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:220-232Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
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:406-446Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:49-77Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:64-174Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
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:129-274Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
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:1-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published1 Mar 2025The Plant journal : for cell and molecular biologyCited by 6 · OpenAlex ↗
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:156-164Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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
Related research article
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Value of the Data
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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:46-71Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
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:845-856Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:58-67Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
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:1-87Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
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
Research Methods
New
Phytologist
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14698137,
2024,
3,
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from
https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19405
by
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Vernon
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Wiley
Online
Library
on
[20/12/Open asset ↗garenj/Faster-methodpdf-raw-page:10 lines:89-144Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
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
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(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
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(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.
7Open asset ↗zenodo · 10.5281/zenodo.8164473pdf-raw-page:30 lines:1-62Dataset · publiconsent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(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.
760
761
Funding
762
This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
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:663-812Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
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:724-739Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
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:500-598Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
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:1-46Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published21 Sept 2019Plant science : an international journal of experimental plant biologyCited by 30 · OpenAlex ↗
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:1-78Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The feasibility of thermography as a technique for plant screening aiming at drought-tolerance has been proven by its relationship with gas exchange, biomass, and yield. In this study, unlike most of the previous, thermography was applied for phenotyping contrasting maize genotypes whose classification for drought tolerance had already been established in the field. Our objective was to determine whether thermography-based classification would discriminate the maize genotypes in a similar way as the field selection in which just grain yield was taken into account as a criterion. We evaluated gas exchange, daily water consumption, leaf relative water content, aboveground biomass, and grain yield. Indeed, the screening of maize genotypes based on canopy temperature showed similar results to traditional methods. Nevertheless, canopy temperature only partially reflected gas exchange rates and daily water consumption in plants under drought. Part of the explanation may lie in the changes that drought had caused in plant leaves and canopy structure, altering absorption and dissipation of energy, photosynthesis, transpiration, and partitioning rates. Accordingly, although there was a negative relationship between grain yield and plant canopy temperature, it does not necessarily mean that plants whose canopies were maintained cooler under drought achieved the highest yield.
Why it matches plant phenotyping methods熱画像サーモグラフィーを用いた作物表現型スクリーニングを、既知の乾燥耐性分類と比較して検証しており、方法の適用・妥当性評価が研究の中心です。
abstractthermography was applied for phenotyping contrasting maize genotypes
Reproduction assets foundThe paper's supplementary materials (hosted at the MDPI supplement URL) explicitly contain paper-specific phenotyping assets: weather data recorded during the experiment (Supplementary File 1), UAV/thermal-imager setup (File 2), the mean-shift segmentation procedure (File 3), segmented canopy masks (File 4), and the unSupplement · publicum quantum yield of photosystem II
gs
Stomatal conductance to water vapor
GY
Grain Yield
i WUE
Intrinsic Water Use Efficiency
LRWC
Leaf Relative Water Content
PSII
Photosystem II
RGB
Red, Green and Blue color model
SWC
Soil Water Content
UAV
Unmanned Aerial Vehicle
Supplementary Materials
Supplementary materials can be found at https://www.mdpi.com/1422-0067/20/9/2273/s1 .
Click here for additional data file.
Author Contributions
C.A.F.S., R.L.G. and P.C.M. conceived and designed the experiments; R.A.C.N.C., D.S.P., T.M.M.F. and V.N.B.S. performed the experiments; C.A.F.S., R.A.C.N.C., N.G.O., M.T.S.J., T.T.S. analyzed the data; C.A.F.S., H.B.C.M., A.K.K., T.T.S. and M.T.S.J. wrote the paperOpen asset ↗lines:105-166Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.
Why it matches plant phenotyping methods圃場型HTPシステムを用いたNDVI・群落温度の取得と、ゲノム選抜モデルへの統合を技術的に評価しており、表現型取得基盤の適用が中心的です。
abstractA portable phenotyping system called 'Phenocart' was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates.
Reproduction assets foundThe authors explicitly state that all data sets and analysis scripts for this wheat HTP/genomic-selection study are publicly deposited in the Dryad Digital Repository under DOI 10.5061/dryad.7f138. This covers the paper-specific phenotype data (NDVI, canopy temperature, grain yield BLUPs) and scripts. Note: no Dryad/DODataset · publicAll data sets and scripts are available from the Dryad Digital Repository: 10.5061/dryad.7f138 .Dryad Digital Repository · 10.5061/dryad.7f138lines:223-263