Why it matches plant phenotyping methodsUAVマルチセンサー基盤、校正、データ処理、形質抽出を開発し、地上データで検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Holm oak decline is a complex phenomenon mainly influenced by the presence of Phytophthora cinnamomi and water stress. Plant functional traits (PTs) are altered during the decline process — initially affecting the physiological condition of the plants with non-visual symptoms and subsequently the leaf pigment content and canopy structure — being its quantification critical for the development of scalable detection methods for effective management. This study examines the relationship between spectral-based PTs and oak decline incidence and severity. We evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline. Field surveys comprising more than 1100 trees with varying disease incidence and severity were used to train and validate the model and predictions. Declining trees showed decreases of model-based PTs such as water, chlorophyll, carotenoid, and anthocyanin contents, as well as fluorescence and leaf area index, and increases in crown temperature and dry matter content, compared to healthy trees. Our classification model built using different PT indicators showed up to 82% accuracy for decline detection and successfully identified 34% of declining trees that were not detected by visual inspection and confirmed in a re-evaluation 2 years later. Among all variables analysed, canopy temperature was identified as the most important variable in the model, followed by chlorophyll fluorescence. This methodological approach identified spectral plant traits suitable for the detection of pre-symptomatic trees and mapping of oak forest disease outbreaks up to 2 years in advance of identification via field surveys. Early detection can guide management activities such as tree culling and clearance to prevent the spread of dieback processes. Our study demonstrates the utility of 3-D RTM models to untangle the PT alterations produced by oak decline due to its heterogeneity. In particular, we show the combined use of RTM and machine learning classifiers to be an effective method for early detection of oak decline potentially applicable to many other forest diseases worldwide.
Why it matches plant phenotyping methodsハイパースペクトル・熱画像、3-D放射伝達モデル、機械学習を用いて、樹木の生理・構造形質と病害状態を推定し、技術的に検証した研究であり、植物フェノタイピング手法が中心である。
abstractWe evaluate the use of high-resolution hyperspectral and thermal imagery (< 1 m) together with a 3-D radiative transfer model (RTM) to assess a supervised classification model of holm oak decline.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Field / plotThermalWhole plant / canopy / plot / fieldSegmentationPlant / canopy temperature
Crop canopy temperature measurement is necessary for monitoring water stress indicators such as the Crop Water Stress Index (CWSI). Water stress indicators are very useful for irrigation strategies management in the precision agriculture context. For this purpose, one of the techniques used is thermography, which allows remote temperature measurement. However, the applicability of these techniques depends on being affordable, allowing continuous monitoring over multiple field measurement. In this article, the development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented. In addition, we provide results on almond trees comparing our system with a commercial thermal camera, in which an R-squared of 0.75 is obtained.
Why it matches plant phenotyping methods作物キャノピー温度という植物の生理状態指標を取得する低コスト熱画像センサーと画像セグメンテーション手法を開発し、商用カメラとの比較検証も行っており、フェノタイピング手法が中心である。
abstractthe development of a sensor capable of automatically measuring the crop canopy temperature by means of a low-cost thermal camera and the implementation of artificial intelligence-based image segmentation models is presented.
Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.
Why it matches plant phenotyping methodsUAV熱画像・気象センサーとCWSIを統合し、作物の水ストレス状態および水要求量を推定する計測・処理方法が中心で、UAV機能評価とシステム標準化も行っているため。
abstractThe proposed methodology was based on the crop water stress index (CWSI)
The goal of this research is to use a WORKSWELL WIRIS AGRO R INFRARED CAMERA (WWARIC) to assess the crop water stress index (CWSIW) on tomato growth in two soil types. This normalized index (CWSI) can map water stress to prevent drought, mapping yield, and irrigation scheduling. The canopy temperature, air temperature, and vapor pressure deficit were measured and used to calculate the empirical value of the CWSI based on the Idso approach (CWSIIdso). The vegetation water content (VWC) was also measured at each growth stage of tomato growth. The research was conducted as a 2 × 4 factorial experiment arranged in a Completely Randomized Block Design. The treatments imposed were two soil types: sandy loam and silt loam, with four water stress treatment levels at 70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC on the growth of tomatoes to assess the water stress. The results revealed that CWSIIdso and CWSIW proved a strong correlation in estimating the crop water status at R2 above 0.60 at each growth stage in both soil types. The fruit expansion stage showed the highest correlation at R2 = 0.8363 in sandy loam and R2 = 0.7611 in silt loam. VWC and CWSIW showed a negative relationship with a strong correlation at all the growth stages with R2 values above 0.8 at p
Why it matches plant phenotyping methods熱赤外カメラによるキャノピー温度とCWSIを用いてトマトの水分ストレス状態を推定し、既存指標との相関で検証しており、植物生理状態の取得・評価法が中心です。
abstractThe goal of this research is to use a WORKSWELL WIRIS AGRO R INFRARED CAMERA (WWARIC) to assess the crop water stress index (CWSIW) on tomato growth in two soil types.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Screening for drought tolerance requires precise techniques like phonemics, which is an emerging science aimed at non-destructive methods allowing large-scale screening of genotypes. Large-scale screening complements genomic efforts to identify genes relevant for crop improvement. Thirty maize inbred lines from various sources (exotic and indigenous) maintained at Dryland Agriculture Research Station were used in the current study. In the automated plant transport and imaging systems (LemnaTec Scanalyzer system for large plants), top and side view images were taken of the VIS (visible) and NIR (near infrared) range of the light spectrum to capture phenes. All images were obtained with a thermal imager. All sensors were used to collect images one day after shifting the pots from the greenhouse for 11 days. Image processing was done using pre-processing, segmentation and flowered by features’ extraction. Different surrogate traits such as pixel area, plant aspect ratio, convex hull ratio and calliper length were estimated. A strong association was found between canopy temperature and above ground biomass under stress conditions. Promising lines in different surrogates will be utilized in breeding programmes to develop mapping populations for traits of interest related to drought resilience, in terms of improved tissue water status and mapping of genes/QTLs for drought traits.
Why it matches plant phenotyping methods自動撮像・熱画像・画像処理によってトウモロコシの乾燥耐性関連形質を大規模に抽出するフェノタイピング手法の実質的応用であり、測定法が研究の中心です。
abstractScreening for drought tolerance requires precise techniques like phonemics, which is an emerging science aimed at non-destructive methods allowing large-scale screening of genotypes.
Agriculture is still a major occupation in rural areas of India. Increase in technology creates many opportunities in different fields and attracts human resources from rural areas. Farmers are facing sever human and natural resource problems. Monitoring crops with low man power is the major problem. Smart crop monitoring and automation irrigation system deals with these problems by developing a mobile application which helps farmer to get detailed information about plant diseases and to use irrigation system efficiently. This model uses image processing techniques to identify the picture of the leaf and also provides information about temperature and moisture on field. The Raspberry Pi is the project's control unit, which controls and executes the entire system's operation. Pi camera is placed at the face of the moving vehicle to take the pictures of the leaves. These pictures are analyzed using convolution neural network which is an efficient machine learning algorithm. If the captured leaf image has a disease that is already in the given dataset then farmer will get output message which contains disease cause and pesticide or fertilizers we need to provide to eradicate the disease. Mobile application also sends the data which is sensed using sensors.
Why it matches plant phenotyping methods葉画像をCNNで解析して植物の病害状態を推定する画像ベースのフェノタイピング機能が、監視・自動灌漑システムの主要な技術要素として記述されているため。
abstractThis model uses image processing techniques to identify the picture of the leaf
Demand for food and other agricultural products is projected to increase by 50% globally by 2050. This increasing food demand places growing pressure on agricultural production at a time where there is little scope to expand agricultural lands. In addition, other constraining factors, such as climatic and soil constraints negatively impact agricultural productivity at global scale. To help ensure future food security, we must improve soil-crop management practices to achieve optimum productivity. One major area where productivity could be improved is for crops grown on dispersive sodic soils, which currently affect over 581 million ha worldwide. One strategy is to identify crops and/or cultivars that are more stress tolerant and productive on sodic soil and can improve agricultural productivity. However, limited work has been done to identify tolerant varieties and there is a pressing need for innovative and improved solutions to identify stress tolerance. In recent years, unmanned aerial vehicle (UAV)-based thermal imaging has been widely used in precision agriculture for detecting crop diseases and stress, and this technique has significant potential to also assess crop performance on sodic soils. In this review, we present the current understanding of how thermal imaging techniques can provide a viable technological solution to monitor crop temperature and quantify abiotic stress, which is one of the major causes for yield loss of the major rain-fed field crops, particularly wheat. To the best of our knowledge, no such detailed review has been published. Being able to identify plant stress is a primary requirement to identify more or less tolerant species or cultivars grown on sodic soil under rain-fed conditions. The aim of this review is to provide a clear and concise summary of the impacts of abiotic stress on crops and the use of UAV-thermal imaging, including sensors and calibration, data processing, advantages, and limitations in crop phenotyping, and particularly quantifying abiotic stress to help sustain productivity in sodic soil environments.
Why it matches plant phenotyping methodsUAV熱画像による作物温度・非生物的ストレスの定量化を、センサー、校正、データ処理、作物フェノタイピングの観点から体系的にレビューしており、フェノタイピング手法が中心である。
abstractThe aim of this review is to provide a clear and concise summary of the impacts of abiotic stress on crops and the use of UAV-thermal imaging, including sensors and calibration, data processing, advantages, and limitations in crop phenotyping, and particularly quantifying abiotic stress to help sustain productivity in sodic soil environments.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Abstract The combination of poorly drained soils and high rainfall can cause transient waterlogging and reduce yield of lentil. We screened 111 lentil lines for response to waterlogging in 2019 and 2020 using a pot assay outdoors. At 484 °Cd after emergence (38 d) in 2019 and 452 °Cd after emergence (42 d) in 2020, plants were waterlogged for 184 °Cd (11 d, 2019) and 167 °Cd (14 d, 2020) and allowed to recover for 323 °Cd (20 d, 2019) and 307 °Cd (26 d, 2020). We combined 2‐D digital images of canopy cover and plant height to derive a 3‐D trait that correlates with biomass to derive plant growth rate. Actual biomass at the end of recovery in the waterlogged plants varied 2.6‐fold with genotype, and genotypic and phenotypic correlations showed associations with plant growth rate both during (rg = .92; rp = .74) and after waterlogging (rg = .75; rp = .72); there was no trade‐off between maintenance of growth during waterlogging and growth during recovery. The ratio of biomass between waterlogged and control plants at the end of recovery was associated with growth rate during recovery (rg = .88, rp = .65) and biomass at the end of waterlogging (rg = .61, rp = .67). Broad‐sense heritability was 0.27 for growth rate during waterlogging, 0.37 for growth rate during recovery, 0.51 for biomass at the end of waterlogging and 0.47 for biomass at the end of recovery. High biomass at the end of recovery correlated with cooler canopies but correlations varied with season and measurement date, and heritability of canopy temperature was low. We identified genotypes with consistently higher tolerance to waterlogging and provide an improved understanding of the physiological response of lentil to hypoxia highlighting the importance of growth rate not only during waterlogging but also during recovery.
Why it matches plant phenotyping methods2-D画像から3-D形質と成長速度を推定する高スループット表現型解析を、水logging耐性スクリーニングに中心的に適用しているため。
titleHigh‐throughput phenotyping of plant growth rate to screen for waterlogging tolerance in lentil
High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun‐induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high‐temperature experiment, Temperature Free‐Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high‐temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high‐temperature stress (partial correlation r = 0.60 and −0.23). Near‐infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF) signals. ΦF further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦF outperformed SIF yield in responding to physiological stress (r = −0.37). Our findings highlight that ΦF sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF, if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.
Why it matches plant phenotyping methodsSIFおよび生理的SIF収量を用いて、圃場のダイズ個体群における高温ストレスと光合成生理を推定・評価しており、センサー由来の植物生理表現型の取得・解析が中心的である。
abstractSun‐induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes.
Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to PSII if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions of wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.
Why it matches plant phenotyping methods葉のPSII臨界温度を高スループットに測定する画像蛍光法を開発・検証し、測定条件の影響と推奨設定まで評価しており、表現型取得法が中心である。
abstractWe developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system.
To help predict the effects of global warming on plants, previous studies have investigated how the distributions and physiological performances of plants relate to environmental temperatures. This approach implicitly assumes that leaf temperatures are tightly linked to regional air temperatures. However, the thermoregulatory behaviors and physical properties of leaves can differ greatly between species, leading to different plants having different leaf temperatures even when they occur under similar conditions. It is important to understand this variation in leaf thermoregulatory traits and temperatures in order to predict how individual species will be impacted by global warming. We measured the thermal properties of leaves from >50 tropical and subtropical tree species grown at the Gifford Arboretum (Florida, USA). For each species, we measured maximum leaf temperature and rate of leaf warming using a new standardized protocol to control for both environmental variation and select plant thermoregulatory behaviors. We tested the relationships between the two thermal variables and several leaf functional traits. Even under laboratory‐controlled conditions, leaf temperatures varied by over 8.5°C among species. Maximum leaf temperature was positively correlated with leaf area, and rate of leaf warming was negatively correlated with water content per leaf area. This suggests that some species may be able to offset rising air temperatures through acclimation or adaptation of these leaf properties. Next, we tested the relationships between the species’ leaf thermal properties and their climatic niches/distributions, but we found no significant associations. These results call into question the use of regional air temperatures to model plant physiological and demographic performance. Abstract in Spanish is available with online material.
Why it matches plant phenotyping methods葉温と昇温速度という植物の生理形質を取得する新しい標準化プロトコルを用い、環境変動と熱調節行動を制御した測定法が研究の主要な技術的要素となっているため。
abstractFor each species, we measured maximum leaf temperature and rate of leaf warming using a new standardized protocol to control for both environmental variation and select plant thermoregulatory behaviors.
A non-destructive thermal imaging method was used to study the stomatal response of salt-treated Arabidopsis thaliana plants to excessive light. The plants were exposed to different levels of salt concentrations (0, 75, 150, and 220 mM NaCl). Time-dependent thermograms showed the changes in the temperature distribution over the lamina and provided new insights into the acute light-induced temporary response of Arabidopsis under short-term salinity. The initial response of plants, which was associated with stomatal aperture, revealed an exponential growth in temperature kinetics. Using a single-exponential function, we estimated the time constants of thermal courses of plants exposed to acute high light. The saline-induced impairment in stomatal movement caused the reduced stomatal conductance and transpiration rate. Limited transpiration of NaCl-treated plants resulted in an increased rosette temperature and decreased thermal time constants as compared to the controls. The net CO 2 assimilation rate decreased for plants exposed to 220 mM NaCl; in the case of 75 mM NaCl treatment, an increase was observed. A significant decline in the maximal quantum yield of photosystem II under excessive light was noticeable for the control and NaCl-treated plants. This study provides evidence that thermal imaging as a highly sensitive technique may be useful for analyzing the stomatal aperture and movement under dynamic environmental conditions.
Why it matches plant phenotyping methods熱画像を用いて葉温・温度 kinetics から気孔開閉や蒸散応答を定量化することが研究の中心であり、塩・高光条件下の生理フェノタイプ取得法として実質的に適用・評価している。
abstractA non-destructive thermal imaging method was used to study the stomatal response of salt-treated Arabidopsis thaliana plants to excessive light.
Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile, field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, and vegetation indices and is able to take high quality photos of crops. The system includes. developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.
Why it matches plant phenotyping methods小麦の草冠高さ・温度・植生指数・画像を自動取得する移動式ハイスループット表現型解析プラットフォームの開発が中心である。
titleDevelopment of a Mobile Platform for Field-Based High-Throughput Wheat Phenotyping
Abstract Deficit irrigation (DI) strategies and soil cover are highly effective to improve the the water productivity in semi-arid regions. However, the effective monitoring of plant water status under DI strategies becomes crucial. The main objective of this study was to evaluate the use of thermal images to estimate the water status of melon plants cultivated in soil with and without mulching under different irrigation regimes. The experience was carried out from October to December 2018. The study was carried out in a randomized block design, in a split plot arrangement. Plots were composed by soil cover (with and without mulching with plant material), and subplots by 5 irrigation regimes (120, 100, 80, 60 and 40% of crop evapotranspiration-ETc), with five replicates. The following variables were evaluated: canopy temperature (T canopy ), leaf water potential (Ψ leaf ), air temperature (T air ), soil moisture, crop yield and the thermal index (ΔT), this being defined as the difference between T canopy and T air . ΔT showed high correlations with crop yield and crop water consumption, evidencing that thermography is an efficient tool to identify the water status of melon plants and could be employed for a proper irrigation scheduling under the tropical semi-arid scenarios. Moreover, the use of thermal images also allowed the identification of beneficial effects of soil cover on leaf water status and crop yield, mainly under moderate DI. The obtained results also demonstrate that mulching is essential to increase melon yield and water productivity in tropical regions.
Why it matches plant phenotyping methods熱画像でメロンの水分状態を推定する方法を中心に評価し、熱指標と収量・水分状態の相関を検証しているため、植物フェノタイピング手法の実質的応用・検証に該当する。
abstractThe main objective of this study was to evaluate the use of thermal images to estimate the water status of melon plants cultivated in soil with and without mulching under different irrigation regimes.
Increasing global water deficit and demand for yield improvement call for high-resolution monitoring of irrigation, crop water stress, and crops' general condition. To provide high spatial resolution with high-temperature accuracy, remote sensing is conducted at low altitudes using radiometric longwave thermal infrared cameras. However, the radiometric cameras' price, and the low altitude leading to low coverage in a given time, limit the use of radiometric aerial surveys for agricultural needs. This paper presents progress toward solving both limitations using algorithmic and computational imaging methods: stabilizing the readout of low-cost thermal cameras to obtain radiometric data, and improving the latter's low resolution by applying convolutional neural network-based super-resolution. The two methods were merged by an end-to-end algorithm pipeline, providing a large mosaicked image of the field. First, the potential capabilities of a joint estimation method to correct unknown offset and gain were simulated on remotely sensed agricultural data. Comparison to ground-truth measurements showed radiometric accuracy with a root mean square error (RMSE) of 1.3 °C to 1.8 °C. Then, the proposed super-resolution method was demonstrated on experimental and simulated remotely sensed agricultural data. Preliminary experimental results showed 50% improvement in image sharpness relative to bicubic interpolation. The performance of the algorithm was evaluated on 22 simulated cases at × 2 and × 4 magnification. Finally, image mosaicking using the proposed pipeline was demonstrated. A mosaicked image composed of sub-images pre-processed by the proposed computational methods resulted in a RMSE in temperature of 0.8 °C, as compared to 8.2 °C without the initial processing.
Why it matches plant phenotyping methods低コスト熱赤外カメラの放射計補正、超解像、モザイク化を統合し、農地の温度・水ストレス等の植物状態を推定する計算センシング手法が研究の中心であり、精度評価も実施している。
abstractThis paper presents progress toward solving both limitations using algorithmic and computational imaging methods: stabilizing the readout of low-cost thermal cameras to obtain radiometric data, and improving the latter's low resolution by applying convolutional neural network-based super-resolution.
With the increasing global water scarcity, efficient assessment methods for crop water stress have become a prerequisite to perform precision irrigation scheduling. The 1accessibility of infrared thermal sensor provides a powerful tool to detect and quantify crop water stress. This paper reviews the current practices of infrared thermal imagery utilized to assess crop water stress. Overall, three technological aspects of infrared thermal sensing applications for crop water stress assessment are reviewed along with the challenges and recommendations: (i) introduction of uncooled thermal camera and platforms, including ground-based platform and unmanned aerial vehicles (UAVs) platforms, for thermal imaging acquisition, (ii) strategies of canopy segmentation in thermal imaging used to obtain average canopy temperature for CWSI calculation, (iii) correlation between three forms of crop water stress index (CWSI) i.e. theoretical CWSI (CWSIt), empirical CWSI (CWSIe), and statistic CWSI (CWSIs) and physiological indicators. The emphasis is on imaging process techniques for canopy segmentation in thermal imaging. As a future perspective, the potential use of deep learning approaches to assess crop water stress has been elaborated highlighting the future trends.
Why it matches plant phenotyping methods作物の水ストレスという植物状態を対象に、熱画像取得、キャノピー分割、CWSI算出を中心的にレビューしており、植物フェノタイピング手法レビューに該当する。
abstractThis paper reviews the current practices of infrared thermal imagery utilized to assess crop water stress.
ThermalFlowerCalibration / preprocessingPlant / canopy temperature
Background Floral temperature has important consequences for plant biology, and accurate temperature measurements are therefore important to plant research. Thermography, also referred to as thermal imaging, is beginning to be used more frequently to measure and visualize floral temperature. Accurate thermographic measurements require information about the object's emissivity (its capacity to emit thermal radiation with temperature), to obtain accurate temperature readings. However, there are currently no published estimates of floral emissivity available. This is most likely to be due to flowers being unsuitable for the most common protocols for emissivity estimation. Instead, researchers have used emissivity estimates collected on vegetative plant tissue when conducting floral thermography, assuming these tissues to have the same emissivity. As floral tissue differs from vegetative tissue, it is unclear how appropriate and accurate these vegetative tissue emissivity estimates are when they are applied to floral tissue. Results We collect floral emissivity estimates using two protocols, using a thermocouple and a water bath, providing a guide for making estimates of floral emissivity that can be carried out without needing specialist equipment (apart from the thermal camera). Both protocols involve measuring the thermal infrared radiation from flowers of a known temperature, providing the required information for emissivity estimation. Floral temperature is known within these protocols using either a thermocouple, or by heating the flowers within a water bath. Emissivity estimates indicate floral emissivity is high, near 1, at least across petals. While the two protocols generally indicated the same trends, the water bath protocol gave more realistic and less variable estimates. While some variation with flower species and location on the flower is observed in emissivity estimates, these are generally small or can be explained as resulting from artefacts of these protocols, relating to thermocouple or water surface contact quality. Conclusions Floral emissivity appears to be high, and seems quite consistent across most flowers and between species, at least across petals. A value near 1, for example 0.98, is recommended for accurate thermographic measurements of floral temperature. This suggests that the similarly high values based on vegetation emissivity estimates used by previous researchers were appropriate.
Why it matches plant phenotyping methods花の熱画像による温度測定を正確化するため、花部の放射率推定プロトコルを開発・比較・検証しており、植物表現型取得法が研究の中心である。
abstractWe collect floral emissivity estimates using two protocols, using a thermocouple and a water bath, providing a guide for making estimates of floral emissivity that can be carried out without needing specialist equipment (apart from the thermal camera).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.
Why it matches plant phenotyping methodsブドウ樹のウイルス感染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方法が研究の中心であり、画像前処理、特徴抽出、分類器比較、深層学習による技術評価を含む。
abstractHyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Substantial progress has been made in developing sensor‐based proximal phenotyping systems for cotton ( Gossypium hirsutum L.), but research is needed to improve in‐season prediction of lint yield and to improve accuracy in monitoring crop water stress using such a system. Here, we report on results of a 2‐yr field study in which a proximal remote sensing system (measuring canopy height, spectral indices [normalized difference vegetation index, NDVI], and canopy temperature) was deployed every 2 wk over plots of eight cotton varieties at three rates of ET replacement (0, 45, and 90%). As expected, NDVI was an excellent predictor of canopy and biomass traits, including canopy height and leaf area index (LAI). The strength of correlations between in‐season sensor measurements (NDVI and the canopy‐to‐air temperature difference [ T c – T a ]) and lint yield ranged from poor to fair when analyzed by irrigation rate and excellent when analyzed across all rates. Correlations were weaker in the drier of the two years tested and NDVI was a better and more consistent predictor of yield than T c − T a , though multiple linear regression integrating both variables improved results by up to 9%. Combining the T c − T a data with onsite atmospheric weather station data allowed calculation of empirical crop water stress index (CWSI) values. Using the CWSI metric, relative differences in crop water stress were clear among ET replacement levels once the canopy width reached the threshold for focusing the infrared temperature sensor on the canopy. This indicated that cotton water stress can be successfully monitored using a proximal phenotyping system, which can be quickly and easily deployed across many plots in research or production settings.
Why it matches plant phenotyping methods綿花の近接リモートセンシング系を用いて、キャノピー形態・スペクトル・温度から生育、収量、水ストレスを測定・予測し、センサー性能と指標の有効性を評価しているため、フェノタイピング手法が中心である。
abstractThis indicated that cotton water stress can be successfully monitored using a proximal phenotyping system, which can be quickly and easily deployed across many plots in research or production settings.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像からダイズの洪水障害スコアを推定する画像特徴抽出と深層学習分類法が研究の中心であり、育種圃場で技術評価も行っている。
abstractThe goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights.
Crop monitoring is a very important area of precision agriculture and smart farming. Through an accurate monitoring, it is possible to more efficiently manage the irrigation, fertilization, and pest control. In this study, we propose aerial thermal image calibration method and thermal image processing techniques to analyze the water stress level of fruit trees under different irrigation conditions. The calibration was performed using Gaussian process regression, and it was demonstrated as an appropriate regression method because it satisfied all requirements including the residuals’ normality, independence, and homoscedasticity. In addition, an appropriate image processing technique was necessary to selectively extract only the canopy temperature from the aerial thermal images, while excluding irrelevant elements such as the soil and other objects. For the image processing techniques, three methods (Gaussian mixture model, Otsu binarization algorithm, and Otsu binarization algorithm after Gaussian blurring) were employed. The Gaussian mixture model provided the highest accuracy and stable results for the extraction of the canopy temperature. After the aerial thermal images were subjected to calibration and image processing, the degree above nonstressed canopy (DANS) water stress index was calculated for the fruit trees under different water supply conditions. The distribution of the DANS water stress index was similar to the distribution of the canopy temperature and inversely proportional to the amount of supplied water content. Therefore, we expect that the DANS water stress index, calculated using the calibration and image processing techniques proposed in this study, can be a reliable measure for the estimation of the water stress of crops for the application of aerial infrared techniques to remote sensing.
Why it matches plant phenotyping methodsUAV熱画像の校正、樹冠抽出、画像処理を開発・比較し、果樹の水ストレス状態を推定する手法として検証しており、植物表現型取得が研究の中心である。
abstractwe propose aerial thermal image calibration method and thermal image processing techniques to analyze the water stress level of fruit trees
Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Why it matches plant phenotyping methodsマルチセンサーUASによる植物群落の画像・反射率・熱情報取得と、DSM・DTM・キャノピー高モデルなどの再利用可能な処理データを提供しており、植物状態の計測基盤およびデータセットが中心です。
abstractThe Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm
Field / plotThermalLeafStress / disease detectionStress response / tolerancePlant / canopy temperature
In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.
Why it matches plant phenotyping methods植物の熱画像計測を用いたストレス検出・フェノタイピング手法のレビューであり、測定補正や圃場実装上の技術課題も中心的に扱っている。
abstractThis review focuses on the state of the art of thermography applied to the detection of biotic stress.
Field / plotThermalWhole plant / canopy / plot / fieldCalibration / preprocessingPlant / canopy temperature
Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model ( R 2 = 0.78; p -value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.
Why it matches plant phenotyping methodsUAV熱画像の校正手法を開発・検証し、作物キャノピー温度という植物状態の推定精度を大幅に改善しているため、フェノタイピング手法が中心です。
abstractThe primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references.
AppleField / plotThermalFlowerPhysiological trait estimationPlant / canopy temperature
In many areas, fire blight (Erwinia amylovora) is a sporadic but potentially devastating disease of apples. Infections occur primarily during bloom when warm weather conducive to bacteria multiplication on the stigma of contaminated flowers is followed by a wetting event, facilitating plant entry. Fire blight prediction models which rely on air temperature for disease forecast can help, but currently produce many false positive and some false negative prognoses. The differences between air and apple flower stigma temperature can explain some of the issues. The present study undertakes an introductory step in resolving this matter by being the first of its kind to document apple stigma temperatures and its departure from air temperature. Thermocouples continuously monitored flower temperature for the blooming seasons of 2018 and 2019 in the orchard of Saint-Bruno-de-Montarville, Québec, Canada, while a thermal imager measured the temperature of randomly selected flowers in 2019. Flower stigma temperature measured with thermocouples followed the diurnal pattern of air temperature, but stigma temperature was higher/lower than air with maxima/minima at peak hours of the day/night. Temperatures measured with the thermal imager revealed a mean positive difference with the air temperature during the day (1.6 ± 1.3 °C). Stigma to air differences for both instruments had a strong positive relation with solar radiation during daytime. Under high humidity, this difference was significantly reduced. From these findings, regression models for estimating stigma temperature were developed for fire blight forecasting. When validating with thermal imaging data, the best model utilizes air temperature, radiation and relative humidity to estimate stigma temperature with better results (RMSE = 1.04 °C) than air temperature alone (RMSE = 2.05 °C). Although the application of these findings for fire blight prediction models was not tested, there is evidence that models that solely rely on air temperature are at risk of errors.
Why it matches plant phenotyping methodsリンゴ花柱頭温度という植物器官の生理状態を熱電対・熱画像で測定し、推定回帰モデルを開発・検証している。植物フェノタイピング手法とその技術性能が中心である。
abstractThermocouples continuously monitored flower temperature for the blooming seasons of 2018 and 2019
This study is the first and unique of open-field studies to assess the determination process of heat-induced spikelet sterility (HISS) of rice by using the same variety and the same monitoring system, MINCER (Micrometeorological Instrument for Near Canopy Environment of Rice), covering the major-rice growing regions from Sub-Saharan Africa, South, Southeast, and East Asia, and USA. Applying the observation data from the monitoring network, MINCERnet, to the canopy heat balance model, IM 2 PACT, it was quantitatively corroborated in open-field conditions worldwide that the canopy and panicle transpiration and their evaporative cooling effect played a great role on the micrometeorological gap between the ambient air temperature and the panicle temperature, and that the sterility rate due to HISS in open-fields can be evaluated accurately in diverse climates by the mean panicle temperature at flowering hours in the flowering period. The heat balance structure suggested that the risk of HISS should be higher in high humidity climates rather than in dry climates also in the future, which lead to the importance of the humidity accuracy as well as of the air temperature in climate scenarios and their spatial downscaling for future prediction of rice heat stress and production. Applying the heat-tolerant variety was suggested to be able to keep the sterility due to HISS low in all climates. It is needed of the approach using the panicle temperature as indicator of HISS by intervening sub-model and/or monitoring of micrometeorology inside the canopy to reduce uncertainties in future rice yield prediction under various adaptation measures.
Why it matches plant phenotyping methodsMINCERによるイネ群落内微気象・穂温の測定と熱収支モデルを用いた穂温および高温不稔率の評価が研究の中心であり、広域野外条件で技術的に検証・適用している。
abstractassess the determination process of heat-induced spikelet sterility (HISS) of rice by using the same variety and the same monitoring system, MINCER (Micrometeorological Instrument for Near Canopy Environment of Rice)
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Efficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named “Green-gradient based canopy Segmentation Model (GSM)” was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014–15 and 2015–16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, the mean red and the mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurate prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjection to water and cold stresses, date of imaging, and time of irrigation. The introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.
Why it matches plant phenotyping methodsRGB画像から植生画素を分類し、キャノピー被覆率・NDVI・温度などの植物形質を推定する画像解析モデルを開発しており、フェノタイピング手法が研究の中心である。
abstracta simple image mining technique named “Green-gradient based canopy Segmentation Model (GSM)” was developed
Sudden death syndrome (SDS), a fungal infection in soybean caused by Fusarium virguliforme, greatly affects the plant health and in some cases, can cause yield losses of more than 70%. Infected plants are scored by visual assessment based on severity and extent of infection. This manual process is time intensive and not practical for large acreages. Diseased and stress in plants show elevated canopy temperatures that can potentially lead to identification of unhealthy plants without manual scoring. The infection decreases nutrient distribution causing stress that results in internal plant temperature to increase. Thermal infrared (TIR) sensors have the ability to measure the emitted radiation of an object in the infrared region of the electromagnetic spectrum to estimate canopy temperatures. However, TIR sensors have not yet been utilized to capture changes in canopy temperatures to detect SDS in soybean. Therefore, the goal of this study was to (1) use a TIR sensor to assess plant health and vitality, and (2) evaluate canopy temperatures over the growing season to quantify disease development. A thermal infrared camera was mounted on a small unmanned aerial system to capture aerial imagery over the growing season. The first flight was achieved once SDS foliar symptoms began initial development. The remaining three flights occurred before, during, and after full pod fill when symptoms had reached their apex. Results show increasing correlations over the four days. Elevated canopy temperature changes were observed on canopies at early SDS symptom development. Symptoms at the end of the growing season displayed strong correlations to the canopy temperature with ρ = 0.7404. Disease severity showed the strongest correlation throughout the four flights with the last at ρ = 0.7245. The four flights exhibit a decreasing trend with Spearman's rho (R² = 0.86 for disease severity). Therefore, thermal imaging can be utilized to detect diseased plots. Future studies will be conducted to understand how to mitigate for SDS using thermal detection.
Why it matches plant phenotyping methodsTIRセンサー搭載UASによるキャノピー温度測定を用いて、ダイズのSDS症状・病害重症度を非接触で推定する手法が研究の中心であり、相関による技術評価も行っている。
abstractTherefore, the goal of this study was to (1) use a TIR sensor to assess plant health and vitality, and (2) evaluate canopy temperatures over the growing season to quantify disease development.
Tropical rice production is at risk from rising temperature. Understanding regional and seasonal heterogeneity of optimum temperatures for rice production is important for model simulation to predict rice yield change under climate change. However, studies or tools for widely observation of crop responses to temperature over broad spatial scales with long time spans are limited. In this study, we detected optimum temperature range for rice gross primary production (ToptGPP) in the lower Gangetic plains and delta region using the near-infrared reflectance of vegetation (NIRV), which is a new photosynthetic proxy, to improve ORYZA model performance in high-temperature season and assessed how tropical rice would respond to temperature increase in the study area. According to satellite observations of NIRV from 2001 to 2015, current ambient air temperature has exceeded the mean ToptGPP of Boro rice (24.8 ± 1.8 °C) and Aman rice (26.7 ± 1.2 °C) in the lower Gangetic plains and delta region, suggesting a downtrend of rice production under future warming. The detection results show that rice has lower ToptGPP in the regions with more drought stress and lower background temperature under water-limited conditions. Furthermore, the model modified by NIRv-ToptGPP shows better performance in potential yields, especially in high-temperature seasons on the region scale. Without CO₂ fertilization effect, each degree-Celsius increase is expected to reduce rice potential yields by 4.9 ± 1.6% based on the default ToptGPP range in ORYZA model and by 7.0 ± 1.2% based on the detected NIRv-ToptGPP range in the study area. This study implies that global grid-based model simulation may underestimate sensitivity of tropical rice yield to temperature rise due to the neglect of regional and seasonal heterogeneity of ToptGPP. NIRV makes it possible to determine local optimal temperatures for crop production, and to improve grid-based modelling across various agricultural systems in different growing seasons at the regional scale.
Why it matches plant phenotyping methods衛星NIRvを用いてイネの光合成生産性に関する最適温度（ToptGPP）を抽出・検証し、作物モデルを改善する測定・推定ワークフローが中心であり、単なる収量測定ではない。
abstractIn this study, we detected optimum temperature range for rice gross primary production (ToptGPP) in the lower Gangetic plains and delta region using the near-infrared reflectance of vegetation (NIRV), which is a new photosynthetic proxy, to improve ORYZA model performance
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
Why it matches plant phenotyping methodsUAS画像から植物形質を抽出する画像処理手法を導入し、オルソモザイク画像との比較で精度・遺伝率を検証しており、フェノタイピング手法が中心である。
abstractwe introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images.
Reproduction assets foundThe paper publicly deposits its plot-level orthomosaic and orthorectified images (the phenotyping inputs/outputs of this study) at a KSU repository. The authors' Python analysis code (bip, traitExtraction) is mentioned via GitHub footnotes, but those URLs are not in the allowed list, so they cannot be included as verifDataset · publicData associated with these experiments, including the cropped, plot-level orthomosaic images and corresponding orthorectified images, can be accessed at the public repository 7 .Open asset ↗lines:528-572Supplement · publicSupplementary Table 2 ), were used to extract two independent datasets for the CT trait.Open asset ↗lines:333-343Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball-Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.
Why it matches plant phenotyping methodsC4葉のガス交換特性を推定する結合モデルを開発し、感度・信頼性・文献データ再現性・限界を検証しており、植物表現型の取得・推定手法が中心である。
abstractHere, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models.
Reproduction assets foundThe authors explicitly state that a Jupyter notebook containing the model source code, calibration datasets (maize gas-exchange/SPAD measurements), and figure-generation scripts is publicly available on GitHub. The authors' Julia modeling framework (Cropbox.jl) used for the analysis is also publicly available.Code · publicA Jupyter notebook containing source code of the model with calibration datasets and scripts for producing figures presented in this paper is available at https://github.com/cropbox/plants2020 .Open asset ↗cropbox/plants2020lines:500-598Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Canopy thermal infrared imaging reflects the temperature change in the crop canopy, which is closely related to the stomatal conductance and water utilization characteristics of the crop. Thermal infrared imaging is an effective and nondestructive way to study the early detection of crop diseases. However, efficiently extracting the thermal infrared canopy region of the crops is an important factor restricting the study of canopy temperature changes. The grayscale distribution of the edges of the crop canopy thermal infrared image is uneven with strong noise and cannot be extracted effectively using the traditional image segmentation method. Thus, a recognition method for the thermal infrared images of plant canopies based on heterogeneous image characteristic registration was proposed to overcome the shortcomings above. First, the Gauss membership function was selected to construct the network recognition rules on the basis of the three-layer backward reasoning of the adaptive BP neural network to recognize the visible light reference images of the plant canopies. Second, the optimal registration parameters of the affine transformation were calculated for registering the canopy region of the reference image and that of the initial thermal infrared image. Third, a recognition model for the thermal infrared images of plant canopies was established based on bilinear mapping factors. Finally, information entropy and mutual information were used to evaluate the effectiveness of the recognition model. The results showed that the initial temperature range of the original thermal infrared image was 20.46–36.40 °C. After removing the background interference of the thermal infrared canopy of the crop, the temperature range of the target image was 20.46–26.65 °C, and the average temperature after extraction was 2.04 °C lower than that before extraction. In addition, the entropy difference between the canopy of the thermal infrared image identified by the proposed model in this study and the standard recognition method was within the range of 0.01–0.06, indicating the effectiveness of the recognition model for the plant canopy. Therefore, this study provided an efficient method for obtaining the canopy temperature characteristics of crops.
Why it matches plant phenotyping methods植物キャノピーの熱赤外画像から背景を除去し、キャノピー温度特性を抽出する認識・登録手法の開発が中心であり、植物表現型の取得方法に該当する。
abstractThus, a recognition method for the thermal infrared images of plant canopies based on heterogeneous image characteristic registration was proposed to overcome the shortcomings above.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
The present study investigated genotypic differences in canopy temperature of 218 testcross progenies of a recombinant inbred lines mapping population of winter rye. For this purpose, we repeatedly measured canopy temperature (Tc) and air temperature (Tₐ) differences (Tc − Tₐ) in field trials on two locations with sandy soils in northern Germany, two water treatments (rainfed and irrigated) in two experimental years. The experimental conditions therefore covered a wide range of drought stress conditions between stem elongation and ripening under a high variation of environmental boundary conditions. Genotype effects on (Tc − Tₐ) were significant in both water treatments and were correlated to grain yield reduction under drought stress. With the chosen statistical model, we were able to account for moderate time-series effects and short-term fluctuations caused by changing boundary condition during single screens of (Tc − Tₐ). We could show that under certain weather conditions (Tc − Tₐ) can detect differences in drought stress tolerance more accurate than grain yield determination of rye.
Why it matches plant phenotyping methodsキャノピー温度を用いた干ばつ耐性の高スループット圃場フェノタイピング手法を評価し、統計モデルと穀粒収量との比較によって技術性能を検証しているため。
titleIs canopy temperature suitable for high throughput field phenotyping of drought resistance of winter rye in temperate climate?
There is growing interest in the use of canopy temperature to evaluate the water status of crops for irrigation water management. One of the main indicators currently used is the Crop Water Stress Index (CWSI). In this index, the canopy temperature is normalized by the environmental conditions to account for the evaporative demand of the atmosphere. The theoretical framework, based on the Penman-Monteith equation, defined the CWSI as one minus the ratio between actual and potential water use (Jackson et al., 1981). For the first time in tree crops, we have related the actual transpiration of almond trees measured with heat-pulse sap flow probes with the CWSI, calculated using an empirical Non-Water Stress Baseline. The relationship obtained between the CWSI and the relative transpiration fitted the theoretical relationship, although it showed a large scatter (R²=0.69; RMSE=0.13). The variability in micrometeorological conditions among different parts of the canopy, the scatter of the NWSB, or inherent measurement errors are identified as probable causes of this scatter. Finally, the effect of this scatter on the accurate assessment of actual transpiration from canopy temperature is analyzed for practical irrigation management purposes. We found an error of about 10% in the relative transpiration, which seems acceptable for irrigation management applications.
Why it matches plant phenotyping methodsアーモンド樹の樹冠温度からCWSIを算出し、実測蒸散量との関係と誤差を検証しており、植物の水分状態・生理形質の測定法が中心です。
abstractFor the first time in tree crops, we have related the actual transpiration of almond trees measured with heat-pulse sap flow probes with the CWSI, calculated using an empirical Non-Water Stress Baseline.
Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to photosystem II (PSII) if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions: wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.
Why it matches plant phenotyping methods葉の熱耐性・PSII機能の臨界温度を高スループットに測定する蛍光イメージング手法を開発・検証しており、表現型取得法が研究の中心である。
abstractWe developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system.
Reproduction assets foundThe paper provides authors' public R code and example files for extracting Tcrit values from T-F0 chlorophyll fluorescence curves, hosted on the authors' GitHub repository. The paper also states phenotype data are openly available in figshare (10.6084/m9.figshare.12545093), but no figshare URL is present in the allowedCode · publican leaf temperature estimated from
227 two thermocouples attached to leaves on the plate and relative F0 values using the segmented R
228 package (Muggeo 2017) using the R Environment for Statistical Computing (R Core Team
229 2020). We provide example files and example R code for extracting Tcrit values from T-F0
230 curves at https://github.com/pieterarnold/Tcrit-extraction.
231
232 Surface wetness experiment: effect of wet vs dry surfaces for leaves on CTMIN and CTMAX
233 Most experiments that measure T-F0 have measured leaf samples with all excess surface
234 moisture removed, on a dry surface. However, maintaining water content of detached leaves by
235 providing a wet surface where leaOpen asset ↗pieterarnold/Tcrit-extractionpdf-layout-page:8 lines:1-46Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.
Why it matches plant phenotyping methods植物のキャノピー温度、fPAR、植生指数などの形質・生理状態を取得するタワー型ハイパースペクトル／熱赤外センシングシステムを開発し、相関検証しているため、フェノタイピング手法が中心である。
abstractwe developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower.
In precision agriculture, thermal remote sensing is considered a promising tool that estimates the surface temperature of vegetation and uses satellite thermal imaging cameras or thermal cameras on unmanned aerial vehicles. (Research purpose) The research purpose is in reviewing of thermal cameras for unmanned aerial vehicles and the use of a heat channel (LWIR) to study the state of crops when monitoring using unmanned aerial vehicles. (Materials and methods) We used scientific literature, materials of domestic and foreign authors, and websites of manufacturers of thermal imaging cameras for unmanned aerial vehicles. (Results and discussion) A modern drone with a thermal imaging camera serves as a platform solution for monitoring agricultural fields. Thermal infrared sensors capture information about the temperature of objects warmer than absolute zero (-273 degrees Celsius/-459 degrees Fahrenheit) at certain wavelengths (the LWIR and MWIR ranges) in an amount proportional to their temperature and generate images that display this temperature. The process of collecting and processing thermal data consists of several stages and varies depending on the suspension equipment and the purpose of the survey. Foreign scientists used thermal images obtained using unmanned aerial vehicles and a thermal camera to assess the state of vegetation cover, crop yields, irrigation systems, to measure water stress, determine the maturity phase of row crops and fruit tree productivity. (Conclusions) The heat channel can be used in the formation of decisions for assessing vegetation cover, crop moisture availability, when planning irrigation systems, determining diseases and infected crops, crop readiness for harvesting and yield mapping.
Why it matches plant phenotyping methods熱画像センサーとUAVを用いた作物状態・水ストレス・成熟度・収量などの植物形質推定を主題とするレビューであり、データ取得・処理過程も扱っているため、方法論的中心性がある。
abstractThe research purpose is in reviewing of thermal cameras for unmanned aerial vehicles and the use of a heat channel (LWIR) to study the state of crops when monitoring using unmanned aerial vehicles.
Thermal stress indicators are one of the most accurate indices for sensing plant water status that can be remotely measured by the means of infrared thermography. In addition to the canopy temperature, these indices need to access the wet and dry reference temperatures which refer to the temperatures of the canopy at well-watered and fully stressed conditions, respectively. The main goal of this study is to measure the canopy as well as reference temperatures automatically by the means of a single thermal image, captured from an olive tree. The temperatures of artificial reference surfaces were extracted by the means of an object detection method based on the edge detection and morphological processes. The temperatures of sunlit and shaded canopy portions were also detected, using a Fuzzy C-means clustering of thermal images with the wet and dry reference temperatures as thresholds. The algorithm was successfully detected the references in 90% of the images and the automatic extracted canopy temperatures were significantly correlated with the manual ones.
Why it matches plant phenotyping methods熱画像から作物の樹冠温度と水ストレス指標用の基準温度を自動抽出する画像処理手法を開発・検証しており、植物状態の取得が研究の中心である。
abstractThe main goal of this study is to measure the canopy as well as reference temperatures automatically by the means of a single thermal image, captured from an olive tree.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
Abstract An infra red thermographic analysis of well watered control and well watered heat stressed pearl millet ( Pennisetum glaucum ) was conducted at ICAR – Central Research Institute for Dryland Agriculture as a part of high resolution phenomics studies to identify the individual quantitative physiological parameters by plant phenotyping that form the basis for more complex abiotic stress tolerant traits. It was seen that the temperature gradient increased gradually from ground level to the top in the control non heat stressed plant. In contrast, it was seen that in the heat stressed plant the temperature increased up to the middle of the plant and then started to decrease at the top of the plant in comparison with the non heat stressed control plant. Our results indicate that the lowering of temperatures in the top of the heat stressed plant may be a mechanism by which the heat stressed plant acclimates to stress by regulating its transpiration thereby bringing in a cooling effect to counter stress.
Why it matches plant phenotyping methods赤外線サーモグラフィーによる植物温度・温度勾配の取得を、植物フェノタイピングおよび生理形質の定量化として明示的に扱っており、手法の適用が中心です。
abstractto identify the individual quantitative physiological parameters by plant phenotyping
Canopy temperature is an important variable directly linked to a plant’s water status. Recent advances in Unmanned Aerial Vehicle (UAV) and sensor technology provides a great opportunity to obtain high-quality imagery for crop monitoring and high-throughput phenotyping (HTP) applications. In this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing. Raw thermal imagery collected over a cotton field was converted to surface temperature using the Software Development Kit (SDK) provided by the sensor company. Canopy temperature map was generated using Structure from Motion (SfM), and Thermal Stress Index (TSI) was calculated for the test site. UAV temperature measurements were compared to ground measurements acquired by net radiometers and thermocouples. Temperature differences between UAV and ground measurements were less than 5%, and UAV measurements proved to be more stable. The proposed UAV system was successful in showing temperature differences between the cotton genotype. In conclusion, the system described in this study could possibly be used to monitor crop water status in a field setting, which should prove helpful for precision agriculture and crop research.
Why it matches plant phenotyping methodsUAV搭載熱センサーによる綿花キャノピー温度の取得システムを開発し、地上測定との比較検証まで行っており、植物の水分状態に関わる形質測定法が研究の中心である。
abstractIn this study, a UAV-based thermal system was developed to directly measure canopy temperature, skipping the traditional radiometric calibration process which is time-consuming and complicates data processing.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Progress in high-throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi-automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree-level phenotypes followed by genome-wide association study (GWAS). An RGB-based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 single nucleotide polymorphisms (SNPs). Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisome metabolism. About 16-20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho-physiological traits. Despite a limited availability of markers and individuals, this study is provides a successful proof-of-concept for the combination of high-throughput UAV-based phenotyping with cost-effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer.
Why it matches plant phenotyping methodsUAV画像、点群、マルチスペクトル・熱画像を統合した樹木レベル表現型の半自動取得法が研究の中心であり、成長・生理形質を抽出しているため。
abstractWe developed a semi-automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree-level phenotypes followed by genome-wide association study (GWAS).
Precision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = −1.7 ± 1.7), and RTE the median (difference = −2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI.
Why it matches plant phenotyping methodsトウモロコシの水ストレスを対象に、衛星・UAV熱画像による温度およびCWSI推定手法を比較検証しており、植物状態の取得方法が研究の中心である。
abstractTemperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods
Heat and light-instigated abiotic stresses during summer can cause several physiological disorders in perennial specialty crops. Such stressors increase the fruit surface temperature (FST) and prolonged exposure above a critical FST can result in sunburn. Sunburn in apple may cause considerable crop loss and reduce fresh produce marketability. Existing approaches for sunburn prediction and management, based on atmospheric temperature data, are often unreliable and inefficient for timely actuation of remedial measures. Therefore, this study focuses on the development of a non-invasive and real-time sunburn monitoring tool. We developed a crop physiology sensing (CPS) unit that uses visible-infrared imagery and in-field weather data for FST monitoring, the prime indicator of sunburn susceptibility. The CPS unit consists of a thermal-red-green-blue and all-in-one weather sensor integrated with a single-board computer. Acquired imagery data was analyzed in real-time using a custom-developed algorithm in python ‘OpenCV’ library to estimate imager-based FST. The algorithm was optimized for processing the data on a single-board computer with limited computational resources. Moreover, the processing unit was configured to acquire in-field weather data and to utilize a temperature dynamics weather model for weather-based FST estimation. Two automated CPS units were deployed in the commercial orchards of cv ‘Honeycrisp’ and ‘Cosmic Crisp™’ during the 2019 production season. For each cultivar, field data was collected for three days between 12 and 5 pm at 5-minute intervals. A contact type thermal probe of accuracy ±0.4 °C was also utilized for ground truth apple FST (FSTₐ) measurements. Furthermore, imagery data was analyzed to derive mean FST (FSTᵢ), maximum FST (FSTᵢ₋ₘₐₓ), and mean FST of the 10%, 15% and 20% hottest part of the fruit surface (i.e. FST₁₀, FST₁₅, and FST₂₀, respectively). The results showed significant differences between FSTᵢ, FSTᵢ₋ₘₐₓ, FST₁₀, FST₁₅, and FST₂₀ for Honeycrisp (F₄,₁₆₂ = 73.4, p < 0.0001) as well as Cosmic Crisp (F₄,₄₆ = 19.4, p < 0.0001) cultivars. Moreover, no significant difference was recorded between FSTᵢ and FSTₐ. The weather model-predicted FST (FSTw) was found to be highly sensitive to fruit shading and a significant difference was recorded between FSTw and FSTᵢ. Overall, the developed CPS unit demonstrates a promising potential for reliable FST monitoring that could aid growers in real-time apple sunburn susceptibility prediction and actuation of sunburn preventive strategies.
Why it matches plant phenotyping methodsリンゴ果実表面温度という植物器官の生理状態を、熱・RGB画像と気象センサーでリアルタイム推定する装置および解析アルゴリズムを開発し、接触式プローブで検証しており、フェノタイピング手法が中心である。
abstractTherefore, this study focuses on the development of a non-invasive and real-time sunburn monitoring tool.
A growing number of leaf traits can be predicted from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area, nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. The function predicting Rubisco capacity normalised to 25 {degrees}C predicted the same value, regardless of leaf temperatures ranging from 20 to 35{degrees}C. Leaf temperature affected none of the predicted traits: Vcmax25, J, chlorophyll content, LMA, N content per unit leaf area or Vcmax25/N. However, as others have derived models to predict Rubisco activity that includes variation associated with leaf temperature, we discuss whether these functions may include a temperature signal within the reflectance spectra.
Why it matches plant phenotyping methodsハイパースペクトル反射から葉の生理・構造形質を推定する方法について、葉温度の影響を検証しており、表現型取得法の技術的評価が中心です。
abstractA growing number of leaf traits can be predicted from hyperspectral reflectance data.
Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.
Why it matches plant phenotyping methodsUAV熱画像による植物ストレス・病害・収量などの推定を扱い、校正・地上データ収集・データ処理を含む手法上の論点をレビューしているため、植物フェノタイピング手法レビューとして中心的です。
abstractThis paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.
This study assessed the use of reflectance indices for detecting the combined effects of water and nitrogen stress in tomatoes (Solanum lycopersicum L.). Spectral reflectance data were acquired from tomato plants, subjected to three water and three nitrogen treatments. Irrigation water was applied in amounts of 100, 70, and 30% of full replenishment of root zone soil water to field capacity. Nitrogen application was 100, 70, and 30% of crop nutrient requirement. The treatments were replicated five times in a randomised complete block design. Plant stress indicators, including leaf temperature (Tc), relative water content (RWC), yield, and leaf chlorophyll content (LCC) were measured at the same time of leaf reflectance data, during the growing season. Reflectance indices including Normalised Difference Vegetation Index (NDVI), Renormalised Difference Vegetation Index (RDVI), Optimised Soil Adjusted Vegetation Index (OSAVI), Photochemical Reflectance Index centered at 550 nm (PRI₅₅₀), normalised PRI (PRIₙₒᵣₘ), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Water Index (WI), and WI/NDVI were obtained from the reflectance data. The results showed that the PRI₅₅₀, PRInorm, and WI were the most sensitive indices for distinguishing crop water stress, while RDVI, PRIₙₒᵣₘ, and TCARI had the best correlation with nitrogen stress indicators. PRIₙₒᵣₘ was the most sensitive index for detecting the combined effect of water and nitrogen stress. This study provided more insights into the usefulness of leaf spectral features for assessing crop abiotic stress. Measuring these indices with hyperspectral sensors provides a rapid, non-destructive, and reliable approach for estimating crop stress.
Why it matches plant phenotyping methodsトマトの水・窒素ストレスをスペクトル反射指数から推定・識別する手法を評価し、複数指標の感度とストレス指標との相関を検証しており、フェノタイピング手法が中心です。
titleNarrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field grown tomato crops
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Crop productivity can be expressed as the product of the amount of radiation intercepted, radiation use efficiency and harvest index. Genetic variation for components of radiation use efficiency has rarely been explored due to the lack of appropriate equipment to determine parameters at the scale needed in plant breeding. On the other hand, responses of the photosynthetic apparatus to environmental conditions have not been extensively investigated under field conditions, due to the challenges posed by the fluctuating environmental conditions. This study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance of a set of hybrid barleys and their parents under mild water-stress and unstressed field conditions. We found differences among the genotypic sets that are relevant for plant breeders and geneticists. Hybrids showed lower leaf temperature differential and higher non-photochemical quenching, resembling closer the male parents. The combination of traits detected in hybrids seems favorable, and could indicate improved photoprotection and better fitness under stress conditions. Additionally, we proved the potential of a low-cost, field-based phenotyping equipment to be used routinely in barley breeding programs for early screening for stress tolerance.
Why it matches plant phenotyping methods低コストの野外フルオリメータを用いた高スループット表現型計測が研究の中心であり、光合成性能やストレス耐性関連形質への適用可能性も評価している。
abstractThis study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance
Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.
Why it matches plant phenotyping methodsUAV熱画像による作物キャノピー温度のハイスループット表現型取得ワークフローを構築し、画像統合、精度、反復性、測定時期を評価しており、方法が研究の中心である。
abstractThe aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements.
The use of photosystem II (PSII) inhibitors allows simulating cascade of defense and damage responses, including the oxidative stress. In our study, PSII inhibiting herbicide metribuzin was applied to the leaf of the model plant species Chenopodium album. The temporally and spatially resolved cascade of defense responses was studied noninvasively at the leaf level by combining three imaging approaches: Raman spectroscopy as a principal method, corroborated by chlorophyll a fluorescence (ChlF) and infrared thermal imaging. ChlF imaging show time-dependent transport in acropetal direction through veins and increase of area affected by metribuzin and demonstrated the ability to distinguish between fast processes at the level of electron transport (1 − Vj) from slow processes at the level of non-photochemical energy dissipation (NPQ) or maximum efficiency of PSII photochemistry (Fv/Fm). The high-resolution resonance Raman images show zones of local increase of carotenoid signal 72 h after the herbicide application, surrounding the damaged tissue, which points to the activation of defense mechanisms. The shift in the carotenoid band indicates structural changes in carotenoids. Finally, the increase of leaf temperature in the region surrounding the spot of herbicide application and expanding in the direction to the leaf tip proves the metribuzin effect on slow stomata closure.
Why it matches plant phenotyping methods植物葉の薬剤応答を、Raman・蛍光・熱画像で非侵襲かつ時空間的に取得・評価する手法の適用が中心であり、単なる routine 測定を超える。
abstractThe temporally and spatially resolved cascade of defense responses was studied noninvasively at the leaf level by combining three imaging approaches: Raman spectroscopy as a principal method, corroborated by chlorophyll a fluorescence (ChlF) and infrared thermal imaging.
SunflowerThermalLeafRootPhysiological trait estimationGrowth / development / phenologyStomatal traitsPlant / canopy temperatureWater status / transpiration
Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.
Why it matches plant phenotyping methods熱画像を用いて葉温から蒸散・気孔開度を直接定量する大規模スクリーニング法の開発と実証が中心であり、単なる生物学的測定ではない。
abstractThe screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
SunflowerThermalLeafRootStomata / guard-cell complexPhysiological trait estimationGrowth / development / phenologyStomatal traitsPlant / canopy temperatureWater status / transpiration
Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water deficit or pathogens plays a crucial role. Identifying small molecules that regulate stomatal movement can therefore contribute to understanding the physiological basis by which plants adapt to their environment. Large-scale screening approaches that have been used to identify regulators of stomatal movement have potential limitations: some rely heavily on the abscisic acid (ABA) hormone signaling pathway, therefore excluding ABA-independent mechanisms, while others rely on the observation of indirect, long-term physiological effects such as plant growth and development. The screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging. Since evaporation of water through transpiration results in leaf surface cooling, thermal imaging provides a non-invasive approach to investigate changes in stomatal conductance over time. In this protocol, Helianthus annuus seedlings are grown hydroponically and then treated by root feeding, in which the primary root is cut and dipped into the chemical being tested. Thermal imaging followed by statistical analysis of cotyledonary temperature changes over time allows for the identification of bioactive molecules modulating stomatal aperture. Our proof-of-concept experiments demonstrate that a chemical can be carried from the cut root to the cotyledon of the sunflower seedling within 10 minutes. In addition, when plants are treated with ABA as a positive control, an increase in leaf surface temperature can be detected within minutes. Our method thus allows the efficient and rapid identification of novel molecules regulating stomatal aperture.
Why it matches plant phenotyping methods熱画像と統計解析を用いて葉温から蒸散・気孔開度を直接定量する大規模スクリーニング法が、研究の中心的な技術貢献として提示されている。
abstractThe screening method presented here allows the large-scale treatment of plants with a library of chemicals coupled with a direct quantification of their transpiration by thermal imaging.
Heat stress and resulting sunburn is a major abiotic stress in perineal specialty crops. For example, such stress to the maturing fruits on apple tree canopies can cause several physiological disorders that result in considerable crop losses and reduced marketability of the produce. Thus, there is a critical technological need to effectively monitor the abiotic stress under field conditions for timely actuation of remedial measures. Fruit surface temperature (FST) is one of the stress indicators that can reliably be used to predict apple fruit sunburn susceptibility. This study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring. The tool integrates a smartphone connected thermal-Red-Green-Blue (RGB) imaging sensor and a custom developed application ('AppSense 1.0') for apple fruit sunburn prediction. This tool is configured to acquire and analyze imagery data onboard the smartphone to estimate FST. The tool also utilizes geolocation-specific weather data to estimate weather-based FST using an energy balance modeling approach. The 'AppSense 1.0' application, developed to work in the Android operating system, allows visual display, annotation and real-time sharing of the imagery, weather data and pertinent FST estimates. The developed tool was evaluated in orchard conditions during the 2019 crop production season on the Gala, Fuji, Red delicious and Honeycrisp apple cultivars. Overall, results showed no significant difference (t 110 = 0.51, p = 0.6) between the mobile FST monitoring tool outputs, and ground truth FST data collected using a thermal probe which had accuracy of ±0.4 °C. Upon further refinements, such tool could aid growers in real-time apple fruit sunburn susceptibility prediction and assist in more effective actuation of apple fruit sunburn preventative measures. This tool also has the potential to be customized for in-field monitoring of the heat stressors in some of the sun-exposed perennial and annual specialty crops at produce maturation.
Why it matches plant phenotyping methodsリンゴ果実表面温度という植物ストレス状態を、スマートフォン接続型熱・RGB画像センサーと専用アプリで取得・推定する手法を開発し、圃場でプローブ測定と検証しているため、植物フェノタイピング手法が中心である。
abstractThis study was therefore focused on development and in-field testing of a mobile FST monitoring tool that can be used for real-time crop stress monitoring.
Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( r P e a r s o n = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha - 1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m 3 ha - 1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.
Why it matches plant phenotyping methodsジャガイモの水ストレス状態を熱画像から推定するオープンソース画像処理ソフトウェアを開発し、熱電対由来CWSIとの比較検証も行っており、植物表現型取得手法が中心である。
abstractan open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops.
Time-series high spatial resolution drought monitoring is essential for effective agricultural management. For more than a decade, the multiyear vegetation temperature condition index (VTCI) based on the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) has been applied to regional drought monitoring. However, the spatial resolutions of AVHRR and MODIS (1 km) often represent mixtures of built-up areas and different vegetation or crop types. In this paper, a framework is proposed to obtain a ten-day interval multiyear VTCI at field scales, which is fused from Sentinel-2 data with a fine spatial resolution (20 m) and ten-day interval Terra MODIS data with a coarse spatial resolution using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). This framework includes the disaggregation of the Terra MODIS land surface temperature (LST) using Sentinel-2 data and digital elevation model (DEM) data, the spatiotemporal fusion of normalized difference vegetation index (NDVI) and LST, and drought monitoring by the fused VTCI. The accuracy of this framework for regional drought monitoring was tested in the Guanzhong Plain of China. The results indicate that 1) the Sentinel-2 biophysical products combined with DEM data based on support vector regression can accurately disaggregate the Terra MODIS LST; 2) the ESTARFM has good capability to fuse the NDVI and LST derived from Sentinel-2 and Terra MODIS; 3) the multiyear spatiotemporally fused VTCI, a quantitative drought monitoring index at field scales, can be calculated by using the linear regression between the single-year fused VTCI based on the data in 2018 and the multiyear Terra MODIS VTCI based on an 18-year data record; and 4) the spatiotemporal fusion of the multiyear VTCI can significantly improve the drought monitoring accuracy in the winter wheat and woodland area of the Guanzhong Plain. From mid-March to early May, the multiyear spatiotemporally fused VTCIs have better correlation with the cumulative precipitation over the past 20 days (R² is 0.83 in the entire study area) than the multiyear Terra MODIS VTCIs (R² is 0.80 in the entire study area). The results of this study demonstrate the potential of using the spatiotemporal fusion algorithm to obtain the multiyear VTCI at field scales and provide an effective method for improving the accuracy of drought monitoring.
Why it matches plant phenotyping methodsSentinel-2/MODISデータ融合とLST分解により、圃場スケールの植生・作物の干ばつ状態を推定する方法を開発し、精度検証している。単なる農業管理研究ではなく、植物状態の取得・推定手法が中心である。
abstractIn this paper, a framework is proposed to obtain a ten-day interval multiyear VTCI at field scales, which is fused from Sentinel-2 data with a fine spatial resolution (20 m) and ten-day interval Terra MODIS data with a coarse spatial resolution using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM).
One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding.
Why it matches plant phenotyping methodsUAV搭載の可視・マルチスペクトル・熱センサーでサトウキビの形態・生理形質を高スループット取得し、育種選抜への統合方法を評価しており、表現型取得が研究の中心である。
titleHigh-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding
Photosynthesis reacts dynamic and in different time scales to changing conditions. Light and temperature acclimation balance photosynthetic processes in a complex interplay with the fluctuating environment. However, due to limitations in the measurements techniques, these acclimations are often described under steady-state conditions leading to inaccurate photosynthesis estimates in the field. Here we analyze the photosynthetic interaction with the fluctuating environment and canopy architecture over two seasons using a fully automated phenotyping system. We acquired over 700,000 chlorophyll fluorescence transients and spectral measurements under semi-field conditions in four crop species including 28 genotypes. As expected, the quantum efficiency of the photosystem II (F v /F m in the dark and F q '/F m ' in the light) was determined by light intensity. It was further significantly affected by spectral indices representing canopy structure effects. In contrast, a newly established parameter, monitoring the efficiency of electron transport (F r2 /F v in the dark respective F r2 '/F q ' in the light), was highly responsive to temperature (R 2 up to 0.75). This parameter decreased with temperature and enabled the detection of cold tolerant species and genotypes. We demonstrated the ability to capture and model the dynamic photosynthesis response to the environment over entire growth seasons. The improved linkage of photosynthetic performance to canopy structure, temperature and cold tolerance offers great potential for plant breeding and crop growth modeling.
Why it matches plant phenotyping methods自動蛍光キャノピースキャンによる大規模な生理形質取得と、新規光合成効率パラメータの確立・環境応答モデル化が研究の中心であり、植物フェノタイピング手法に該当する。
abstractusing a fully automated phenotyping system
We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.
Why it matches plant phenotyping methodsセンサーで取得した複数の植物形質を機械学習に統合し、生育期間中の収量予測モデルを構築することが研究の中心であり、再利用可能なフェノミクス解析ワークフローに該当する。
abstractWe explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsIR画像、クロロフィル蛍光、フェノミクスを組み合わせ、樹冠温度と乾燥ストレスを評価する手法の適用が題名上の中心であり、植物状態の取得・評価に該当する。
titleCombining IR imaging, chlorophyll fluorescence and phenomic approach for assessing diurnal canopy temperature dynamics and desiccation stress management in Azadirachta indica and Terminalia mantaly
Stem / branchPhysiological trait estimationGrowth / development / phenologyPlant / canopy temperatureWater status / transpiration
Mechanistic models of plant respiration remain poorly developed, especially in stems and woody tissues where measurements of CO 2 efflux do not necessarily reflect local respiratory activity. We built a process-based model of stem respiration that couples water and carbon fluxes at the organ level (TReSpire). To this end, sap flow, stem diameter variations, xylem and soil water potential, stem temperature, stem CO 2 efflux and nonstructural carbohydrates were measured in a maple tree, while xylem CO 2 concentration and additional stem and xylem diameter variations were monitored in an ancillary tree for model validation. TReSpire realistically described: (1) turgor pressure to differentiate growing from nongrowing metabolism; (2) maintenance expenditures in xylem and outer tissues based on Arrhenius kinetics and nitrogen content; and (3) radial CO 2 diffusivity and CO 2 solubility and transport in the sap solution. Collinearity issues with phloem unloading rates and sugar-starch interconversion rates suggest parallel submodelling to close the stem carbon balance. TReSpire brings a breakthrough in the modelling of stem water and carbon fluxes at a detailed (hourly) temporal resolution. TReSpire is calibrated from a sink-driven perspective, and has potential to advance our understanding on stem growth dynamics, CO 2 fluxes and underlying respiratory physiology across different species and phenological stages.
Why it matches plant phenotyping methods樹幹の呼吸・水炭素フラックスと成長動態を推定する機構モデルを開発し、実測値で較正・検証しており、植物の生理状態を取得・推定する方法が研究の中心である。
abstractWe built a process-based model of stem respiration that couples water and carbon fluxes at the organ level (TReSpire).
Background Virus diseases caused by co-infection with Sweet potato feathery mottle virus (SPFMV) and Sweetpotato chlorotic stunt virus (SPCSV) are a severe problem in the production of sweetpotato ( Ipomoea batatas L.). Traditional molecular virus detection methods include nucleic acid-based and serological tests. In this study, we aimed to validate the use of a non-destructive imaging-based plant phenotype platform to study plant-virus synergism in sweetpotato by comparing four virus treatments with two healthy controls. Results By monitoring physiological and morphological effects of viral infection in sweetpotato over 29 days, we quantified photosynthetic performance from chlorophyll fluorescence (ChlF) imaging and leaf thermography from thermal infrared (TIR) imaging among sweetpotatoes. Moreover, the differences among different treatments observed from ChlF and TIR imaging were related to virus accumulation and distribution in sweetpotato. These findings were further validated at the molecular level by related gene expression in both photosynthesis and carbon fixation pathways. Conclusion Our study validated for the first time the use of ChlF- and TIR-based imaging systems to distinguish the severity of virus diseases related to SPFMV and SPCSV in sweetpotato. In addition, we demonstrated that the operating efficiency of PSII and photochemical quenching were the most sensitive parameters for the quantification of virus effects compared with maximum quantum efficiency, non-photochemical quenching, and leaf temperature.
Why it matches plant phenotyping methodsウイルス感染による植物の症状・生理状態を、クロロフィル蛍光および熱画像で定量する非破壊表現型プラットフォームの検証が中心である。
abstractwe aimed to validate the use of a non-destructive imaging-based plant phenotype platform
To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red-green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 ( n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.
Why it matches plant phenotyping methodsUAVのRGB・熱画像からトウモロコシ群落温度を抽出する手法を開発・校正・検証しており、植物の水ストレス状態を測定する方法が研究の中心です。
abstractthe canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red-green-blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV).
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60-75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r = -0.67 to -0.91 in ADP; r = -0.55 to -0.72 in DDP), followed by seed yield (r = 0.51 to 0.73 in ADP; r = 0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r = 0.28-0.88). Finally, lower resolution satellite images (0.05 to 5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r = 0.52) and biomass (r = -0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping.
Why it matches plant phenotyping methodsUAS・衛星のマルチスペクトル／熱画像からNDVIや温度を抽出し、乾燥豆のバイオマス・収量などの形質推定性能を比較・検証する研究であり、フェノタイピング手法が中心です。
titleUnmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean
ThermalFlowerLeafGrowth / time-series analysisPlant / canopy temperature
Endogenous (˜24 circadian) rhythms control an enormously diverse range of processes in plants and are, increasingly, the target of studies aimed at understanding plant performance. Although in the previous few decades most plant circadian research has focused on Arabidopsis, there is a pressing need for low-cost, high-throughput tools for analyzing rhythms in a wider variety of species. The present contribution investigates using circadian temperature oscillations as a novel marker for assaying plant circadian rhythms. A thermal imaging platform was set up to measure diel and circadian rhythms in different plant species, in wild-type and circadian mutant plants, and in leaves and flowers. Results from the thermal imaging technique were compared with those from other established circadian assay techniques. All of the dicot and monocot species examined showed robust circadian rhythms of leaf surface temperature; the effects of circadian mutations on thermocycles were similar to those reported using other techniques. In Petunia × atkinsiana plants circadian oscillations were observed in both leaves and flowers. Thermal imaging is an extremely useful technique for analyzing circadian rhythms in plants. It is predicted that the ability to make very high temporal resolution measurements may facilitate the discovery of novel aspects of circadian control.
Why it matches plant phenotyping methods植物の概日リズムを葉・花の表面温度から非侵襲的に推定する熱画像プラットフォームを開発・比較検証しており、表現型取得法が研究の中心である。
abstractThe present contribution investigates using circadian temperature oscillations as a novel marker for assaying plant circadian rhythms.
This study compares distinct phenotypic approaches to assess wheat performance under different growing temperatures and vernalization needs. A set of 38 (winter and facultative) wheat cultivars were planted in Valladolid (Spain) under irrigation and two contrasting planting dates: normal (late autumn), and late (late winter). The late plating trial exhibited a 1.5 °C increase in average crop temperature. Measurements with different remote sensing techniques were performed at heading and grain filling, as well as carbon isotope composition (δ 13 C) and nitrogen content analysis. Multispectral and RGB vegetation indices and canopy temperature related better to grain yield (GY) across the whole set of genotypes in the normal compared with the late planting, with indices (such as the RGB indices Hue, a* and the spectral indices NDVI, EVI and CCI) measured at grain filling performing the best. Aerially assessed remote sensing indices only performed better than ground-acquired ones at heading. Nitrogen content and δ 13 C correlated with GY at both planting dates. Correlations within winter and facultative genotypes were much weaker, particularly in the facultative subset. For both planting dates, the best GY prediction models were achieved when combining remote sensing indices with δ 13 C and nitrogen of mature grains. Implications for phenotyping in the context of increasing temperatures are further discussed.
Why it matches plant phenotyping methods小麦収量を対象に、マルチスペクトル・RGB・熱赤外リモートセンシングと同位体指標を比較し、収量予測性能を評価することが中心であり、表現型取得・推定手法の検証に該当する。
titleRemote sensing techniques and stable isotopes as phenotyping tools to assess wheat yield performance
Reproduction assets foundThe paper used the authors' MosaicTool software (a FIJI plugin) to crop and process UAV RGB/thermal/multispectral plot images and compute vegetation indices for this wheat phenotyping study. MosaicTool is publicly available via the authors' GitLab repository and project page, both listed in the article text and in the,Code · publiclater overlaps up to 30 images (with at least 80%
ro
overlap) and removes UAV flight effects to produce accurate ortho-mosaics. Afterwards, regions
of interest (plots) were cropped and processed using the MosaicTool software (Prof. Shawn C.
Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/,
-p
https://gitlab.com/sckefauver/MosaicTool/, University of Barcelona, Barcelona, Spain)
integrated as a plugin for the open source image analysis platform FIJI (Fiji is Just ImageJ;
http://fiji.sc/Fiji) [40].
re
Extracted RGB vegetation indices collected from both ground and aerial platforms were obtained
using an updated version of the original Breedpix 2.0 software [41Open asset ↗sckefauver/MosaicToolpdf-layout-page:7 lines:1-78Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
In plant phenotyping, leaf-level physiological and chemical trait measurements are needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, enabling automated monitoring with minimal human intervention. In this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties. The robotic system comprised of a four Degree of Freedom (DOF) robotic manipulator and a Time-of-Flight (TOF) camera. A robotic gripper was developed to integrate an optical fiber cable (coupled to a portable spectrometer) for leaf spectral reflectance measurement, and a thermistor for leaf temperature measurement. A MATLAB program along with a Graphical User Interface (GUI) was developed to control the robotic system and its components, and for acquiring and recording data obtained from the sensors. The system was tested in a greenhouse using maize and sorghum plants. The results showed that leaf temperature measurements by the phenotyping robot were significantly correlated with those measured manually by a human researcher (R2 = 0.58 for maize and 0.63 for sorghum). The leaf spectral measurements by the phenotyping robot predicted leaf chlorophyll, water content and potassium with moderate success (R2 ranged from 0.52 to 0.61), whereas the prediction for leaf nitrogen and phosphorus were poor. The total execution time to grasp and take measurements from one leaf was 35.5 ± 4.4 s for maize and 38.5 ± 5.7 s for sorghum. Furthermore, the test showed that the grasping success rate was 78% for maize and 48% for sorghum. The phenotyping robot can be useful to complement the traditional image-based high-throughput plant phenotyping in greenhouses by collecting in vivo leaf-level physiological and biochemical trait measurements.
Why it matches plant phenotyping methods植物葉の生理・化学形質を自動取得するロボット型フェノタイピングシステムを開発し、手動測定との相関、予測性能、把持成功率、実行時間を評価しており、方法が研究の中心である。
abstractIn this paper, a plant phenotyping robotic system was developed to realize automated measurement of plant leaf properties.
The use of thermography as a means of crop water status estimation is based on the assumption that a sufficient amount of soil moisture enables plants to transpire at potential rates resulting in cooler canopy than the surrounding air temperature. The same principle is applied in this study where the crop transpiration changes occur because of the fungal infection. The field experiment was conducted where 25 wheat genotypes were infected with Zymoseptoria tritici. The focus of this study was to predict the onset of the disease before the visual symptoms appeared on the plants. The results showed an early significant increase in the maximum temperature difference within the canopy from 1 to 7 days after inoculation (DAI). Biotic stress associated with increasing level of disease can be seen in the increasing average canopy temperature (ACT) and maximum temperature difference (MTD) and decreasing canopy temperature depression (CTD). However, only MTD (p ≤ 0.01) and CTD (p ≤ 0.05) parameters were significantly related to the disease level and can be used to predict the onset of fungal infection on wheat. The potential of thermography as a non-invasive high throughput phenotyping technique for early fungal disease detection in wheat was evident in this study.
Why it matches plant phenotyping methods赤外線サーモグラフィーでコムギの感染に伴う温度形質を測定し、目視症状前の病害検出性能を評価しており、フェノタイピング手法の適用・検証が中心である。
abstractThe focus of this study was to predict the onset of the disease before the visual symptoms appeared on the plants.