Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurate estimation of evapotranspiration (ET) is critical for irrigation management in water-scarce regions such as the Middle East and North Africa (MENA). This study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer, deployed simultaneously over an irrigated late-season potato field (1.8 ha) in the Beqaa Valley, Lebanon. Satellite NDVI observations indicate that the BLS–EC overlap period (13 October–27 November 2021) sampled the crop from peak canopy (NDVI ≈ 0.85–0.90) through the onset of senescence (NDVI ≈ 0.79). The BLS (Scintec BLS900) operated along a 140 m path. The EC system showed incomplete daytime energy-balance closure, with a regression slope of ≈0.69 and a seasonal Bowen-ratio-preserving correction factor of CF = 1.24 (a ~19% closure deficit) was used. Across the matched period, daily H from the BLS was strongly correlated with EC (r ≈ 0.82) but systematically lower, with a regression slope of ≈0.63 that persisted across timescales; this scale-invariant amplitude compression reflects the path-averaged, similarity-based nature of the scintillometer retrieval rather than the EC closure deficit, which instead governs the mean bias. BLS-derived daily ET showed a systematic positive bias relative to uncorrected EC (mean bias error, MBE = +0.30 mm d−1; +16% cumulative). Applying the Bowen-ratio-preserving correction (CF = 1.24) to EC reduced this to MBE = −0.14 mm d−1 (−6%), and the residual-to-LE correction yielded MBE = −0.15 mm d−1 (−6.4%); the latter comparison is only partly independent, as both methods share the same Rn and G. The Bowen-ratio-preserving method is therefore recommended for this dataset. Overall, the BLS captured the temporal variability of crop water use well, but residual-based ET estimates require careful treatment of the energy-balance-closure gap and are sensitive to the high BLS gap fraction (61.6% of 15 min records over the overlap, exceeding 90% at night). Once EC is closure-corrected to serve as the reference, the BLS offers a cost-effective alternative for field-scale ET monitoring in the MENA region, subject to the conditional agreement documented here.
Why it matches plant phenotyping methodsジャガイモ圃場の作物蒸発散量（ET）という生理・水利用状態を対象に、ECとBLSを比較検証し、補正法や測定誤差も評価している。センサー測定法の技術的妥当性が中心であり、単なる routine measurement ではない。
abstractThis study compares sensible heat flux (H), latent heat flux (LE), and ET derived from eddy covariance (EC) and a boundary-layer scintillometer (BLS) operated with an aperture reducer
Reproduction assets foundThe paper's flux/ET datasets are only available on request from the corresponding author, so they do not qualify as public assets. However, the Supplementary Information file (available at the MDPI supplementary URL) explicitly contains experiment sensor documentation and field/canopy images (Figures S1–S4: study site,Supplement · publicmeasurements along the beam. Because these results derive from a single crop, season, and phenological window, their generalization awaits multi-site, multi-season replication spanning the full-canopy cycle—the priority for subsequent campaigns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s26144398/s1 , Figure S1: Study site and potato canopy—Beqaa Valley, Lebanon; Figure S2: Eddy covariance system—full tower view (peak canopy); Figure S3: EC sensor suite close-up and soil sensor installation; Figure S4: BLS900 scintillometer—transmitter, receiver, and meteorological station.
Author Contributions
Conceptualization, HOpen asset ↗lines:251-268Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Field-based phenotyping of water-related traits for precision irrigation in tropical agroecosystems poses a persistent methodological challenge, driven by high climatic variability and the complex water-use physiology of Crassulacean Acid Metabolism (CAM) crops such as pineapple (Ananas comosus var. MD2). We developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status. A six-month field campaign (March–August 2022) across 25 georeferenced commercial pineapple plots in the Colombian Orinoquia piedmont yielded a spatiotemporally balanced dataset of N=150 observations. Soil-adjusted vegetation indices (OSAVI, MSAVI) outperformed standard NDVI for capturing water-related canopy traits, effectively decoupling spectral responses from substrate noise. A Gradient Boosting regressor achieved R2=0.842 and RMSE=0.0705 on a normalized target scale, corresponding to a 7.05% error over the prediction range, while the traffic-light Decision Support System (DSS) for irrigation scheduling reached 91.1% accuracy (Cohen’s Kappa =0.91). Incorporating daily soil moisture depletion as a mechanistic feature improved predictive accuracy over a spectral-only baseline (ΔR2=+0.052) and anchored predictions within a physically consistent framework based on the FAO-56 water balance, with no false negatives observed for water deficit detection in the hold-out validation set. This framework advances high-throughput, population-scale phenotyping of water-related traits in open-canopy CAM crops, establishing a transferable methodology for operational precision irrigation under tropical savanna conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物の水関連形質・水状態を推定する枠組みを開発・検証しており、表現型取得と予測手法が研究の中心である。
abstractWe developed and validated a Physics-Informed Machine Learning (PIML) framework that integrates high-resolution UAV multispectral imagery, IoT-based microclimatic records, and a mechanistic soil water balance based on the FAO-56 Penman–Monteith standard to predict plot-scale soil moisture depletion as a proxy of plant water status.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the complete dataset and source code (raw UAV multispectral imagery, Python scripts, IoT sensor logs, CROPWAT 8.0 files, and XGBoost model code) in a public Mendeley Data repository, which directly reproduces this paper's phenotyping measurements and analysis.Dataset · publicThe complete dataset and source code supporting this study are publicly available at Mendeley Data: https://data.mendeley.com/datasets/9xwdvzf3bf/1 (accessed on 20 May 2026). The repository includes: (1) raw multispectral UAV imagery with calibration panel captures; (2) Python scripts for DN-to-reflectance conversion and spectral index extraction; (3) IoT sensor logs (soil moisture, temperature, relative humidity); (4) CROPWAT 8.0 project files for FAO-56 soil water balance simulation; and (5) XGBoost model source code with hyperparameter optimization routines.Open asset ↗Mendeley Data · 9xwdvzf3bf/1lines:193-228Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
TissuePhysiological trait estimationWater status / transpiration
Process-based models that mechanistically represent water-carbon balances in the atmosphere-soil-plant continuum are an attractive tool for monitoring live fuel moisture content (LFMC) dynamics, a key variable when assessing fire danger. However, their application as operational tools to assess near-term wildfire danger at regional scale faces important challenges. Here, we explored key sources of prediction uncertainty in process-based modeling of LFMC. We applied the SurEau-ECOS model of plant hydraulics embedded within the MEDFATE modeling framework to assess how the accuracy of LFMC predictions was influenced by input data sources, by the availability of species-specific plant traits and by the level of mechanistic detail used to model water content of plant tissues. A lack of accurate data describing soil physical properties compromises the application of process-based models for predicting LFMC. Nonetheless, using global meteorological and vegetation data allows for successful regional-scale applications. Fully mechanistic approaches that model LFMC from plant water status using ecophysiological knowledge yield more accurate predictions. However, when reliable plant traits are lacking, semimechanistic approaches based on empirical equations offer a robust alternative. Overall, addressing the sources of uncertainty highlighted here could pave the way for developing operational tools to forecast near-term wildfire danger through process-based modeling of LFMC dynamics.
Why it matches plant phenotyping methods植物の生体燃料水分量（LFMC）という生理状態の推定モデルを対象に、入力データ、植物形質、機構的詳細度が予測精度へ与える影響と不確実性を評価しており、植物状態の取得・推定手法が中心である。
abstractHere, we explored key sources of prediction uncertainty in process-based modeling of LFMC.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the LFMC field data (Catalan and Reseau–Hydrique networks) and the analysis/figure code in a public GitHub repository, which directly reproduces this paper's phenotyping measurements (7203 LFMC values) and computational analysis. Supporting Information TablesSCode · publicof the ‘Severo Ochoa’ Centres of Excellence programme, Ref. CEX2023‐001340‐S, funded by MICIU/AEI/ https://doi.org/10.13039/501100011033 . Also it was supported by the Spanish Government project IMPROMED (grant no. PID2023‐152644NB‐I00).
Data availability
The data and code for analyses and figures are available through GitHub ( https://github.com/emf‐creaf/LFMC_FR_CAT ). Also, the data that support the findings of this study are available in the Supporting Information of this article, specifically in Tables S1–S3 .
References
Balaguer‐Romano
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,
De Cáceres
M
,
Espelta
JM
. 2025 .
Second‐growth forests exhibit higher sensitivity to dry and wet years than long‐existing ones
. Ecosystems
28 : 6Open asset ↗emf‐creaf/LFMC_FR_CATlines:253-664Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.
Why it matches plant phenotyping methodsトマトの電気生理シグナルから水ストレス状態を推定するセンシング・機械学習パイプラインを開発し、未学習データで性能検証しているため、植物フェノタイピング手法が中心である。
abstractDirect physiological sensing offers the potential to detect stress responses before visible symptoms appear.
Reproduction assets foundThe paper's electrophysiological time-series and soil moisture measurements from the water-stress tomato experiment are explicitly stated to be publicly available online via a Zenodo deposit (Buss et al. 2026a), referenced in both the Methods and Data availability sections.Dataset · publicAll recorded and
processed data are available online (Buss et al. 2026a).Open asset ↗pdf-page:5 lines:1-37Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
Why it matches plant phenotyping methods植物形質をハイパースペクトル画像から推定する深層学習について、OOD条件での不確実性推定手法Dis_UNを開発・評価しており、表現型取得・推定手法が中心である。
abstractwe propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty
Reproduction assets foundThe paper's authors publicly released their uncertainty-analysis code (two GitHub repositories) and the study data (Hugging Face dataset) with explicit availability statements and URLs. The EnMAP and NEON hyperspectral scenes are third-party public data sources, not paper-specific deposits, and the supplement is not anCode · publicThe code for this study is available at: https://github.com/echerif18/Multi_trait_Uncertainty/ (last access: 8 March 2026).Open asset ↗echerif18/Multi_trait_Uncertaintylines:449-456Dataset · publicThe data used in this study are available on Hugging Face: https://doi.org/10.57967/hf/7838 (Cherif et al., 2026).Open asset ↗Hugging Face · 10.57967/hf/7838lines:457-483Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Abstract Purpose Accurate estimation of crop transpiration is essential for optimizing irrigation management and improving water-use efficiency in precision agriculture. However, direct measurement of transpiration is often invasive, costly, and difficult to maintain at large scales. This study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques. Methods Field experiments were conducted during the 2023 and 2024 growing seasons in central Italy under irrigated silage maize. Meteorological variables, soil water content, and crop growth indicators were used as inputs, while sap flow measurements served as reference outputs. Several machine learning models were evaluated, including Linear Regression, Support Vector Regression (SVR), Decision Tree Regressor, and Multi-Layer Perceptron Regressor (MLPR), using both Point Estimation and Temporal Estimation strategies. Temporal approaches incorporated short-term historical information through feature concatenation and previous-average windows. Results Results demonstrate that non-linear models, particularly MLPR and SVR, consistently outperform linear and tree-based approaches. The inclusion of short temporal windows (45 minutes to 2 hours) significantly improves predictive accuracy, enhancing reconstruction of the diurnal transpiration pattern. Feature concatenation proved more effective than averaging strategies in capturing soil–plant–atmosphere interactions. Model performance remained robust across two contrasting growing seasons, confirming good generalization capability under interannual variability and data discontinuities. Conclusion The proposed framework provides a reliable and minimally invasive solution for real-time estimation of maize transpiration, supporting precision irrigation management. These findings highlight the potential of machine learning models as practical decision-support tools for sustainable agricultural water management.
Why it matches plant phenotyping methodsトウモロコシの蒸散・樹液流という生理形質を、気象・土壌水分データと機械学習で推定する手法を開発・比較し、複数年で性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes a data-driven framework to estimate maize ( Zea mays L.) sap flow driven by transpiration using widely available climatic and soil moisture data combined with machine learning techniques.
Reproduction assets foundThe paper's Data Availability statement says part of the datasets generated and analyzed (maize sap flow, climate, and soil moisture measurements) are publicly available on the authors' GitHub, while the analysis source code is only promised upon acceptance.Dataset · publicon; Datacuration; Formal
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analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision;
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Validation; Visualization; Writing – original draft; Writing – review and editing.
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D t v il ility Part of the datasets generated and analyzed during the current study are
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publicly available at https://github.com/isarlab-department-690
engineering/Agritech3.1.5FIWARE. The source code used for data processing and analysis will
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be released upon acceptance of the paper in the GitHub repository https://github.com/isarlab-692
department-engineering/DD_Maize_Sap_Flow.
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Funding This work was carried out within the framework of the project Agritech National ROpen asset ↗isarlab-department-690pdf-raw-page:31 lines:1-67Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract The global issue of water scarcity and climate change requires highly efficient and intelligent irrigation systems that are capable of optimizing water consumption with high crop productivity. The paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data. The paper analyzes 55,450 soybean data with 13 physiological and biochemical parameters to implement and compare six regression models for predicting the water stress index. After eliminating tautology by removing the direct water content parameter from the prediction model, LightGBM and XGBoost ensemble tree models achieved near-perfect accuracy for predicting crop water stress using regular plant parameters alone, with R² = 1.0 and RMSE = 1.57×10⁻⁸ to 5.04×10⁻⁵. The Random Forest classifier, which was implemented without any direct stress indicators, achieved perfect discrimination between low, moderate, and high stress classes with precision/recall equal to 1.0, and 5-fold cross-validation and noise tests confirmed its robustness. SHAP analysis of the results showed protein percentage (PPE) and seed yield per unit area (SYUA) to be key drivers of water stress, providing valuable insights for precision agriculture. The model for determining irrigation requirements based on crop evapotranspiration and stress level achieved R² = 1.0 with zero error, making it possible to translate trait values directly into irrigation requirements. The framework presented in this paper brings together machine learning and agronomic knowledge to provide real-time data-driven solutions for irrigation systems, which have 30–50% water savings potential while maintaining healthy crops. It lays the ground for the development of AI-assisted irrigation systems that are applicable to different crops and climatic conditions, particularly in water-scarce countries such as Iraq.
Why it matches plant phenotyping methods作物の水ストレス状態を生理・農学データから機械学習で推定し、複数モデルの比較、交差検証、ノイズ試験、解釈分析まで行う計算的フェノタイピング手法が中心である。灌漑最適化への応用を含むが、単なる日常的測定ではない。
abstractThe paper aims to provide a holistic machine learning framework for crop water stress prediction and efficient irrigation scheduling using multi-parametric agronomic data.
Reproduction assets foundThe paper's soybean phenotyping dataset (55,450 records, 13 physiological/biochemical traits) is publicly available on Kaggle; the Data Availability statement points to it, though it ambiguously labels it as the code implementation location. No separate verified code repository is provided.Dataset · publicThe dataset used in this study (Advanced Soybean Agricultural Dataset) is available from the corresponding author upon reasonable request. The code implementation for all analyses is available at: https://www.kaggle.com/datasets/wisam1985/advanced-soybean-agricultural-dataset-2025 .Open asset ↗kaggle · wisam1985/advanced-soybean-agricultural-dataset-2025lines:372-406Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Accurate plant health monitoring relies on hyperspectral imagery to extract vegetation spectral signatures and compute vegetation indices (VIs), which are critical for phenotyping and crop condition assessment. However, the requirement for high spectral resolution significantly increases the cost and complexity of data acquisition. In this study, we proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data. The aim was to reduce the dependency on high-resolution spectral imagery without compromising prediction accuracy. The framework integrated correlation-based feature selection with four regression models to identify and utilize the most informative spectral bands from coarsely sampled data. The system was trained and validated using a data set consisting of 555 spectral signatures collected from olive leaves at five stages of dehydration, with spectral resolutions ranging from 1 to 100 nm. A total of 25 vegetation indices, commonly used in the estimation of water stress, chlorophyll, and nitrogen, were predicted on various sampling scales. Experimental results show that even with 100 nm spectral resolution, the proposed framework achieves high prediction accuracy, with coefficients of determination reaching 0.99 for RVSI, VOPT, and SPADI indices. These findings demonstrate that accurate vegetation index estimation is achievable with significantly fewer spectral bands, offering a cost-effective solution for large-scale plant health monitoring. This framework lays the groundwork for the development of low-cost, data-efficient remote sensing systems for precision agriculture, especially in crops such as olives, where health dynamics are sensitive to water and nutrient status.
Why it matches plant phenotyping methodsオリーブ葉のハイパースペクトルデータから植物状態に関わる植生指数を推定する、低コストな機械学習・スペクトル測定フレームワークの開発と検証が中心である。
abstractwe proposed a novel machine learning-based framework for predicting VIs from down-sampled hyperspectral reflectance data
Reproduction assets foundThe paper's Data Availability statement points to a Figshare deposit (DOI 10.6084/m9.figshare.26950660.v2), which per the statement hosts the study's data — the 555 olive-leaf hyperspectral signatures and vegetation index measurements underlying the phenotyping analysis. This is a paper-specific, publicly accessible,直接Dataset · publicnm.
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Inclusivity in global research questionnaire.
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Acknowledgments
The authors thank the Advanced Center of Electric and Electronic Engineering - AC3E ANID. The authors acknowledge the support provided by Universidad Técnica Federico Santa María and the Direction of Post-Grade programs DDP.
Data Availability
https://doi.org/10.6084/m9.figshare.26950660.v2 .
Funding Statement
This work was funded by the ANID FB240002 basal center AC3E, and ANID national doctorate scholarship, folio N°21231129. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
References
1. Ruiz-Carrasco B, Fernández-Lobato L, López-Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2lines:266-293Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Pea (Pisum sativum) production is challenged by drought stress. Traditional methods for assessing drought tolerance are limited, and high-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits. Herein, 180 Pisum spp. accessions were evaluated using an indoor HTP platform under two irrigation treatments, control (70% field capacity) and drought stress (30% field capacity), for 50 days. A combination of digital phenotyping via imaging and manual measurements was used to analyse biomass-related, architectural, and physiological traits. Drought conditions resulted in significant reductions in biomass-related traits including fresh weight (47%), total leaf area (43%), and dry weight (41%). In contrast, PSII photochemical efficiency, leaf weight ratio, and solidity showed negative sensitivity index values (ranging from -7% to -1%), indicating comparatively lower sensitivity to drought and suggesting relative stability of these traits under water-limited conditions. The high heritability value for water use efficiency (0.87) suggests that this parameter may be useful for distinguishing pea's responses to suboptimal soil moisture levels. Principal component analysis (PCA) highlighted patterns of trait variation and associations among biomass-related traits, such as fresh weight, dry weight, and leaf area, which were sensitive to drought conditions. This suggests that the plants may use a combination of strategies to cope with water limitations. Furthermore, studying the significant variation in drought response among the diverse Pisum species and subspecies revealed distinct adaptation strategies. These findings support the development of crops that are resilient to the negative effects of climate change.
Why it matches plant phenotyping methods屋内HTPプラットフォームと画像ベースのデジタルフェノタイピングを用いて、多数アクセッションの形態・生理形質を取得・解析しており、フェノタイピング手法の実質的な適用が研究の中心です。
abstracthigh-throughput phenotyping (HTP) can facilitate the rapid and automated assessment of plant traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe analysis software for the RGB side‐view imaging has been developed in Python by the NPEC data team, the source is published on Github, accessible via this link: https://github.com/NPEC‐NL/greenhouse_m5 .Open asset ↗NPEC‐NL/greenhouse_m5lines:68-83Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. Belding, and P. Jain for insightful
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discussions. This work was supported by the Center for Research on Programmable Open asset ↗GitHub · desai-sahil/sys-bio-gspdf-raw-page:9 lines:1-74Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedCrossref · checked 6 Sept 2026
CoffeeMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations.
Why it matches plant phenotyping methodsコーヒー植物の葉スペクトルから水ポテンシャルという生理形質を機械学習で推定・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・評価に該当する。
abstractThe objective of this study was to estimate water potential in coffee plants using spectral variables.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's datasets (coffee leaf spectral reflectance and water potential measurements) and the MATLAB analysis codes are publicly available at the authors' UFLA repository, which is an allowed URL. This is a paper-specific, public, actionable asset.Dataset · publicData Availability Statement: The datasets and MATLAB codes used in this study are available at
http://www.aia.ufla.br/home/filesdatasets/, accessed on 27 November 2025.Open asset ↗aia.ufla.brpdf-page:18 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
MaizeTomatoLeafPhysiological trait estimationWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルという植物生理形質を連続測定するセンサー設置手順とデータ処理コードを中心に扱うプロトコルであり、植物フェノタイピング手法が研究の中心である。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its example water-potential datasets (soil matric potential from Teros 21, leaf water potential from PSY1, transpiration from scales) and the authors' data extraction/cleaning/analysis code on Zenodo (10.5281/zenodo.17158115), under CC0/CC-BY. A supplementary installation video is separately on ZenodDataset · public52.
PubMed Abstract | Publisher Full Text
Cotrozzi L, Couture JJ, Cavender-Bares J, et al.: Using foliar spectral properties
References
Figure 9. Example of data cleaning using the algorithm. Green is kept data and red is discarded data.
Data availability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
CreOpen asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:11 lines:1-61Code · publicat were missing,
zero, or otherwise aberrant. It was also programmed to iden-
tify and remove inverted day-night cycle patterns, as well as
values that were statistically insignificant. Figure 9 shows appli-
cations of data cleaning on the example dataset. For more
details, please check codes that have been deposited on Zenodo
(https://doi.org/10.5281/zenodo.17158115, D'Agostino, 2025).
Ethics and consent
Ethical approval and consent were not required
Figure 8. Example of the charging effects on the data recordings.
Page 10 of 18
Open Research Europe 2025, 5:363 Last updated: 19 JUN 2026Open asset ↗Zenodo · 10.5281/zenodo.17158115pdf-raw-page:10 lines:1-58Supplement · publicavailability
The datasets and codes to analyze the data have been deposited
on Zenodo (https://doi.org/10.5281/zenodo.17158115, D'Agostino
(2025)).
Data are available under the terms of the Creative Commons
Zero v1.0 Universal
An additional explicative video for the psychrometer instal-
lation on leaves is available on Zenodo (https://doi.org/10.5281/zenodo.17510720, Degand et al. (2025)).
The author(s) declare that this video is released under the
Creative Commons CC0 1.0 Universal Public Domain Dedica-
tion. This means the video is free of all copyright restrictions
and may be copied, modified, distributed, and used without
permission, including for commercial purposes.
Data are availablOpen asset ↗Zenodo · 10.5281/zenodo.17510720pdf-raw-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become essential for optimizing water utilization and promoting healthy crop development. While existing machine learning methods have demonstrated efficacy, they often lack the adaptability required to accommodate the dynamic conditions of agricultural environments. Prior research has identified soil moisture and chlorophyll content as key indicators of plant health and stress, with conventional models relying on simplistic algorithms for stress prediction. However, these models exhibit limitations in scalability, adaptability and interpretability. To overcome these challenges, this study employed sparse additive models with learning (SAM-L) algorithms, integrated with explainable artificial intelligence (XAI), to provide a flexible and transparent solution. In this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content. The SAM-L algorithm is a machine learning method that focuses on sparsely selecting relevant features through additive models. It aims to enhance model interpretability while maintaining high prediction accuracy by learning sparse representations of input data. The SAM-L algorithm enhances interpretability while preserving high predictive accuracy by learning sparse feature representations from input data. Additionally, XAI was incorporated to ensure interpretable decision-making, enabling farmers and stakeholders to comprehend the rationale behind irrigation recommendations. The model's architecture incorporates a three-layer Long Short-Term Memory (LSTM) network to process sequential data effectively. The proposed framework achieved a high performance on publicly available dataset, yielding an overall accuracy of 89.2% on the multi-class classification task. Further analysis of the results across the three predefined stress categories (healthy, moderate stress, and high stress) revealed strong performance, with the model obtaining a macro F1-score of 0.88 and a macro recall of 0.88. The proposed framework not only can enhance prediction accuracy but also can promote sustainable farming practices by reducing water wastage and improving crop resilience.
Why it matches plant phenotyping methods植物ストレス状態を土壌水分とクロロフィルから推定するSAM-L・XAI・LSTM統合手法が研究の中心であり、植物の生理状態を対象とした計算的フェノタイピング手法に該当する。
abstractIn this paper, we proposed a novel framework that integrates SAM-L and XAI to predict plant stress using soil moisture and chlorophyll content.
Reproduction assets foundThe paper states its plant-stress phenotyping data came from a publicly available Kaggle dataset ('Real-Time Plant Health Insights: Simulated Biosensor Data for AI-Driven Monitoring'), used directly for the SAM-L/XAI stress-prediction experiments. Code is only available on request, so it does not qualify as a public,作者Dataset · publicThe dataset used in this study was sourced from Kaggle and was publicly available.Open asset ↗Kagglepdf-page:16 lines:1-70Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ABSTRACT As an essential species across European forests, Scots pine ( Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time‐consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single‐nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid‐to‐total‐chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water‐related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress‐related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype‐by‐environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate‐adaptive breeding in Scots pine.
Why it matches plant phenotyping methods針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。
abstracthyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code, Dataset · publicThe data supporting the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.27134907.v2 .Open asset ↗Figshare · 10.6084/m9.figshare.27134907.v2lines:454-598Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Carbonyl sulfide (COS) has been proposed as a proxy for gross primary production (GPP), as it is taken up by plants through a pathway comparable to that of CO2. COS diffuses into the leaf, where it undergoes an essentially one-way reaction in the mesophyll cells, irreversibly catalyzed by the enzyme carbonic anhydrase (CA), and is likely not respired by the leaf. In order to use COS as a proxy for GPP, the mechanisms of COS uptake and its coupling to photosynthesis need to be well understood. Characterizing the isotopic discrimination of COS during plant uptake could provide valuable information on the physiological COS uptake process and may help to constrain the COS budget. This study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O). A C3 plant, sunflower (Helianthus annuus), and a C4 plant, papyrus (Cyperus papyrus), were enclosed in a flow-through plant chamber and exposed to varying light levels. The incoming and outgoing gas compositions were measured online, and discrete air samples were taken for isotope analysis. Simultaneously measuring fluxes and isotope discrimination of both COS and CO2 yielded a unique dataset that includes information on the plant's behavior and allowed for the estimation of stomatal- and mesophyll conductances. The average COS uptake fluxes were 73.3 ± 1.5 pmol m−2 s−1 for sunflower and 107.3 ± 1.5 pmol m−2 s−1 for papyrus (PAR > 0) and displayed virtually no trend with increasing PAR from 200 to 600 µmol m−2 s−1. The mean observed 34Δ for COS was 3.4 ± 1.0 ‰ for sunflower and 2.6 ± 1.0 ‰ for papyrus. 34Δ was stable across all light intensities, which could be explained by a sufficient stomatal opening and low variability in the ratio of mesophyll vs. ambient COS mole fraction, CmS/CaS. For both C3 and C4 plants, for CO2, a negative relationship was observed between the uptake flux and the isotopic discriminations 13Δ and 18Δ. The CO2 uptake and 13CO2 and C16O18O discriminations of sunflower have expected values for a C3 plant, while the low CO2 flux and high 13Δ and 18Δ values observed for papyrus were not in the typical C4 range, which was perhaps due to the relatively low light conditions during our experiments.
Why it matches plant phenotyping methods植物のCOS・CO2取り込み、同位体識別、気孔・葉肉コンダクタンスをフロースルー植物チャンバーで定量する生理的表現型測定が研究の中心であり、再利用可能な測定データセットと推定手法を提示している。
abstractThis study presents joint measurements of isotope discrimination during plant uptake for COS (CO34S) and CO2 (13CO2 and C18O16O).
Reproduction assets foundThe paper's isotope discrimination and gas-exchange dataset from the flow-through chamber experiments is publicly deposited on Zenodo by the authors.Dataset · publicynthetically available radiation at the top of the chamber, 34 Δ is the discrimination against CO 34 S and LRU is the leaf relative uptake ratio.
* n =1 , error states is the single measurement precision instead of the repeatability precision.
Download Print Version | Download XLSX
Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Field / plotMultispectral / hyperspectralLeafClassificationPhysiological trait estimationWater status / transpiration
Abstract There is an urgent need for effective large-scale biodiversity monitoring across ecosystems, given the recent tendency toward global biodiversity loss. The assessment of plant spectral diversity offers a promising approach as it is intrinsically linked to phylogenetic and functional diversity. This study investigates the relationship between taxonomic, functional, and spectral diversity in temperate saltmarsh and seagrass ecosystems in the Gulf of Biscay. Using hyperspectral leaf reflectance data and functional traits from 19 plant species across four estuaries, these three dimensions of biodiversity were compared. The predictive power of spectral data was assessed for estimating biochemical and anatomical traits and species identification. Results reveal significant correlations between functional and spectral diversity, with species sharing similar functional traits exhibiting similar spectral signatures. Spectral diversity is significantly influenced by taxonomic classification, with higher taxonomic levels (e.g., order, class) explaining substantial part of the spectral variation. Spectral regions of 720–770 nm and 1330–1380 nm were important for species discrimination, achieving 98% accuracy. Partial least squares regression models successfully estimated functional traits (e.g., water content, carbon, phosphorus) with high precision in these environments. These findings demonstrate that spectral data can effectively capture taxonomic and functional diversity, offering an effective tool for large-scale biodiversity monitoring in estuarine ecosystems. This study underscores the potential of remote sensing to track biodiversity and ecosystem health, providing a foundation for future applications in conservation and management.
Why it matches plant phenotyping methods葉のハイパースペクトル反射データから機能形質を推定し、スペクトル手法の予測性能も評価しており、植物フェノタイピング手法が中心である。
abstractThe predictive power of spectral data was assessed for estimating biochemical and anatomical traits and species identification.
Reproduction assets foundThe authors explicitly state that the dataset produced and used by this work (hyperspectral leaf reflectance and functional trait measurements from 19 estuarine plant species) is available open-access via the IHCantabria DIES API. No author analysis code or trained models are mentioned.Dataset · publicThe dataset produced and used by this work is available open-access through the link https://apidies.ihcantabria.com/swagger/index.htmlOpen asset ↗apidies.ihcantabria.comlines:288-303Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation and detect signatures of local adaptation across populations. We combined hyperspectral data, inverse modeling, and network analysis to investigate population-level variation in Streptanthus tortuosus. Using a common garden experiment with four geographically distinct populations, we applied partial least square discriminant analysis (PLS-DA) and ridge regression for population discrimination, inverse PROSPECT modeling to estimate leaf biochemical traits, and canonical correlation analysis to examine trait-climate relationships across historical (1900-1994) and recent (1995-2024) periods. We developed a spectral network approach treating wavelength correlations as biologically meaningful trait networks. Populations showed distinct, heritable spectral signatures with high classification accuracy. Significant population differences emerged in anthocyanins, carotenoids, chlorophyll, and water content. Trait-climate correlations shifted between time periods, consistent with historical climate adaptation. Network analysis revealed population-specific integration patterns, with more variable environments displaying greater spectral modularity. Hyperspectral signatures provide a high-throughput tool for detecting population-level adaptation and trait coordination. Our findings provide a framework to investigate how plant populations respond to climate change through evolved shifts in trait networks rather than isolated traits alone.
Why it matches plant phenotyping methodsハイパースペクトル計測と逆モデリングを用いて葉の機能形質を推定し、集団間比較・適応評価を行う手法が研究の中心であるため。
abstractHyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw hyperspectral data and source code in a public GitHub repository, which is an allowed URL.Code · publicRR, JL; Formal Analysis:
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RR, JNM, TSM; Funding acquisition: JRG, JNM, TSM; Investigation: RR, JNM, TSM;
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Visualization: RR; Writing – original draft: RR; Writing – review & editing: RR, BQ-C, JL, SA,
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JRG, JNM, TSM
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Data availability
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Raw data and source code are available in the following Github repository.
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https://github.com/rishavray/spectral-network
559
References
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Albert R, Barabási A-L. 2002. Statistical mechanics of complex networks. Reviews of Modern
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Physics 74: 47–97.
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Anderson JT, DeMarche ML, Denney DA, Breckheimer I, Santangelo J, Wadgymar SM.
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2025. Adaptation and gene flow are insufficient to rescue a montane plant under climate change.
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ScieOpen asset ↗rishavray/spectral-networkpdf-raw-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Deep understanding of slow-wilting is essential for developing drought-tolerant crops. Existing approaches to measure transpiration rates are difficult to apply to large populations due to their high cost and low throughput. To overcome these challenges, we developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device. The system tracked the transpiration rate in real time by measuring changes in the pot weight in 224 recombinant inbred lines of Taekwangkong (fast-wilting) x SS2-2 (slow-wilting) under water-restricted conditions. Among five transpiration features we determined, stress recognition time point (SRTP) and decrease in transpiration rate by stress (DTrs) are informative parameters, that are interconnected and independently affect slow-wilting as well. Quantitative trait loci (QTL) for SRTP and DTrs were identified at the same location as the major QTL for slow wilting, qSW_Gm10 , identified in the previous study. Notably, we found a novel major QTL for DTrs, qDTrs_Gm04 , with a LOD value of 42 and PVE of 47 %. As a candidate gene for qDTrs_Gm04 , GmWRKY58 was selected with differential expression between the parental lines under drought conditions as well as upstream sequence variation. Our high-throughput system is of help not only to biological research but breeding programs of drought-tolerant lines.
Why it matches plant phenotyping methods高スループットなセンサー基盤を開発し、ポット重量変化からダイズの蒸散率・乾燥ストレス応答をリアルタイム抽出することが研究の中心であるため。
abstractwe developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device.
Reproduction assets foundThe paper's data availability statement explicitly deposits the processed phenotypic data (transpiration features from the RIL drought experiment) and trained Random Forest/XGBoost model objects on Figshare, which is a paper-specific, publicly accessible asset.Dataset · publicThe processed phenotypic data, along with the trained Random Forest and XGBoost machine learning model objects (.rds files), are publicly available on Figshare at https://doi.org/10.6084/m9.figshare.c.7951601.v1.Open asset ↗Figshare · 10.6084/m9.figshare.c.7951601.v1html-lines:276-299Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹林冠の3D再構成アルゴリズム開発と生理形質推定が明示されており、植物表現型取得法が研究の中心的要素である。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets generated during the study (phenotype/physiological measurements and 3D canopy reconstruction outputs) in a public GitHub repository with an authors' URL, making it a paper-specific, publicly actionable asset.Dataset · publicThe datasets generated during this study are available in the GitHub repository: https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025 .Open asset ↗https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025lines:270-306Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotRootMorphology / geometry measurementGrowth / time-series analysisRoot system architectureWater status / transpiration
Summary Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labor-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access-tubes and customized ingrowth-core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 meters, over five years. The less invasive studies using ingrowth-cores reached depths of 4.2 meters. Nutrient tracer 15N analysis showed marked differences in deep root activity among crop species. TDR sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analyzing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根の形質取得を目的とした圃場施設であり、ミニライゾトロン画像とAIパイプラインによる根形質解析が中心的に記述されているため、植物フェノタイピング基盤として収録する。
abstractThe facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific root phenotyping assets — raw minirhizotron images, RootPainter training datasets, manual counts, and trained segmentation models — in a public Zenodo repository (DOI 10.5281/zenodo.15213661), which is listed in allowed_urls.Dataset · publico be studied
in well-replicated studies. The facility is an ideal
platform for conducting studies at deep soil layers in
the field with a capacity for generating statistically
and biologically meaningful results.
Data availability
The raw images, training datasets, manual counts
and models generated after training are available
http://doi.org/10.5281/zenodo.15213661.Author contributions
EH prepared the manuscript and all co-authors
contributed to writing. EH trained the model for
automatic root segmentation and conducted all the
ingrowth-core experiments. CC installed the TDR
system and conducted the experiment. AGS created
the RootPainter software and provided technical
advice on trainingOpen asset ↗zenodo · 10.5281/zenodo.15213661pdf-raw-page:18 lines:1-107Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Remote sensing holds promise for ecosystem-level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field-based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory-based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of "space for time" approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress.
Why it matches plant phenotyping methods樹木の干ばつストレスを対象に、航空機VSWIR画像による含水量推定と水ポテンシャルとの関係を時空間的に検証しており、リモートセンシング手法の妥当性・限界評価が中心です。
abstractwater content (measured in situ and via repeat airborne VSWIR imaging)
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's data and analysis code on Zenodo (DOI 10.5281/zenodo.15110087), and the AVIRIS-NG reflectance data used for the canopy water content analysis is publicly archived at ORNL DAAC (DOI 10.3334/ORNLDAAC/2376). Both are paper-specific, public, and match Code · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:245-270Dataset · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:467-475Dataset · publicReflectance data was obtained from ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2376 .Open asset ↗ORNL DAAC · 10.3334/ORNLDAAC/2376lines:245-270Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Summary Leaf water loss after stomatal closure is key to understanding the effects of prolonged drought on vegetation. It is therefore important to accurately quantify such water losses to improve physiology‐based models of drought‐induced plant mortality. We measured water loss of detached leaves continuously during dehydration in nine woody angiosperm species. We computed minimum leaf conductance ( g min ) at different water potential thresholds along a sequence of physiological function losses, spanning from turgor loss point to hydraulic failure. A mechanistic model evaluated the impact of different g min estimations on the time to hydraulic failure (THF). Residual conductance is not steady and decreases continuously at varying rates across species during the entire dehydration process, even after correcting for leaf shrinkage and vapor pressure deficit shifts. Different estimations of g min had a significant impact on the THF predicted by the model, especially for drought‐resistant species. We demonstrate that residual conductance is variable during dehydration, and thus, it is important to use physiological or water status boundaries for its estimation in order to determine distinct g min values of water loss. We describe an accurate, repeatable and open‐source methodology to estimate g min . Such methodology could enhance models of plant mortality under drought.
Why it matches plant phenotyping methods葉の脱水過程における最小葉コンダクタンスの定量法を開発・評価し、反復可能な方法論として提示しているため、植物生理フェノタイピング手法が中心です。
abstractWe describe an accurate, repeatable and open‐source methodology to estimate g min .
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' analysis/acquisition code (gminComputation in Python, g_Residual in R, and the 'cuticular' acquisition software) hosted on a public Gitlab repository, and the manuscript's underlying dehydration/gmin measurement data on the法Code · publicCodes developed for data acquisition (software ‘cuticular’ for Windows) and computation of raw residual conductance (project ‘gminComputation’ is developed as a console version in python, and ‘g_Residual’ is a script written in R language) are available in the following public Gitlab repository: https://gitub.u‐bordeaux.fr/phenoboisOpen asset ↗https://gitub.u‐bordeaux.fr/phenoboislines:509-550Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / 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 6 Sept 2026
Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物のマルチスペクトル・ハイパースペクトル画像と葉の水分状態・化学形質を含む評価用データセットを構築しており、フェノタイピング手法開発のためのベンチマークが中心である。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper's multispectral images, hyperspectral reflectance, and trait measurements (weight, chlorophyll, nitrogen, fuel moisture) are publicly deposited on Figshare with an explicit DOI. The authors' sample Matlab code is included within that dataset. The MicaSense imageprocessing repository is a generic third-party工具Dataset · publicAll the data is available at this repository DOI: https://doi.org/10.6084/m9.figshare.26950660.v2.Open asset ↗figshare · 10.6084/m9.figshare.26950660.v2pdf-page:14 lines:1-35Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Mungbean is an important sub‐tropical legume crop grown across Asia, Africa, and Australia. Yield improvement is crucial for expanding production, but phenotyping important traits across diverse environments using current approaches is challenging, limiting the scale and complexity of information captured. High‐throughput phenotyping platforms offer a solution by rapidly screening traits at scale. This study deploys an unmanned aerial vehicle (UAV) platform to determine the potential of phenotyping a range of agronomic and physiological traits within a diverse mungbean population evaluated across three field trials. Three predictive data‐driven modeling approaches were undertaken to evaluate performance accuracy in predicting these traits: linear regression, stepwise regression, and partial least squares regression. Results show that using the geometric trait “coverage” as a proxy is most suitable for screening visual traits like early vigor. For functional traits (i.e., aboveground biomass), predictive data‐driven models demonstrate high accuracy during early‐ and mid‐canopy development stages ( R 2 0.79, root mean square error [RMSE] 4.08 and R 2 0.8, RMSE 26.92, respectively), but accuracy declines in late‐canopy development ( R 2 0.33 and RMSE 43.15). Prediction accuracy can be optimized by using different modeling approaches at different stages during the transition from early‐ to mid‐canopy development as well as canopy closure. Similar findings were observed when examining the prediction models for the physiological trait, stomatal conductance ( R 2 0.69 and RMSE 0.10). These approaches are expected to enable breeders and researchers to incorporate UAV‐based phenotyping systems into mungbean improvement programs. Such approaches might be most efficiently used at scale if applied as part of a “real‐time” calibration approach.
Why it matches plant phenotyping methodsUAVプラットフォームと予測モデルにより、マングビーンの農業・生理形質を推定し、精度を評価することが研究の中心である。
abstractThis study deploys an unmanned aerial vehicle (UAV) platform to determine the potential of phenotyping a range of agronomic and physiological traits within a diverse mungbean population evaluated across three field trials.
Reproduction assets foundThe article's Data Availability Statement points to a public UQ eSpace deposit (DOI 10.48610/20cffed.O) containing the paper's UAV phenotyping dataset. No author analysis code or trained model checkpoints are explicitly deposited; the Supporting Information is only generically referenced.Dataset · publicy The University of
Queensland, as part of the Wiley - The University of Queens-
land agreement via the Council of Australian University
Librarians.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
This dataset is available at UQ eSpace: https://doi.org/10.48610/20cffed.O RC I D
ShaniceVanHaeften https://orcid.org/0000-0003-0412-3457
Daniel Smith https://orcid.org/0000-0002-5867-9613
HannahRobinson https://orcid.org/0000-0002-8303-8076
CaitlinDudley https://orcid.org/0000-0001-5297-7487
YichenKang https://orcid.org/0000-0002-3613-7426
LeeT. Hickey https://orcid.org/0000-0001-6909-7101
Andries PoOpen asset ↗UQ eSpace · 10.48610/20cffed.Opdf-raw-page:15 lines:1-87Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Investigating greenhouse gases (GHGs) and water flux dynamics within the soil–plant–atmosphere interphase is key for understanding ecosystem functioning, as they reflect the ecosystem's responses to environmental changes. Understanding these responses is essential for developing sustainable agricultural systems that can help to adapt to global challenges such as increased drought. Typically, an initial understanding of GHGs and water flux dynamics is gained through laboratory or greenhouse pot experiments, where gas exchange is often measured using commercially available manual closed-chamber (leaf) systems. However, these systems are rather expensive and often labor-intensive, thus limiting the number of different treatments and their repetitions that can be studied. Here, we present a fully automatic, low-cost (EUR 2 and evapotranspiration (ET) fluxes. It can operate in two modes: an independent and a dependent measurement mode. The independent measurement mode utilizes low-cost NDIR (non-dispersive infrared) CO 2 (K30 FR) and relative humidity (SHT31) sensors, thus making each greenhouse coffin a fully independent measurement device. The dependent measurement mode connects multiple greenhouse coffins via a low-cost multiplexer (EUR 2 O, CH 4 and stable isotopes). In both modes, CO 2 and ET fluxes are determined through the respective concentration increase during closure time. We tested both modes and demonstrated that the presented system is able to deliver precise and accurate CO 2 and ET flux measurements using low-cost sensors, with an emphasis on calibrating the sensors to improve measurement precision. By connecting multiple greenhouse coffins via our low-cost multiplexer to a single infrared gas analyzer in the dependent mode, we could additionally show that the system can efficiently measure CO 2 and ET fluxes in a high temporal resolution across various treatments with both labor and cost efficiency. Therefore, the developed system is expected to be a valuable tool for conducting greenhouse experiments, enabling comprehensive testing of plant–soil dynamic responses to various treatments and conditions.
Why it matches plant phenotyping methods低コストセンサーと自動閉鎖チャンバーによる植物・土壌系のCO2および蒸発散フラックス測定システムを開発・検証しており、測定手法自体が中心である。
abstractHere, we present a fully automatic, low-cost
Reproduction assets foundThe paper's CO2/ET flux measurements and Arduino analysis/control code are publicly deposited on Bonares (ZALF), explicitly stated in the Code and data availability section and the reference list.Dataset · publicy those with a high level of complexity (e.g., mesocosm experiment), allowing for holistic assessment of the dynamic responses of plants to various treatments and conditions while significantly reducing the required cost and labor.
Code and data availability
The data and code referred to in this study are publicly accessible at https://doi.org/10.4228/ZALF-JG04-HV79 (Al Hamwi et al., 2024).
Author contributions
MH, WA, and MD conceptualized and developed the system and codes. WA carried out the sealing and validation experiments. WA, MH, MD, and JS wrote and prepared the paper with contributions from all co-authors. All authors reviewed and agreed to the final version of the paper.
CompetiOpen asset ↗10.4228/ZALF-JG04-HV79lines:279-306Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant net photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites. The final dataset (https://doi.org/10.5281/zenodo.12191876; Zahn and Bou-Zeid, 2024) spans a 5-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing confidence in these estimates. Partitioning of CO2 components, on the other hand, was found to be less robust and more dependent on prior knowledge of water use efficiency. This highlights some limitations of these present methods that we discuss, emphasizing the broader challenge posed by the lack of an accurate reference method to validate against. Despite these limitations, this dataset has several potential applications, especially in addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods複数手法で蒸散・植物純光合成などの植物生理フラックスを分離推定し、手法間比較と妥当性・限界評価を行った大規模データセットであり、植物状態の取得手法が中心である。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites.
Reproduction assets foundThe paper's flux-partitioning dataset (transpiration, evaporation, plant photosynthesis, soil respiration across 47 NEON sites) is publicly deposited on Zenodo, and the authors' scripts implementing all five partitioning methods are also publicly available on Zenodo with explicit availability statements.Dataset · publicl. ( 2020 ) across FLUXNET sites. By comparing different algorithms, we can further explore their uncertainties and focus on model improvement. Finally, as more data become available, other options can be used to train machine learning algorithms, focusing on gap-filling.
7 Code and data availability
The dataset is available at https://doi.org/10.5281/zenodo.12191876 ( Zahn and Bou-Zeid , 2024 ) . In addition to all the flux components, it contains the auxiliary meteorological inputs used to implement the Extreme Gradient Boosting algorithm for gap-filling and feature importance analysis. The scripts used to implement all five partitioning methods can be found at https://doi.org/10.5281/zenOpen asset ↗Zenodo · 10.5281/zenodo.12191876lines:346-361Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Net photosynthesis (AN) is a major component of the global carbon cycle, with significant feedback to decadal-scale climate change. Although plant acclimation to environmental changes can modify AN, traditional vegetation models in Earth System Models (ESMs) often rely on plant functional type (PFT)-specific parameter calibrations or simplified acclimation assumptions, both of which lacked generalizability across time, space and PFTs. In this study, we propose a differentiable photosynthesis model to learn the environmental dependencies of Vc,max25, as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of Vc,max25, learning the environment dependencies of key photosynthetic parameters improves model spatiotemporal generalizability. Applying environmental acclimation to Vc,max25 led to substantial variation in global mean AN, calling for the attention to acclimation in ESMs. The model effectively captured multivariate observations (Vcmax25, stomatal conductance gs, and AN) simultaneously and, in fact, multivariate constraints further improved model generalization across space and PFTs. It also learned sensible acclimation relationships of Vc,max25 to different environmental conditions. The model explained more than 54%, 57% and 62% of the variance of AN, gs, and Vcmax25, respectively, presenting a first global-scale spatial test benchmark of AN and gs. These results highlight the potential of differentiable modeling to enhanced process-based modules in ESMs and effectively leverage information from large, multivariate datasets.
Why it matches plant phenotyping methods植物の光合成・気孔コンダクタンス等の生理形質を推定する微分可能な物理情報機械学習モデルを開発し、観測データで検証・ベンチマークしており、フェノタイピング手法が中心である。
abstractwe propose a differentiable photosynthesis model to learn the environmental dependencies of Vc,max25
Reproduction assets foundThe Open Research section states the differentiable photosynthesis model code is publicly available on Zenodo, and the leaf gas exchange databases (Knauer et al. 2018; Lin et al. 2015) and NGEE-Tropics leaf gas exchange datasets (Jardine et al. 2020; Rogers et al. 2022) used for the photosynthesis phenotyping analysis,Code · public611differentiable photosynthesis modelcodeisavailableathttps://zenodo.org/records/8067204whileOpen asset ↗Zenodo · 8067204pdf-page:27 lines:1-32Dataset · public608[https://ngee-tropics.lbl.gov/research/data/]. Observations of Vc,max25 wereobtainedfrom(Alietal.,Open asset ↗NGEE-Tropicspdf-page:27 lines:1-32Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf-level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6-year-old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%-98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%-23% of spectral variation within populations and 14%-48% of the variation across all populations. Second, we found gene by environment interactions leading to population-specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change.
Why it matches plant phenotyping methods葉のハイパースペクトル反射を用いて遺伝型、クロロフィル、水分量、光合成効率などの植物形質・生理状態を推定し、精度評価と環境間比較を行っており、フェノタイピング手法の適用が中心です。
abstractLeaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels.
Reproduction assets foundThe paper's leaf hyperspectral reflectance data (the core phenotyping measurements) are publicly deposited in EcoSIS via an explicit data availability statement with DOI. No author analysis code repository is stated; R package references (vegan, prospectr) are generic libraries, not paper-specific assets.Dataset · publicThe data that support the findings of this study are available from EcoSIS at https://doi.org/10.21232/9bbY8fVJ .Open asset ↗EcoSIS · 10.21232/9bbY8fVJlines:291-351Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recent research has shown that optimizing photosynthetic and stomatal traits holds promise for improved crop performance. However, standard phenotyping tools such as gas exchange systems have limited throughput. In this work, a novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes. Using the dual-imaging methods and traditional approaches, we found broad and significant variation in key traits, including photosynthetic CO2 uptake at saturating light and ambient CO2 concentration (Asat), photosynthetic CO2 uptake at saturating light and elevated CO2 concentration (Amax), the maximum velocity of Rubisco for carboxylation (Vcmax), time for stomatal opening (Ki), and leaf evaporative cooling. Anatomical analysis revealed significant variation in flag leaf adaxial stomatal density. Associations between traits highlighted significant relationships between leaf evaporative cooling, leaf stomatal conductance, and Fq'/Fm', highlighting the importance of stomatal conductance and stomatal rapidity in maintaining optimal leaf temperature for photosynthesis in wheat. Additionally, gsmin and gsmax were positively associated, indicating that potential combinations of preferable traits (i.e. inherently high gsmax, low Ki, and maintained leaf evaporative cooling) are present in wheat. This work highlights the effectiveness of thermal imaging in screening dynamic gs in a panel of wheat genotypes. The wide phenotypic variation observed suggested the presence of exploitable genetic variability in bread wheat for dynamic stomatal conductance traits and photosynthetic capacity for targeted optimization within future breeding programmes.
Why it matches plant phenotyping methods特注ガス交換チャンバーと熱画像を組み合わせた動的な気孔コンダクタンス・光合成形質の取得手法を開発し、複数のコムギ遺伝子型で実証しているため、植物フェノタイピング手法が中心です。
abstracta novel approach based on a bespoke gas exchange chamber allowing combined measurement of the quantum yield of PSII (Fq'/Fm'), with an estimation of stomatal conductance via thermal imaging was used to phenotype a range of bread wheat (Triticum aestivum L.) genotypes.
Reproduction assets foundThe paper's Data Availability statement points to a public Dryad repository containing the raw phenotyping data (photosynthesis and stomatal kinetics measurements) for this study, matching an allowed URL. Supplementary datasets S1–S2 are calculation spreadsheets but no standalone public URL is given for them beyond theDataset · publicRaw data can be accessed from the Dryad Digital Repository ( Faralli et al. , 2024 ) ( https://doi.org/10.5061/dryad.79cnp5j4d ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.79cnp5j4dlines:117-171Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationBiomass / plant weightWater status / transpiration
Abstract This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel‐1 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV‐polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH‐polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel‐1 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.
Why it matches plant phenotyping methodsSentinel-1後方散乱をAquaCropへ同化し、作物バイオマスを推定・独立データで評価する手法が研究の中心であり、植物形質の取得・推定方法を実質的に検証している。
abstractThe objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates.
Reproduction assets foundThe Data Availability Statement points to the authors' public GitHub repository containing the water cloud model (WCM) calibration scripts used in this paper's Sentinel-1 backscatter forward-operator analysis. Other listed resources (AquaCrop source, MERRA-2, HWSD, CORINE, SMAP, VODsmap) are generic model code or thirdCode · publiccode into LIS has not yet been officially released, but the AquaCrop source code
can be found on the FAO website, https://www.fao.org/aquacrop/en/. The following repository includes the
generic crop file and management file; https://doi.org/10.1002/2014MS000330. The water cloud model (WCM)
calibration scripts can be found here: https://github.com/KUL‐RSDA/obs_operator_calibration. All data that
were used for model input and evaluation are freely available online. Please visit the following links for data
access. MERRA‐2 variables: https://disc.gsfc.nasa.gov/datasets?project=MERRA‐2(last access: 1 Jan 2022,
Global Modeling and Assimilation office, 2015a, https://doi.org/10.5067/VJAFPLI1CSIV, Open asset ↗KUL‐RSDA/obs_operator_calibrationpdf-layout-page:26 lines:1-26Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods植物・土壌フラックスを分離推定する5手法を47地点で実装し、手法間の一致度を評価した長期データセットであり、植物の生理状態（蒸散・光合成）の取得・推定法が中心的です。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites.
Reproduction assets foundThe paper's five-year NEON flux-partitioning dataset and the authors' partitioning-method scripts are explicitly deposited on Zenodo with public DOIs.Dataset · publicows the availability
of flux components as a fraction of the total number of half-
hour periods in the record. Overall, all the methods cover
a similar temporal distribution of flux partitioning and are
potential candidates for ensemble averaging.
4 Description of the final dataset
The final dataset is available for download at
https://doi.org/10.5281/zenodo.12191876 (Zahn and Bou-
Zeid, 2024). It is organized into different folders for each
site, with each site containing a .csv file for each method.
This format is selected to be user-friendly and accessible in
various programming languages and software packages. For
FVS and CECw, in addition to their ensemble averages for
https://doi.org/Open asset ↗Zenodo · 10.5281/zenodo.12191876pdf-raw-page:9 lines:136-149Code · publicnthesis, transpiration and stomatal conduc-
tance: potential and limitations, Plant Cell Environ., 35, 657–
667, https://doi.org/10.1111/j.1365-3040.2011.02451.x, 2011.
Zahn, E.: einaraz/PartitioningMethods: Processing Eddy-
Covariance Data: Five Evapotranspiration Flux Parti-
tioning Methods (v1.0.1) [Software], Zenodo [code],
https://doi.org/10.5281/zenodo.11510363, 2024.
Zahn, E. and Bou-Zeid, E.: Partitioning of water and CO2 fluxes at
NEON sites into soil and plant components: a five-year dataset
for spatial and temporal analysis [dataset], Zenodo [data set],
https://doi.org/10.5281/zenodo.12191876, 2024.
Zahn, E., Chor, T. L., and Dias, N. L.: A Simple Methodology for
Quality ControlOpen asset ↗Zenodo · 10.5281/zenodo.11510363pdf-raw-page:22 lines:1-58Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.
Why it matches plant phenotyping methodsジャガイモの水欠乏状態をハイパースペクトル画像と深層学習で識別し、性能評価および重要波長の同定を行っており、植物ストレス表現型の取得・抽出手法が中心である。
abstractHyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the pre-processed hyperspectral dataset on Zenodo and the authors' analysis code on GitHub, both with public URLs matching allowed_urls. The SiaPy Zenodo record is a generic open-source library, not a paper-specific asset.Code · publicand code at https://github.com/janezlapajne/manuscripts (accessed on 8 July 2024)Open asset ↗github · janezlapajne/manuscriptslines:104-424Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Automating stomatal trait measurement has gained popularity because of their inherent importance for field phenotyping application as stomata are critical for both carbon capture and water use efficiency in plants. Such tool has been reported for rice, wheat, tomato, barley and oil palm. However, none exist yet for canola, which is an important economic and agronomic crop globally. Results We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8). Digital micrographs of leaf surfaces enter the SCAN pipeline, which includes stomata detection, stomata segmentation and stomatal pore segmentation models, to measure stomatal density, stomatal size and stomatal pore area, respectively. In addition to SCAN’s ability to measure leaf stomatal traits in canola at 89 to 94% accuracy, we also showed that SCAN can be used to predict stomatal density even in species not included in the training set such as Arabidopsis, tobacco, rice, wheat, maize and proso millet. SCAN was designed for the biological science community with the premise that users are not required to possess advanced programming capabilities to manage dependency prerequisites, execute the models, and integrate the analysis. This was achieved by packaging the models into a desktop application system that can be accessed offline. Conclusion Overall, SCAN provides a non-destructive, real-time, portable, and high-throughput measurement of leaf stomatal traits in canola. The minimised hardware requirement and user-friendly desktop application system make SCAN suitable for field phenotyping application.
Why it matches plant phenotyping methodsカノーラ葉の気孔形質を画像と機械学習で自動抽出するツールを開発し、精度検証と他種での適用性評価を行っており、フェノタイピング手法が中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN by combining the use of high-resolution portable digital microscopy with machine learning based on You Only Look Once algorithm (YOLOv8).
Reproduction assets foundThe paper's authors publicly deposit the SCAN pipeline's model weights, hyperparameters, training scripts, and datasets in the FD_detection GitHub repository, and provide the SCAN application itself (with download and demonstration) in a second GitHub repository. Both are paper-specific, public, and actionable.Code · publicin Table S1.
123
124
The training tasks were carried out on an Ubuntu 20.04 Linux server at the Research School of Biology in
125
Australian National University, using two Nvidia A30 (24G) Graphic Processing Units (GPUs). The full
126
details of models’ weights, hyperparameters, training scripts and datasets can be found at
127
https://github.com/William-Yao0993/FD_detection.128
129
Model evaluation
130
131
Mean Average Precision (mAP, Fig. 3) and F1 score were used to assess model ability (Fig. 4). mAP is
132
calculated as the mean value of each class area under the precision-recall curve over thresholds, and the
133
F1 score is the harmonic mean of precision and recall. The formulas are deOpen asset ↗William-Yao0993/FD_detectionpdf-raw-page:4 lines:1-81Code / 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 confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Introduction: is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods: We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results: The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion: The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
Why it matches plant phenotyping methods日次の非破壊画像計測を用いて成長関連形質とWUEを時系列で抽出し、早期表現型を解析しており、画像ベースの植物表現型取得が研究の主要な方法として記述されている。
abstractWe conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment.
Reproduction assets foundThe paper's image-derived phenotypic measurements and analysis tables (accession list with phenotypic data, germination data, heritability, trait rankings, SNP-trait correlations, candidate genes) are stated to be included in the article's Supplementary Materials, publicly available at the Frontiers supplementary URL. Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2024.1278802/full#supplementary-material
Supplementary File S1
Table of Bioenergy Association Panel accessions used in this study (adapted from Brenton et al., 2016 ) with image-derived phenotypic data.
Supplementary File S2
Heatmap of a kinship matrix showing correlation analysis among the 369 BAP accessions. The coloOpen asset ↗lines:229-258Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Poikilohydric plants respond to hydration by undergoing dry-wet-dry cycles. Carbon balance represents the net gain or loss of carbon from each cycle. Here we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration. Methods and results We applied the carbon balance technique to capture responses to water stress in populations of the moss Syntrichia caninervis , comparing 19 associated physiological variables. Carbon balance was negative in desiccation-acclimated (field-collected) mosses, which exhibited large respiratory losses. Contrastingly, carbon balance was positive in hydration-acclimated (lab-cultivated) mosses, which began exhibiting net carbon uptake Conclusions Carbon balance is a functional trait indicative of physiological performance, hydration stress, and survival in poikilohydric plants, and the carbon balance method can be applied broadly across taxa to test hypotheses related to environmental stress and global change.
Why it matches plant phenotyping methodsコケ植物の炭素収支を測定する標準プロトコルとカスタムチャンバーを開発し、炭素収支を機能形質として評価しているため、植物フェノタイピング手法が中心である。
abstractHere we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration.
Reproduction assets foundThe authors deposit all case-study data and analysis materials in a public GitHub repository, explicitly stated in the Data Availability Statement. The R Markdown/R analysis workflow (Appendix S3) and supporting files are also provided, making the paper's carbon-balance phenotyping data and computational analysis code,Code · publiclability Statement
A detailed carbon balance protocol, RMD file, custom chamber baseplate data files, and standard curve data are available in the Supporting Information for this manuscript. All other data used in the manuscript, including in the hydration‐acclimation case study, are available via the public GitHub repository ( https://github.com/KirstenKCoe/Coe-et-al.-2024-APPS ).Open asset ↗KirstenKCoe/Coe-et-al.-2024-APPSlines:474-476Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at and available for local use in a containerized environment at , this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tool’s utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research. Plain language summary StomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate.
Why it matches plant phenotyping methods気孔数、孔サイズ、閉鎖率などの植物形質を画像から自動抽出するツールの開発・提供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。
abstractStomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate.
Reproduction assets foundThe authors publicly release their StomaVision source code, trained YOLOv7-seg model, and all labeled stomata images on GitHub, plus a public Streamlit web portal for stomatal trait analysis. Cited datasets (Dryad/LeafNet, Cuticle Database) and generic libraries (VDP, Detectron2, Ultralytics, Label Studio) are prior/thCode · publicl for advancing our understanding of stomatal behavior,
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particularly in an era in which plant resilience and adaptation are of paramount
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concern.
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Data Availability
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The source code, trained model, user installation and training guideline, and all the
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labeled images of leaf stomata are available at
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https://github.com/YaoChengLab/StomaVision. The web portal of extracting stomatal
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traits is available at https://stomavision.streamlit.app/.850
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Author Contributions
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TLW, PYC, XD, PLC, and YCL conceived the research. TLW, JYO, PXZ, YLW, RHW,
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TCH, CYL, and YCL conducted the field and growth chamber experiments. TLW,
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JYO, PXZ, YLW, and RHW produceOpen asset ↗YaoChengLab/StomaVisionpdf-raw-page:27 lines:1-65Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p 0.22, p
Why it matches plant phenotyping methods作物を対象に、塔載観測によるPRI・SIFを用いたGPP推定関係の改善と検証を主題としており、植物の生理状態推定手法が中心である。
titleAssessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean
Reproduction assets foundThe paper's tower-based SIF, PRI, and GPP measurements for corn and soybean at US-Ne2/US-Ne3 derive from an openly downloadable ORNL DAAC dataset (ds_id=2136), explicitly cited in the Data Availability Statement. No author analysis code or models are disclosed.Dataset · publicResearch and Development Program of
China, grant number 2021YFC2600501; the National Natural Science Foundation of China, grant
number SKLNBC2023-01.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset used in this study can be downloaded at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2136 (accessed on 1 January 2024).
Acknowledgments: We appreciate the open-access dataset supported by Wu from the Agroecosystem
Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at
Urbana-Champaign, Urbana, IL, USA.
Conflicts of Interest: The authors declare no conflictsOpen asset ↗daac.ornl.gov · ds_id=2136pdf-raw-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSunflowerField / plotLaboratory / benchtopStem / branchPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
There is currently a need for inexpensive, continuous, non-destructive water potential measurements at high temporal resolution ( Helianthus annuus ) (petioles and stems) and a monocotyledon ( Zea mays ) species (stems) for 1 week during dehydration and re-watering treatments under laboratory conditions. We also demonstrated the ability of the device to record branch and trunk diameter variation of a woody dicotyledon ( Rhus typhina ) in the field. Under laboratory conditions, we compared our device (hereafter 'contact' dendrometer) with modified versions of another open-source dendrometer (the 'optical' dendrometer). Overall, contact and optical dendrometers were well aligned with one another, with Pearson correlation coefficients ranging from 0.77 to 0.97. Both dendrometer devices were well aligned with direct measurements of xylem water potential, with calibration curves exhibiting significant non-linearity, especially at water potentials near the point of incipient plasmolysis, with pseudo R 2 values (Efron) ranging from 0.89 to 0.99. Overall, both dendrometers were comparable and provided sufficient resolution to detect subtle differences in stem water potential (ca. 50 kPa) resulting from light-induced changes in transpiration, vapour pressure deficit and drying/wetting soils. All hardware designs, alternative configurations, software and build instructions for the contact dendrometers are provided.
Why it matches plant phenotyping methods安価なオープンソース樹幹径計を開発し、水ポテンシャルと茎径成長を高時間・空間分解能で測定する手法を比較検証しており、植物表現型取得が研究の中心である。
titleDevelopment and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all sensor software, 3D prints, photos, and schematics in a public GitHub repository, and the dendrometer measurement data are provided as a CSV in the supplementary information (no allowed URL for the SI itself). The GitHub repository is a paper-specific,公开,可Code · publicins, CO 80526, USA.
Data Availability
All data used in this study are available for download from the supplemental information as a CSV file (sup_data_all_dendro.csv). All software needed for operating sensors, 3D prints, photos, and schematics have been included in the SI materials, and can also be freely accessed via github ( https://github.com/sean-gl/dendrometer_water_potential_device ).
Sources of Funding
J.J.S. was supported by an NSF Postdoctoral Research Fellowship in Biology, Grant No. IOS-1907338.
Contributions by the Authors
All authors contributed meaningfully to the manuscript. S.M.G., J.J.S., B.A., S.K.P. and J.M. designed the experiment and collected the data. S.M.G. wrote theOpen asset ↗https://github.com/sean-gl/dendrometer_water_potential_devicelines:224-283Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
CottonGreenhouseThermalLeafClassificationPhotosynthesis / fluorescenceStress response / toleranceWater status / transpiration
Introduction Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. Methods In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Results Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Discussion Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.
Why it matches plant phenotyping methods葉の熱画像からマスクと熱特徴量を抽出し、機械学習で水分状態・乾燥処理を判定する画像解析パイプラインが中心であり、植物表現型の取得・推定手法に該当する。
abstractOur approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicSupplementary Table S3
Single measurements of volumetric soil water content across all collected images.Open asset ↗lines:440-465Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldLeaf traitsPigment / colour / senescenceWater status / transpiration
Mediterranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. Optical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. However, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. Thus, the objective of the SENTHYMED/MEDOAK experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi-platform sensors having different spectral characteristics and spatial resolutions. Seven monthly data collections were performed between April and October 2021 (with a complementary one in June 2023) over two forests in the north of Montpellier, France, comprised of two oak endemic species with different phenological dynamics (evergreen: Quercus ilex and deciduous: Quercus pubescens ) and a variability of canopy cover fractions (from dense to open canopy). These collections were coincident with satellite multispectral Sentinel-2 data and one with airborne hyperspectral AVIRIS-Next Generation data. In addition, satellite hyperspectral PRISMA and DESIS were also available for some dates. All these airborne and satellite data are provided from free online download websites. Eight datasets are presented in this paper from thirteen studied forest plots: (1) overstory and understory inventory, (2) 687 canopy plant area index from Li-COR plant canopy analyzers, (3) 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from ASD spectroradiometers, (4) 92 soil moistures and temperatures from IMKO and Campbell probes, (5) 747 leaf-clip optical data from SPAD and DUALEX sensors, (6) 2594 in-lab leaf directional-hemispherical reflectances and transmittances from ASD spectroradiometer coupled with an integrating sphere, (7) 747 in-lab measured leaf water and dry matter content, and additional leaf traits by inversion of the PROSPECT model and (8) UAV-borne LiDAR 3-D point clouds. These datasets can be useful for multi-scale and multi-temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. Other targeted applications can be forest 3-D modelling, biodiversity assessment, fire risk prevention and globally vegetation monitoring.
Why it matches plant phenotyping methods森林の葉からキャノピーまでの植物形質データとマルチプラットフォーム光学・LiDARデータを体系的に整備し、衛星植生形質プロダクトの較正・検証に用いるデータセット研究であり、形質取得と再利用可能な検証基盤が中心です。
titleMulti-scale datasets for monitoring Mediterranean oak forests from optical remote sensing during the SENTHYMED/MEDOAK experiment in the north of Montpellier (France).
Reproduction assets foundThis Data in Brief article deposits the paper's own SENTHYMED/MEDOAK plant-phenotyping measurements (forest inventory, canopy plant area index, forest/leaf optical properties, soil moisture, leaf-clip sensor data, leaf traits, UAV-borne LiDAR point clouds) in the public SEDOO repository with explicit DOIs and a direct,Dataset · publicat https://eoweb.dlr.de/egp/ (image rasters, .tif for GeoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
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Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UOpen asset ↗SEDOO · 10.6096/8005lines:31-86Dataset · publicoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.154Open asset ↗SEDOO · 10.6096/8007lines:31-86Dataset · publicTHEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to aOpen asset ↗SEDOO · 10.6096/8006lines:31-86Dataset · publicoduct/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/Open asset ↗SEDOO · 10.6096/8001lines:31-86Dataset · publiceoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the daOpen asset ↗SEDOO · 10.6096/8002lines:31-86Dataset · public//doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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Value of the Data
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These datasets were collected to provide calibratioOpen asset ↗SEDOO · 10.15454/AGBW7Glines:31-86Dataset · publictical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
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These datasets were collected to provide calibration/validation data for methods aimiOpen asset ↗SEDOO · 10.15454/DMYWPBlines:31-86Dataset · publicoisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
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These datasets were collected to provide calibration/validation data for methods aiming at linking ground observations on Mediterranean forests withOpen asset ↗SEDOOlines:31-86Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MaizeLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsWater status / transpiration
Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R 2 =0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R 2 =0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R 2 =0.83) with actual canopy measurements. The iPhone’s surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R 2 =0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This Template The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com .
Why it matches plant phenotyping methodsiPhoneのLiDARと3D点群を用いてトウモロコシの葉・植物表面積を推定する手法を開発・検証しており、植物形質の取得が研究の中心である。
abstractThis study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays).
Reproduction assets foundThe paper states that all statistical code and data files for the maize 3D leaf phenotyping analysis are publicly available in the authors' GitHub repository.Code · publicconducted using the “scipy” package’s “f_oneway”
12 function [18]. The Python packages “pandas” [19] and “numpy” [20] were used to arrange the
13 data before plotting. The Python packages “matplotlib”, “seaborn” [21] were used for data
14 visualization. All statistical code and data files needed are available to download
15 at https://github.com/gavrielbs/3D_Corn_Phenotype.
16
17 PlantArray System by Plant-DiTech LTD
18 PlantArray is a high-throughput, multi-sensor physiological phenotyping gravimetric
19 platform. This plant phenotyping system performs quick plant screening based on precise
20 physiology traits measurements that are great indicators for yield potential with proven high
21 cOpen asset ↗gavrielbs/3D_Corn_Phenotypepdf-layout-page:4 lines:1-44Code / dataset availability confirmedCrossref · checked 7 Sept 2026
The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path.
Why it matches plant phenotyping methodsハイパースペクトル由来の2種類の正規化差分指標を組み合わせ、作物の成長・老化過程や生育段階を時系列で推定する手法が研究の中心であるため。
abstractBy using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only.
Reproduction assets foundThe paper's analysis is based on the publicly available GHISA EO-1 Hyperion hyperspectral dataset from USGS, explicitly named in the Data Availability Statement. No author analysis code or trained models are deposited.Dataset · publicData Availability Statement: The data are publicly available at https://www.usgs.gov/media/files/
ghisa-usa-eo-1-hyperion-dataset (accessed on 14 November 2023).Open asset ↗ghisa-usa-eo-1-hyperion-datasetpdf-page:13 lines:1-56Code / 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 · bioRxiv · checked 7 Sept 2026
Field / plotLeafStomata / guard-cell complexPhysiological trait estimation2D/3D reconstructionPhotosynthesis / fluorescenceStomatal traitsWater status / transpiration
ABSTRACT Premise of the study The adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima ( Sida fallax ). Key results Coastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO 2 conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface. Conclusions This is the first direct experimental evidence that amphistomy per se increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO 2 to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい生理的測定法を開発し、複数集団で比較検証しており、表現型取得が研究の中心である。
abstractWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the custom analysis scripts for this study's amphistomy advantage measurements. Raw data are only promised for future Dryad deposit (not yet available), so only the code asset qualifies.Code · publicCustom scripts are available on a GitHub repository (https://github.com/cdmuir/stomata-ilima) and will
be archived on Zenodo with a DOI and stable URL upon publication.Open asset ↗cdmuir/stomata-ilimapdf-page:16 lines:1-52Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
Why it matches plant phenotyping methodsロボット化した屋内フェノタイピングによる適応形質の高スループット測定と、圃場条件への推定・整合性評価が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
abstractRobotized indoor phenotyping allows measuring such traits at high throughput for speed breeding
Reproduction assets foundThe paper's Data availability statement deposits its phenotypic/genotypic datasets (diversity panel, genetic progress panel, recent hybrids panel) on Recherche Data Gouv with three public DOIs. These are paper-specific public phenotype datasets directly reproducing the study's measurements. The PhenoArch platform page,Dataset · publicThe datasets for phenotypic and genotypic values for the diversity panel are available at https://doi.org/10.15454/IASSTN .Open asset ↗10.15454/IASSTNlines:186-234Dataset · publicThe datasets for phenotypic and genotypic values for the genetic progress panel are available at https://doi.org/10.15454/KLD0GH .Open asset ↗10.15454/KLD0GHlines:186-234Dataset · publicThe dataset for the ‘recent hybrid’ panel is available at https://doi.org/10.57745/NZY1KL .Open asset ↗10.57745/NZY1KLlines:186-234Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves, which is important for photosynthesis. Previous deep learning-based plant stomata detection methods are based on horizontal detection. The detection anchor boxes of deep learning model are horizontal, while the angle of stomata is randomized, so it is not possible to calculate stomata traits directly from the detection anchor boxes. Additional processing of image (e.g., rotating image) is required before detecting stomata and calculating stomata traits. This paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time. Simultaneously, the stomata conductance loss function is introduced in the DeepRSD model training, which improves the efficiency of stomata detection and conductance calculation. The experimental results demonstrate that the DeepRSD model reaches 94.3% recognition accuracy for stomata of maize leaf. The proposed method can help researchers conduct large-scale studies on stomata morphology, structure, and stomata conductance models.
Why it matches plant phenotyping methods植物葉の気孔を対象に、回転を考慮した検出と気孔形質・コンダクタンス算出を一体化する手法を開発しており、フェノタイピング手法が中心である。
abstractThis paper proposes a novel approach, named DeepRSD (deep learning-based rotating stomata detection), for detecting rotating stomata and calculating stomata basic traits at the same time.
Reproduction assets foundThe paper's DeepRSD stomata detection and conductance calculation code is explicitly stated to be publicly hosted on GitHub at the authors' repository URL. No public dataset of the 2,192 maize stomata images is mentioned.Code · publicThe code of anchor-free stomata detection and stomata conductance calculation has been hosted to GitHub and is available at https://github.com/sswangbo159357/Rotating-stomata-detection .Open asset ↗sswangbo159357/Rotating-stomata-detectionlines:288-533Code / dataset availability confirmedCrossref · checked 14 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Abstract Background and aims Root distribution over the soil profile is important for crop resource uptake. Using machine learning (ML), this study investigated whether measured square root of planar root length density (Sqrt_pRLD) at different soil depths were related to uptake of isotope tracer (15N) and drought stress indicator (13C) in wheat, to reveal root function. Methods In the RadiMax semi-field root-screening facility 95 winter wheat genotypes were phenotyped for root growth in 2018 and 120 genotypes in 2019. Using the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network. We developed ML models to explore whether the Sqrt_pRLD estimates at different soil depths were predictive of the uptake of deep soil nitrogen - using deep placement of 15N tracer as well as natural abundance of 13C isotope. We analyzed the correlations to tracer levels to both a parametrized root depth estimation and an ML approach. We further analyzed the genotypic effects on root function using mediation analysis. Results Both parametrized and ML models demonstrated clear correlations between Sqrt_pRLD distribution and resource uptake. Further, both models demonstrated that deep roots at approx. 150 to 170 cm depth were most important for explaining the plant content of 15N and 13C isotopes. The correlations were higher in 2018. Conclusions The results demonstrated that, parametrized models and ML-based analysis provided complementary insight into the importance of deep rooting for water and nitrogen uptake.
Why it matches plant phenotyping methods深根画像をCNNで解析して根長密度を抽出し、機械学習による根形質推定と技術的解析を行っており、表現型取得・抽出法が研究の中心である。
abstractUsing the minirhizotron technique, root images were acquired across a depth range from 80 to 250 cm in May, June, and July and RL was extracted using a convolutional neural network.
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 · publicCodes and data are available on GitHub at https://github.com/satyasaran/CropML.git .Open asset ↗satyasaran/CropML · CropMLlines:182-216Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.
Why it matches plant phenotyping methods高スループット表現型解析のための低コスト灌水・水利用測定システムを開発・実証しており、植物の水利用と乾燥ストレス関連形質の取得が中心的な方法論的貢献である。
abstractThis study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping.
Reproduction assets foundThe article's Data availability statement places the study's original contributions (phenotype measurements and supplementary experiment data) in the article/Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, models, or standalone phenotypeSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1201102/full#supplementary-material
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References
Araya Y. N.
Gowing D. J.
Dise N.
( 2010 ).
A controlled water-table depth system toOpen asset ↗lines:288-364Code / 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
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Declarations
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Ethics approval and consent to participate
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Not applicable.
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Consent for publication
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Not applicable.
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,
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https://doi.org/10.5281/zenodo.8033640)
755
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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.,
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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.
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Funding
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This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 14 Sept 2026
Abstract Nondestructive plant phenotyping is fundamental for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-through phenotyping facilities can further our understanding of plant development and stress responses, their high costs significantly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration. We paired these devices with a suite of computational pipelines for integrated and straightforward data analysis. We validated the suitability of our system for large screens by evaluating a cowpea diversity panel for responses to drought stress. The observed natural variation was subsequently used for Genome-Wide Association Study, where we identified nine genetic loci that putatively contribute to cowpea drought resilience during early vegetative development. We validated the homologs of the identified candidate genes in Arabidopsis using available mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と計算パイプラインを開発し、植物成長・蒸発散を測定するフェノタイピングシステムとして検証しており、手法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration.
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 · public142 how to build and program the device can be found at https://github.com/ok84-star/AAWSMO. DetailsOpen asset ↗ok84-star/AAWSMOpdf-page:6 lines:1-42Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
GreenhouseWhole plant / canopy / plot / fieldTrackingGrowth / development / phenologyWater status / transpiration
The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms. In this paper, we systematically compare the performance of the AlphaGarden to professional horticulturalists on the staff of the UC Berkeley Oxford Tract Greenhouse. The humans and the machine tend side-by-side polyculture gardens with the same seed arrangement. We compare performance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%. Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison.
Why it matches plant phenotyping methods自動栽培プラットフォームに植物フェノタイピング・追跡アルゴリズムが組み込まれ、キャノピー被覆率や植物多様性を用いて性能比較しているため、フェノタイピング手法の実質的な応用・評価が中心的です。
abstractThe AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms.
Reproduction assets foundThe paper explicitly states that code, videos, and datasets from the AlphaGarden vs. horticulturalist comparison (including time-lapse data from 120 days of physical experiments) are publicly available at the authors' project site.Code · publicormance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%.
Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison .
I Introduction
In 1950, Alan Turing considered the question “Can Machines Think?” and proposed a test based on comparing human vs. machine ability to answer questions.
In this paper, we consider the question “Can Machines Garden?” based on comparing human vs. machine ability to tend a real polyculture gardenOpen asset ↗lines:1-83Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Plant hydraulic conductivity and its decline under water stress are the focal point of current plant hydraulic research. The common methods of measuring hydraulic conductivity control a pressure gradient to push water through plant samples, submitting them to conditions far away from those that are experienced in nature where flow is suction driven and determined by the leaf water demand. In this paper, we present two methods for measuring hydraulic conductivity under closer to natural conditions, an artificial plant setup and a horizontal syringe pump setup. Both approaches use suction to pull water through a plant sample while dynamically monitoring the flow rate and pressure gradients. The syringe setup presented here allows for controlling and rapidly changing flow and pressure conditions, enabling experimental assessment of rapid plant hydraulic responses to water stress. The setup also allows quantification of dynamic changes in water storage of plant samples. Our tests demonstrate that the syringe pump setup can reproduce hydraulic conductivity values measured using the current standard method based on pushing water under above-atmospheric pressure. Surprisingly, using both the traditional and our new syringe pump setup, we found a positive correlation between changes in flow rate and hydraulic conductivity. Moreover, when flow or pressure conditions were changed rapidly, we found substantial contributions to flow by dynamic and largely reversible changes in the water storage of plant samples. Although the measurements can be performed under sub-atmospheric pressures, it is not possible to subject the samples to negative pressures due to the presence of gas bubbles near the valves and pressure sensors. Regardless, this setup allows for unprecedented insights into the interplay between pressure, flow rate, hydraulic conductivity and water storage in plant segments. This work was performed using an Open Science approach with the original data and analysis to be found at https://doi.org/10.5281/zenodo.7322605.
Why it matches plant phenotyping methods植物セグメントの水理伝導度と水貯蔵変化を自然条件に近く測定する新規セットアップを開発・検証しており、植物生理状態の取得手法が研究の中心である。
abstractIn this paper, we present two methods for measuring hydraulic conductivity under closer to natural conditions, an artificial plant setup and a horizontal syringe pump setup.
Reproduction assets foundThe paper explicitly states that all original data (hydraulic conductivity, flow, and pressure measurements) and the authors' analysis code are publicly available in a Zenodo deposit, cited twice (abstract and Data Availability statement).Dataset · publicAll data and analysis code is available at https://doi.org/10.5281/zenodo.7322605 .Open asset ↗Zenodo · 10.5281/zenodo.7322605lines:124-182Code / dataset availability confirmedCrossref · checked 8 Sept 2026
AppleArabidopsisLaboratory / benchtopMultispectral / hyperspectralLeafTissueClassificationObject detectionWater status / transpiration
Abstract Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana , and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Why it matches plant phenotyping methods植物組織培養におけるハイパーヒドリシティという植物状態を、分光計測と機械学習で自動検出・分類する手法を開発し、別のハイパースペクトル画像取得で妥当性検証しているため。
abstractThe objective of this study was to characterize optical features to allow an automated detection of HH.
Reproduction assets foundThe paper's RGB image dataset of hyperhydric in vitro explants (used for CNN-based HH detection) is publicly available on Roboflow, explicitly stated in the Data availability section and cited as Bethge (2023). Spectral datasets and trained spectral classifier are only available on request.Dataset · publicRGB image dataset analysed during the current study available in the Bethge ( 2023 ) repository, [ https://universe.roboflow.com/hains/hh-detection-in-vitro/dataset/8 ].Open asset ↗Roboflow · hh-detection-in-vitrolines:203-234Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
PotatoMRI / PETTissueClassificationWater status / transpiration
Magnetic Resonance Imaging is a powerful non-destructive tool in the study of plant tissues. For potato tubers, it greatly assists the study of tissue defects and tissue evolution during storage. This paper describes the MRI analysis of potato tubers with internal defects in their flesh tissue at eight sampling dates from 14 to 33 weeks after harvest. Spatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers. Six classes with statistically different relaxation parameters were identified at each sampling date, allowing the defects and the pith and cortex tissues to be detected. A further distinction could be made between three constitutive elements within the flesh, revealing the heterogeneity of this particular tissue. Relaxation parameters for each class and their evolution during storage were successfully analyzed. The work demonstrated the value of MRI for detailed non-invasive plant tissue characterization.
Why it matches plant phenotyping methodsMRIと空間化T2緩和解析を用いて、ジャガイモ塊茎の組織・内部欠陥を非破壊で分類・特性評価する方法が研究の中心であり、植物器官の状態を直接推定している。
abstractSpatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers.
Reproduction assets foundThe paper's MRI T2 relaxometry data (potato tuber images and relaxation measurements) are openly deposited in a public repository (Recherche Data Gouv, DOI 10.57745/DR2GSS), as stated in the Data Availability Statement. The supplementary material contains only result figures, not datasets or code; no analysis code or模型Dataset · publicThe MRI data presented in this study are openly available at: https://doi.org/10.57745/DR2GSS (accessed on 29 January 2023).Open asset ↗10.57745/DR2GSSlines:388-401Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers ( Capsicum annuum var. annuum ); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R 2 values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses.
Why it matches plant phenotyping methods作物の生体重と葉面積を非破壊推定する計測システムの開発が中心であり、画像とCNNによる形質抽出および検証を行っている。
abstractTherefore, a non-destructive measurement method with high versatility is essential.
Reproduction assets foundThe paper's Supplementary Materials (hosted by MDPI at the allowed URL) explicitly contain sample crop images used as phenotyping inputs, trained-model validation results, model architectures, training parameters, and leaf-area regression coefficients — directly reproducing this paper's fresh-weight and leaf-area phenySupplement · publicowth can be found in the raw data containing changes in the image and weight. Therefore, a monitoring system that can collect both factors can be widely applied for data analyses, such as machine learning, crop modeling, and data standardization.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s22207728/s1 , Figure S1: Sample images collected from the camera. Images were cropped and resized into 128 × 128, and the resized images were augmented using flipping and shifting; Figure S2: Validation accuracies of the trained deep learning models for estimating the calculated fresh weight.; Figure S3: Validation accuracy of theOpen asset ↗lines:129-147Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Plants are complex organisms subject to variable environmental conditions, which influence their physiology and phenotype dynamically. We propose to interpret plants as reservoirs in physical reservoir computing. The physical reservoir computing paradigm originates from computer science; instead of relying on Boolean circuits to perform computations, any substrate that exhibits complex non-linear and temporal dynamics can serve as a computing element. Here, we present the first application of physical reservoir computing with plants. In addition to investigating classical benchmark tasks, we show that Fragaria × ananassa (strawberry) plants can solve environmental and eco-physiological tasks using only eight leaf thickness sensors. Although the results indicate that plants are not suitable for general-purpose computation but are well-suited for eco-physiological tasks such as photosynthetic rate and transpiration rate. Having the means to investigate the information processing by plants improves quantification and understanding of integrative plant responses to dynamic changes in their environment. This first demonstration of physical reservoir computing with plants is key for transitioning towards a holistic view of phenotyping and early stress detection in precision agriculture applications since physical reservoir computing enables us to analyse plant responses in a general way: environmental changes are processed by plants to optimise their phenotype.
Why it matches plant phenotyping methods植物の葉厚センサーを用いた物理リザバーコンピューティングを提案・実証し、光合成速度や蒸散速度などの生理形質推定とストレス早期検出への応用を中心に扱うため、植物フェノタイピング手法として適格。
abstractHere, we present the first application of physical reservoir computing with plants.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated/analysed (leaf thickness sensor traces, environmental variables, gas exchange data) on Zenodo and the analysis data/code on a public GitHub repository, both with exact URLs matching allowed_urls.Dataset · publicDatasets generated and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4264624 .Open asset ↗Zenodo · 10.5281/zenodo.4264624lines:153-237Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
O_LIMore than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. C_LIO_LIUsing 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. C_LIO_LIOur pressed-leaf models were best at estimating LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514-0.790; %RMSE = 12.8-19.6). Remaining elements were predicted poorly (R2 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. C_LIO_LIThese results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the worlds preserved plant collections. C_LI
Why it matches plant phenotyping methods押葉の反射分光から葉の機能形質を非破壊推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractwe used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits
Reproduction assets foundThe paper's fresh-leaf spectral dataset is publicly available via the CABO data portal. Pressed/ground spectra, trait data, and PLSR/PLS-DA models are only promised 'upon publication' to EcoSIS and EcoSML, so those require contacting the authors; no author analysis code with a public URL is stated.Dataset · publicAll fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).Open asset ↗CABO data portalpdf-page:24 lines:1-32Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB's latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m 2 compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB's modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management. Supplementary information The online version contains supplementary material available at 10.1007/s00271-022-00787-x.
Why it matches plant phenotyping methodsリモートセンシングによる3SEBモデルを用いてブドウ樹・被覆作物の蒸発散分離を推定し、渦相関測定および既存モデルと比較検証している。水利用管理への応用を含むが、植物の生理状態推定手法の技術評価が中心である。
abstracta remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California's Central Valley to investigate whether it improves the depiction of the grapevine-interrow system.
Reproduction assets foundThe paper applies the authors' 3SEB model to vineyard ET partitioning and explicitly points to the authors' public GitHub repository as the model source code. No public phenotype/trait datasets or trained models are deposited; the GRAPEX flux/radiometric data are described but no public URL is given.Code · publicRefer to Burchard-Levine et al. ( 2022 ) or the source code ( https://github.com/VicenteBurchard/3SEB ) for model details and specifications.Open asset ↗VicenteBurchard/3SEBlines:112-122Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals ( R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.
Why it matches plant phenotyping methods衛星データから作物形質を推定するGPR retrievalモデルと、GEE上での実装・検証ワークフローが研究の中心であり、植物形質フェノタイピング手法に該当する。
abstractwe implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe GEE codes to run the EBD-GPR models and display the vegetation maps of this study is hosted on the repository https://github.com/esjoal/GEE_GPR_mapping_vegetation .Open asset ↗esjoal/GEE_GPR_mapping_vegetationlines:222-231Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
MaizeSorghumMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
Lack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants. Efficient and non-destructive hyperspectral imaging can quantify plant physiological traits under abiotic stresses; however, prediction models generally are developed for few genotypes of one species, limiting the broader applications of this technology. Therefore, the objective of this research was to explore the possibility of developing cross-species models to predict physiological traits (relative water content and nitrogen content) based on hyperspectral reflectance through partial least square regression for three genotypes of sorghum (Sorghum bicolor (L.) Moench) and six genotypes of corn (Zea mays L.) under varying water and nitrogen treatments. Multi-species models were predictive for the relative water content of sorghum and corn (R2 = 0.809), as well as for the nitrogen content of sorghum and corn (R2 = 0.637). Reflectances at 506, 535, 583, 627, 652, 694, 722, and 964 nm were responsive to changes in the relative water content, while the reflectances at 486, 521, 625, 680, 699, and 754 nm were responsive to changes in the nitrogen content. High-throughput hyperspectral imaging can be used to predict physiological status of plants across genotypes and some similar species with acceptable accuracy.
Why it matches plant phenotyping methodsハイスループット hyperspectral imaging と回帰モデルにより、植物の相対含水量・窒素含量という生理形質を非破壊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。
abstractLack of high-throughput phenotyping is a bottleneck to breeding for abiotic stress tolerance in crop plants.
Reproduction assets foundThe paper's hyperspectral reflectance measurements, RWC/NC ground-reference trait data, and metadata are explicitly deposited in the Purdue University Research Repository (PURR) with a public URL stated in the Data Availability Statement. The MDPI supplement contains only stepwise regression tables, model evaluation, VDataset · publicData and meta-data are available at The Purdue University Research Repository (PURR), https://purr.purdue.edu/publications/3958/1 (accessed on 30 January 2022).Open asset ↗The Purdue University Research Repository (PURR)lines:222-238Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.
Why it matches plant phenotyping methodsSARデータとGPRモデルにより作物のPAI・VWC・湿重量バイオマスを推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThe purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the authors' GPR analysis code for crop biophysical parameter retrieval from RADARSAT-2 data. The in situ SMAPVEX16-MB measurements and RADARSAT-2 imagery themselves are not stated as publicly released by the authors.Code · publicData Availability Statement: The code for the present work is available at: https://github.com/
Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2, accessed 15 February 2022.Open asset ↗Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2pdf-page:24 lines:1-60Code / 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 confirmedCrossref · checked 13 Sept 2026
Abstract A key impediment to studying water-related mechanisms in plants is the inability to non-invasively image water fluxes in cells at high temporal and spatial resolution. Here, we report that Raman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions. Raman imaging of water-transporting xylem vessels in Arabidopsis thaliana mutant roots reveals faster xylem water transport in endodermal diffusion barrier mutants. Furthermore, transverse line scans across the root suggest water transported via the root xylem does not re-enter outer root tissues nor the surrounding soil when en-route to shoot tissues if endodermal diffusion barriers are intact, thereby separating ‘two water worlds’.
Why it matches plant phenotyping methodsRaman顕微分光と流体力学モデリングを組み合わせ、根組織内の水輸送を非侵襲・細胞解像度で測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。
abstractRaman microspectroscopy, complemented by hydrodynamic modelling, can achieve this goal - monitoring hydrodynamics within living root tissues at cell- and sub-second-scale resolutions.
Reproduction assets foundThe paper's MECHA 3D solute advection-diffusion model and MATLAB inverse modeling code are openly available on GitHub under a GPL.2 license. Other deposits (Raman data, hydraulic conductivity data, custom RMS analysis code on FigShare) exist but their URLs are not in the allowed list, so only the GitHub code asset is aCode · publicThe latest code of MECHA working in 3D with solute advection-diffusion and associated Matlab codes for inverse modeling schemes are openly available online under GPL.2 open-source licence at FigShare [10.6084/m9.figshare.14892408.v2] or GitHub [ https://github.com/MECHARoot/MECHA/blob/master/MECHA_4Dsolute.zip ].Open asset ↗MECHARoot/MECHA · MECHA_4Dsolute.ziplines:111-143Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
Why it matches plant phenotyping methodsジャガイモの水ストレス表現型を取得するための非侵襲的イメージング・生理計測手法と装置構成が研究の中心であり、単なる生物学的測定ではない。
abstractThere is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Reproduction assets foundThe paper's MRI phenotyping data (3D images of potato tubers in pots under water deficit) are openly deposited in Data INRAE with an explicit DOI, as stated in the Availability of data and materials section. No author analysis code or trained models are reported.Dataset · publicThe MRI data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ ) repository at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/SFAXAA ).Open asset ↗Data INRAE · doi:10.15454/SFAXAAlines:160-172Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationWater status / transpiration
Root hydraulic properties play a central role in the global water cycle, in agricultural systems productivity, and in ecosystem survival as they impact the canopy water supply. However, the existing experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging, and their applicability to thin roots and small root segments is limited. Therefore, there is a gap in methods enabling easy estimations of root hydraulic conductivities in diverse root types. Here, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes. Shortly, free-hand root cross-sections were used to extract a selected number of key anatomical traits. We used these traits to parametrize the Generator of Root Anatomy in R (GRANAR) model to simulate root anatomical networks. Finally, we used these generated anatomical networks within the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities ( k x and k r , respectively). Using this combination of anatomical data and computational models, we were able to create a root hydraulic conductivity atlas at the root system level, for 14-day-old pot-grown Zea mays (maize) plants of the var. B73 . The altas highlights the significant functional variations along and between different root types. For instance, predicted variations of radial conductivity along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem vessels. Differences in anatomical traits along and across root types generated substantial variations in radial and axial conductivities estimated with our novel approach. Our methodological pipeline combines anatomical data and computational models to turn root cross-section images into a detailed hydraulic atlas. It is an inexpensive, fast, and easily applicable investigation tool for root hydraulics that complements existing complex experimental methods. It opens the way to high-throughput studies on the functional importance of root types in plant hydraulics, especially if combined with novel phenotyping techniques such as laser ablation tomography.
Why it matches plant phenotyping methods根の断面画像と計算モデルを組み合わせ、根の解剖形質から油圧伝導性を推定する新規パイプラインを開発しており、植物表現型の取得・推定手法が研究の中心である。
abstractHere, we present a new pipeline to quickly estimate root hydraulic conductivities across different root types, at high resolution along root axes.
Reproduction assets foundThe paper provides two paper-specific public assets: the GRANAR–MECHA coupling workflow (Jupyter notebook via Binder, GitHub repo HeymansAdrien/GranarMecha, Zenodo DOI 10.5281/zenodo.4316762) and the Rmarkdown script plus all input/output data used to compute the B73 root hydraulic atlas (GitHub repo granar/B73_HydraulCode · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code · publicThe whole script that was used to compute the root hydraulic atlas from the root anatomical measurement is presented as a Rmarkdown script stored in a GitHub repository ( https://github.com/granar/B73_HydraulicMap doi: https://doi.org/10.5281/zenodo.4320861 ). All input and output data of this study are stored in the same repository.Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861lines:267-339Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.
Why it matches plant phenotyping methods植物の生理形質を取得・解析するFPP-FM統合フレームワークを開発し、トマト集団の蒸散測定で実証しており、表現型取得と解析手法が中心である。
abstractHere, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM).
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 · publicWe have coded all statistical algorithms that build our framework into a user-friendly R package for public use ( https://github.com/FFP-FM/Version1 ).Open asset ↗FFP-FM/Version1lines:232-250Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurements of these trace gas fluxes require enclosing of shoots in closed non-steady-state chambers. Due to plant physiological activity, this type of enclosure, however, leads to CO2 depletion in the enclosed air volume, condensation of transpired water, and warming of the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gas fluxes over extended periods, capturing diurnal and seasonal variations, and linking trace gas exchange to plant physiological functioning and environmental drivers. Initial measurements show daytime CH4 emissions of two pine shoots of 0.056 and 0.089 nmol per gram of foliage dry weight (d.w.) per hour or 7.80 and 13.1 nmolm-2h-1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmolm-2h-1, and transpiration rates were 1.24 and 0.90 molm-2h-1. Concurrent measurement of VOC emissions demonstrated that potential effects of spectral interferences on CH4 flux measurements were at least 10-fold smaller than the measured CH4 fluxes. Overall, this new system solves multiple technical problems that have so far prevented automated plant shoot trace gas flux measurements and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
Why it matches plant phenotyping methods植物シュートからの微量ガス・VOCフラックスを自動・連続測定する装置を開発し、技術的課題を解決しているため、植物生理状態の取得方法が研究の中心である。
abstractHere, we present ShoTGa-FluMS (SHOot Trace Gas FLUx Measurement System), a novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots.
Reproduction assets foundThe paper's Code and data availability section states that raw measurement data and the analysis script are deposited on Zenodo (doi 10.5281/zenodo.4609836) and that the custom control software (Koppi/koppismear) is publicly available on Bitbucket. Both are paper-specific, public, and actionable.Dataset · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ).Open asset ↗Zenodo · 10.5281/zenodo.4609836lines:435-476Code · publicRaw measurement data and the analysis script are available at Zenodo ( https://doi.org/10.5281/zenodo.4609836 ; Kohl et al. , 2021 ). The software used to operate both systems is available online at https://bitbucket.org/makoskinen/koppismear/ ( Koskinen , 2021 ) .Open asset ↗Bitbucket · koppismearlines:435-476Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman’s rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key Message Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.
Why it matches plant phenotyping methodsNIRスペクトルとSVMを用いて、トウモロコシ種子の加工中水分含量を高スループットかつ定量的に推定する方法の開発・検証が研究の中心であり、育種規模への適用も示している。
abstractThis study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels.
Reproduction assets foundThe paper's Code Availability section states all analysis code is publicly available on GitHub at the HirschLabUMN ML_Moisture_Prediction repository, which is an allowed URL. This is the authors' code for the NIR/machine-learning moisture prediction analysis. No separate public phenotype dataset deposit is explicitly aCode · publicCode Availability
All code is publicly available on GitHub at https://github.com/HirschLabUMN/ML_Moisture_Prediction.Open asset ↗HirschLabUMN/ML_Moisture_Predictionpdf-page:15 lines:1-55Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially, depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is, therefore, crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for parameterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model–data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with data sets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; collectively AMSR-E). VOD is proportional to vegetation water content and, therefore, closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere–Land Exchange Inverse (ALEXI) model is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by plant functional types (PFTs) improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model–data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.
Why it matches plant phenotyping methodsモデル・データ融合とマイクロ波リモートセンシングにより、全球規模の植物水理形質を推定し、独立した地上測定データと比較検証しているため、植物形質の取得・推定法が中心です。
abstractHere, we use a model–data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe.
Reproduction assets foundThe paper's retrieved global plant hydraulic trait maps are publicly available on Figshare, and the authors' plant hydraulic model and model–data fusion code are on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of the used plant hydraulic model and the model–data fusion algorithm is available at https://github.com/YanlanLiu/VOD_hydraulics ( Liu et al. , 2020 b ) .Open asset ↗GitHub · YanlanLiu/VOD_hydraulicslines:521-550Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R 2 p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs. CORE IDEAS A low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava roots Plant breeding-relevant cross-validation schemes were used for predictions High prediction accuracies were achieved for cassava root dry matter content A spectral region predictive of plant water content was identified as important
Why it matches plant phenotyping methodsカッサバ根の乾物含量という植物形質を対象に、低コスト携帯型NIR分光計と予測モデルを開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava.
Reproduction assets foundThe paper's raw RDMC and NIR spectral data (21,044 SCiO scans paired with oven-dry RDMC from 10 Nigerian field trials) are publicly deposited on Cyverse under the GoreLab shared directory, as stated in the Data Availability section. The analysis R code on GitHub (GoreLab/CassavaNIRS) is paper-specific but its URL is anDataset · publicRaw RDMC and spectral data are available for download on Cyverse atOpen asset ↗Cyversepdf-page:20 lines:1-54Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO 2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Why it matches plant phenotyping methods顕微鏡動画から個々の気孔を追跡し、セグメンテーションで気孔開口面積を定量化する観測・解析システムを開発しており、植物表現型取得が研究の中心です。
abstracta stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods.
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 · public(2) The module is easy to install with the aid of an executable program (EXE) ( https://github.com/shem123456/Stomata-segmentation-with-GUI ).Open asset ↗https://github.com/shem123456/Stomata-segmentation-with-GUIlines:61-68Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurementsof these trace gas fluxes require enclosing of shoots in closed non-steady state chambers. Due to plant physiological activity, this type of enclosures, however, lead to CO2 depletion in the enclosed air volume, condensation of transpired water, and warmingof the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present PlasTraGAS, ab novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gasfluxes over extended periods, capturing diurnal and seasonal variations and linking trace gas exchange to plant physiologicalfunctioning and environmental drivers. Initial measurements show daytime CH4 emissions two pine shoots of 0.056 and 0.089 nmol g−1 foliage d.w.h−1or 7.80 and 13.1 nmol m−2 h−1. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmol m−2 sec−1 and transpiration rates of 1.24 and 0.90 mol m−2 h−1. Concurrent measurement of VOC emissionsdemonstrated that potential effects of spectral interferences on CH4 flux measurements were at least ten-fold smaller than themeasured CH4 fluxes. Overall, this new system solves multiple technical problems that so far prevented automated plant shoottrace gas flux measurements, and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.
Why it matches plant phenotyping methods植物シュートからのガスフラックスを連続・自動測定する新規システムの開発が中心であり、植物の生理状態に関わる測定法を提供している。
abstractHere, we present PlasTraGAS, ab novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots.
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 software used to operate both systems is available online at
https://bitbucket.org/makoskinen/koppismear/ (Koskinen, 2021).Open asset ↗Bitbucket · makoskinen/koppismearpdf-page:14 lines:1-64Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Root water uptake by plants is a vital process that influences terrestrial energy, water, and carbon exchanges. At the soil, vegetation, and atmosphere interfaces, root water uptake and solar radiation predominantly regulate the dynamics and health of vegetation growth, which can be remotely monitored by satellites, using the soil–plant relationship proxy – solar-induced chlorophyll fluorescence. However, most current canopy photosynthesis and fluorescence models do not account for root water uptake, which compromises their applications under water-stressed conditions. To address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere. The coupled model was evaluated with field measurements of maize and grass canopies. The results indicated that the simulation of land surface fluxes was significantly improved by the coupled model, especially when the canopy experienced moderate water stress. This finding highlights the importance of enhanced soil heat and moisture transfer, as well as dynamic root growth, on simulating ecosystem functioning.
Why it matches plant phenotyping methods植物の光合成・蛍光などの生理状態を推定する結合モデルを開発し、トウモロコシおよび草本キャノピーで評価しており、モデル開発と検証が研究の中心です。
abstractTo address this limitation, this study integrated photosynthesis, fluorescence emission, and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model and a resistance scheme linking soil, roots, leaves, and the atmosphere.
Reproduction assets foundThe paper's Code and data availability section explicitly archives the exact STEMMUS–SCOPE model version on Zenodo and publishes the Yangling eddy-covariance validation dataset on 4TU, both with public DOIs.Dataset · publicdoi.org/10.5281/zenodo.3839092 , Wang et al., 2020). The original source of the SCOPE model and STEMMUS model was obtained from Van der Tol et al. (2009) and Zeng et al. (2011a, b), respectively. The tower-based eddy-covariance measurements used for model validation were provided by the authors for the Yangling station, China ( https://doi.org/10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7 , Wang et al., 2019), and were obtained from the FLUXNET2015 Dataset and PLUMBER2 program for the Vaira Ranch (US-Var) FLUXNET site.
Author contributions
YW, YZ, HC, and ZS designed the study. YW developed the code, conducted the analysis, and wrote the paper. YW and HC collected and shared their eddy-cOpen asset ↗10.4121/uuid:aa0ed483-701e-4ba0-b7b0-674695f5f7a7lines:657-684Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Field / plotRaman / spectroscopyLeafWhole plant / canopy / plot / fieldObject detectionCalibration / preprocessingGrowth / development / phenologyLeaf traitsWater status / transpiration
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.
Why it matches plant phenotyping methods葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。
abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
Reproduction assets foundThe paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.Dataset · publicumption. The number of
390
replicates is indicated for each dataset. Data processing and statistical analyses were all
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatOpen asset ↗Dryad · 10.5061/dryad.gtht76hkxpdf-raw-page:17 lines:1-49Dataset · publicall
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
392
individual wavelengths; see Results and figure captions for details. Scripts and source data
393
are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
394
spectroradiometer data are also deposited in SPECCHIO
395
(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
396
be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
397
Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C),
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‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descriOpen asset ↗SPECCHIOpdf-raw-page:17 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Hyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits, which has been transferred from remote to proximal sensing applications, and from manual laboratory setups to automated plant phenotyping platforms. Due to the higher resolution in proximal sensing, illumination variation and plant geometry result in increased non-biological variation in plant spectra that may mask subtle biological differences. Here, a better understanding of spectral measurements for proximal sensing and their application to study drought, developmental and diurnal responses was acquired in a drought case study of maize grown in a greenhouse phenotyping platform with a hyperspectral imaging setup. The use of brightness classification to reduce the illumination-induced non-biological variation is demonstrated, and allowed the detection of diurnal, developmental and early drought-induced changes in maize reflectance and physiology. Diurnal changes in transpiration rate and vapor pressure deficit were significantly correlated with red and red-edge reflectance. Drought-induced changes in effective quantum yield and water potential were accurately predicted using partial least squares regression and the newly developed Water Potential Index 2, respectively. The prediction accuracy of hyperspectral indices and partial least squares regression were similar, as long as a strong relationship between the physiological trait and reflectance was present. This demonstrates that current hyperspectral processing approaches can be used in automated plant phenotyping platforms to monitor physiological traits with a high temporal resolution.
Why it matches plant phenotyping methods近接ハイパースペクトル画像を用いた植物生理形質の非破壊フェノタイピング手法を扱い、照明変動補正、形質予測、プラットフォーム適用を技術的に検証しているため、方法が中心である。
abstractHyperspectral imaging is a promising tool for non-destructive phenotyping of plant physiological traits
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 Table 2
The relationship between relative reflectance and physiological traits.Open asset ↗lines:646-777Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.
Why it matches plant phenotyping methodsUAVのRGB・熱画像から植生指数と水ストレス指標を抽出し、目視スコアとの相関や遺伝率を評価する高スループット表現型解析が研究の中心です。
abstractwe evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score
Reproduction assets foundThe authors state their phenotyping data (UAV RGB/thermal-derived vegetation indices, breeder scores, and related measurements) are publicly available on Zenodo under DOI 10.5281/zenodo.4415643. This is a paper-specific, public, directly actionable dataset. No author analysis code repository is disclosed; the Matlab/CHDataset · publicData Availability Statement: Data is publicly available at 10.5281/zenodo.4415643.Open asset ↗Zenodo · 10.5281/zenodo.4415643pdf-raw-page:19 lines:1-46Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is therefore crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for paramaterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model-data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with datasets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band JAXA Advanced Microwave Scanning Radiometer for EOS (AMSR-E). VOD is proportional to vegetation water content and therefore closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere Land-Exchange Inverse model (ALEXI) is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by PFTs improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model-data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.
Why it matches plant phenotyping methodsモデルデータ融合とマイクロ波リモートセンシングを用いて、全球規模で植物の水理形質を推定・検証する方法が研究の中心であり、単なる生態系モニタリングではない。
abstractHere, we use a model-data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe.
Reproduction assets foundThe paper's retrieved global plant hydraulic trait maps are publicly deposited on Figshare, and the authors' plant hydraulic model plus model–data fusion code is on GitHub. Both are paper-specific, public, and actionable.Dataset · publicore, are sub-
ject to model and data uncertainties. However, our findings
highlight opportunities and challenges for further investiga-
tion of plant hydraulics at a global scale.
Code and data availability. The maps of retrieved ensemble mean
and standard deviation of plant hydraulic traits are publicly avail-
able on Figshare https://doi.org/10.6084/m9.figshare.13350713.v2
(Liu et al., 2020). The source code of the used plant hydraulic
model and the model–data fusion algorithm is available at https:
//github.com/YanlanLiu/VOD_hydraulics (Liu et al., 2020b). All
the assimilation and forcing data sets used in this study are
publicly available from the referenced sources, except for the
microOpen asset ↗Figshare · 10.6084/m9.figshare.13350713.v2pdf-raw-page:13 lines:1-86Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Root hydraulic properties play a central role in the global water cycle, agricultural systems productivity, and ecosystem survival as they impact the global canopy water supply. However, the available experimental methods to quantify root hydraulic conductivities, such as the root pressure probing, are particularly challenging and their applicability on thin roots and small root segments is limited. There is a gap in methods enabling easy estimations of root hydraulic conductivities across a diversity of root types and at high resolution along root axes. In this case study, we analysed Zea mays (maize) plants of the var. B73 that were grown in pots for 14 days. Root cross-section data were used to extract anatomical measurements. We used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively), based on the generated anatomical networks and cell hydraulic properties from the literature. The root hydraulic conductivity maps obtained from the root cross-sections suggest significant functional variations along and between different root types. Predicted variations of kr along the root axis were strongly dependent on the maturation stage of hydrophobic barriers. The same was also true for the maturation rates of the metaxylem. The different anatomical features, as well as their evolution along the root type add significant variation to the kr estimation in between root type and along the root axe. Under the prism of root types, anatomy, and hydrophobic barriers, our results highlight the diversity of root radial and axial hydraulic conductivities, which may be veiled under low-resolution measurements of the root system hydraulic conductivity. While predictions of our root hydraulic maps match the range and trend of measurements reported in the literature, future studies could focus on the quantitative validation of hydraulic maps. From now on, a novel method, which turns root cross-section images into hydraulic maps will offer an inexpensive and easily applicable investigation tool for root hydraulics, in parallel to root pressure probing experiments. One-Sentence summaryThe use of cross-section images and modelling tools to generate a map the axial and radial hydraulic conductivity along different root types for the maize cultivar B73.
Why it matches plant phenotyping methods根の断面画像から解剖学的形質を抽出し、モデルで軸方向・半径方向の根 hydraulic conductivity を推定する手法が研究の中心であるため、植物表現型計測手法として収録する。
abstractWe used the Generator of Root Anatomy in R (GRANAR) model to generate root anatomical networks from anatomical features. Then we used the Model of Explicit Cross-section Hydraulic Anatomy (MECHA) to compute an estimation of the root axial and radial hydraulic conductivities (kx and kr, respectively)
Reproduction assets foundThe paper provides two public, paper-specific assets: the GRANAR-MECHA coupling workflow (Jupyter/R repository with Zenodo DOI) and the B73_HydraulicMap repository containing the Rmarkdown script used to compute the root hydraulic maps plus all input and output data of the study.Code · publicection can be visualized through different figures that show the
186 proportion of the water fluxes in each compartiment (apoplastic and symplastic fluxes).
The whole script that was used to compute the root hydraulic maps from the root anatomical
188 measurement is presented as a Rmarkdown script stored in a GitHub repository
(https://github.com/granar/B73_HydraulicMap doi: 10.5281/zenodo.4320861). In the same
190 repository are stored all input and output data of this study.
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CC-BY 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint Open asset ↗granar/B73_HydraulicMap · 10.5281/zenodo.4320861pdf-raw-page:10 lines:1-20Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.
Why it matches plant phenotyping methodsNDVIおよび炭素同位体組成を用いたバイオマス・干ばつ適応形質の推定性能と測定時期の影響を評価しており、表現型取得法の応用・技術評価が中心である。
titleCarbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection
Reproduction assets foundThe paper's Supplementary Materials (hosted at the MDPI URL listed in allowed_urls) contain paper-specific phenotyping assets: Table S1 (flowering time/phenological stage data for the 248 accessions), Tables S2–S3 (linear regressions of biomass vs. NDVI and biomass vs. δ13C), and Figure S1 (image of the field trial at Supplement · publicAgronomy 2020, 10, 1679 16 of 19
these traits are evaluated and (2) genotypic variability in intrinsic characteristics, such as phenology,
is accounted for.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4395/10/11/1679/s1,
Table S1: Number of accessions at different flowering times and phenological stages, Table S2: Linear regression
of the relationship between the biomass at mid grain filling and the NDVI, Table S3: Linear regression of the
relationship between the biomass and δ13C in the flag leaf dry matter sampled before imposition of stressOpen asset ↗mdpi.compdf-raw-page:16 lines:1-55Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. RESULTS: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. CONCLUSION: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.
Why it matches plant phenotyping methods屋外HTPのロードセルデータから蒸散応答の特徴量とVPDブレークポイントを自動抽出し、遺伝型を識別する解析手法が研究の中心であるため。
abstractoutdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicInterested readers can find the R scripts on the open-source GitHub platform, https://github.com/KSoumya/EZTr .Open asset ↗KSoumya/EZTrlines:185-203Code / 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 confirmedCrossref · checked 9 Sept 2026
Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.
Why it matches plant phenotyping methods葉の気孔画像から形態・開口状態を自動抽出・定量する画像解析手法を開発し、精度検証しており、植物フェノタイピング手法が研究の中心です。
abstractThis paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks.
Reproduction assets foundThe paper's authors explicitly state that the complete project code for the Mask R-CNN-based stomatal pore detection and measurement method is publicly available on GitHub. The image datasets themselves are not stated as deposited by the authors (the generalization datasets are cited prior work, Stomatacounter [39]).Code · publicThe complete code for the project can be accessed at
https://github.com/lijunyu159/stomatal_pore_measurement-MaskRCNN (accessed on 15 July 2020).Open asset ↗lijunyu159/stomatal_pore_measurement-MaskRCNNpdf-page:9 lines:1-57Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Field / plotRootPhysiological trait estimationWater status / transpiration
Abstract Background and aims Monitoring root water uptake dynamics under water deficit (WD) conditions in fields are crucial to assess plant drought tolerance. In this study, we investigate the ability of Electrical Resistivity Tomography (ERT) to capture specific soil water depletion induced by root water uptake. Methods A combination of surface and depth electrodes with a high spatial resolution (10 cm) was used to map 2-D changes of bulk soil electrical conductivity (EC) in an agronomic trial with different herbaceous species. A synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots. The impact of root segments was incorporated in the forward electrical model using the power-law mixing model. Results The time-lapse analysis of the synthetic ERT experiment shows that different root water uptake patterns can be delineated for measurements collected under WD conditions but not under wet conditions. Three indices were found (depletion amount, maximum depth, and spread), which allow capturing plant-specific water signatures based moisture profile changes derived from EC profiles. When root electrical properties were incorporated in the synthetic experiments, it led to the wrong estimation of the amount of water depletion, but a correct ranking of plants depletion depth. When applied to the filed data, our indices showed that Cocksfoot and Ryegrass had shallower soil water depletion zones than white clover and white clover combined with Ryegrass. However, in terms of water depletion amount, Cocksfoot consumed the largest amount of water, followed by White Clover, Ryegrass+White Clover mixture, and Ryegrass. Conclusion ERT is a well-suited method for phenotyping root water uptake ability in field trials under WD conditions.
Why it matches plant phenotyping methodsERTによる根の吸水動態・耐乾性の表現型取得法を、合成実験と圃場データで検証・適用しており、フェノタイピング手法が中心である。
abstractA synthetic experiment was performed with a mechanistic model to assess the ability of the electrode configuration to discriminate abstraction patterns due to roots.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the field datasets acquired and analyzed (ERT/TDR soil water depletion measurements used for plant phenotyping) on Zenodo, with a public URL matching an allowed entry.Dataset · publicThe field datasets acquired and analyzed in the current study are available at https://zenodo.org/record/3750199#.XpS-bVwzY2x , https://doi.org/10.5281/zenodo.3750199Open asset ↗zenodo · 10.5281/zenodo.3750199lines:218-250Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Genetic selection for whole-plant water use efficiency (yield per transpiration; WUE plant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUE plant such as intrinsic water use efficiency (WUE i ; CO 2 assimilation rate per stomatal conductance). Measuring WUE i by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUE i over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant . However, a genetic link between δ 13 C leaf and WUE plant in a C 4 species has not been well established. Therefore, to determine if there is a genetic relationship in a C 4 plant between δ 13 C leaf and WUE plant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUE plant in a recombinant inbred line (RIL) population created between the C 4 grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for δ 13 C leaf were found and co-localized with transpiration, biomass accumulation, and WUE plant . Additionally, WUE plant for each of the δ 13 C leaf QTL allele classes was negatively correlated with δ 13 C leaf , as would be predicted when WUE i influences WUE plant . These results demonstrate that δ 13 C leaf is genetically linked to WUE plant , likely to be through their relationship with WUE i , and can be used as a high-throughput proxy to screen for WUE plant in these C 4 species.
Why it matches plant phenotyping methods葉の炭素安定同位体比を全植物体の水利用効率の高スループット代理指標として検証し、WUEとの遺伝的関連を評価しており、表現型取得・スクリーニング法が中心的です。
abstractleaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-integrated proxy for WUE i that may provide a tool to screen for WUE plant
Reproduction assets foundThe paper's QTL analysis was performed with the authors' custom Python and R scripts, publicly deposited on GitHub (foxy_qtl_pipeline). No public phenotype dataset URL is stated in the supplied blocks.Code · publicin Feldman et al. (2017); Feldman et al. (2018) were
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used in this study, and here the methods used are repeated. QTL mapping was
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performed on day 27 within each treatment group using functions in the R/qtl and
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funqtl packages (Kwak et al., 2016). The functions were called by a set of custom
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Python and R scripts (https://github.com/maxjfeldman/foxy_qtl_pipeline). Two
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complimentary analysis methods were utilized. First, a single QTL model genome
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scan using Haley-Knott regression was performed to identify QTL exhibiting LOD
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score peaks greater than a permutation-based significance threshold (α = 0.05, n =
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1000). Second, a stepwise forward/backward selection proceOpen asset ↗maxjfeldman/foxy_qtl_pipelinepdf-raw-page:11 lines:1-77Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceRootMorphology / geometry measurement2D/3D reconstructionRoot system architectureWater status / transpiration
Background and aims Upland rice is often grown where water and phosphorus (P) are limited and these two factors interact on P bioavailability. To better understand this interaction, mechanistic models representing small-scale nutrient gradients and water dynamics in the rhizosphere of full-grown root systems are needed. Methods Rice was grown in large columns using a P-deficient soil at three different P supplies in the topsoil (deficient, suboptimal, non-limiting) in combination with two water regimes (field capacity versus drying periods). Root architectural parameters and P uptake were determined. Using a multiscale model of water and nutrient uptake, in-silico experiments were conducted by mimicking similar P and water treatments. First, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data, such as nodal root number, lateral types, interbranch distance, root diameters, and root biomass allocation along depth. Secondly, the multiscale model was informed with these 3D root architectures and the actual transpiration rates. Finally, water and P uptake were simulated. Key results The plant P uptake increased over threefold by increasing P and water supply, and drying periods reduced P uptake at high but not at low P supply. Root architecture was significantly affected by the treatments. Without calibration, simulation results adequately predicted P uptake, including the different effects of drying periods on P uptake at different P levels. However, P uptake was underestimated under P deficiency, a process likely related to an underestimated affinity of P uptake transporters in the roots. Both types of laterals (i.e. S- and L-type) are shown to be highly important for both water and P uptake, and the relative contribution of each type depend on both soil P availability and water dynamics. Key drivers in P uptake are growing root tips and the distribution of laterals. Conclusions This model-data integration demonstrates how multiple co-occurring single root phene responses to environmental stressors contribute to the development of a more efficient root system. Further model improvements such as the use of Michaelis constants from buffered systems and the inclusion of mycorrhizal infections and exudates are proposed.
Why it matches plant phenotyping methods3D根系アーキテクチャを観測データで再構成・較正し、根形態と吸水・リン吸収を統合モデルで推定する手法適用が研究の中心である。
abstractFirst, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data
Reproduction assets foundThe paper explicitly states that the multiscale soil-root model code used for the water and phosphorus uptake simulations is publicly shared on GitHub at the Plant-Root-Soil-Interactions-Modelling/dumux-rosi repository (pub/Mai2019 branch), which is the authors' computational analysis code for this study. No public rawCode · publictrient transport models, the
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implementation of the dynamic root growth in the flow and transport model, the root growth model, the
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mathematical equations, and the multiscale coupling method are presented in Supplementary Information
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(Text S1) and can be found in Mai et al. (2018). The model code is shared on GitHub
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(https://github.com/Plant-Root-Soil-Interactions-Modelling/dumux-rosi/tree/pub/Mai2019).24
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Virtual experiment setup
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted January 27, 2020.
;
https://doi.org/10.1101/2020.01.27.921247
doi:
biOpen asset ↗Plant-Root-Soil-Interactions-Modelling/dumux-rosi · pub/Mai2019pdf-raw-page:10 lines:1-58Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 15 Sept 2026
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis-related parameters, which require laborious, time-consuming measurements, were also investigated. Traditional laboratory-based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state-of-the-art performance of the deep-learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high-throughput phenotyping for studying plant salinity stress using a combination of HSI and deep-learning approaches.
Why it matches plant phenotyping methodsHSIと深層学習による植物・葉のセグメンテーションおよび生理形質推定が研究の中心であり、高スループット表現型取得手法を実装・評価している。
titleHyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping
Reproduction assets foundThe authors publicly deposited the plant/leaf segmentation models in CodeOcean and the MMD clustering source code on GitHub, both directly supporting this paper's phenotyping analysis. The CVPPP 2015 dataset and Hitachi annotation tool are third-party/generic resources, not paper-specific assets.Code · publicels were constructed using Python3.6 (Guido van Ros-
sum, Python Dev Team).
DATA AVAILABILITY STATEMENT
Data further supporting this work, such as details of plant
and leaf segmentation models used in this study, are open
and available in codeocean (https://doi.org/10.24433/CO.3430273.v1). The source code of MMD is available on
https://github.com/jinnuozhang/Coderoom/blob/master/CLUS
TER.ipynb.
ACKNOWLEDGEMENT
The authors would like to thank Hui Fang for helping in illustrat-
ing.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
AUTHOR CONTRIBUTIONS
XF designed the research. YH and DJ supervised the pro-
ject. XF, YZ, XY, CY, HW and ZT performed the experi-
ments. QW analyzOpen asset ↗github.com/jinnuozhang/Coderoompdf-raw-page:13 lines:1-89Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeRootMorphology / geometry measurementPhysiological trait estimationRoot system architectureWater status / transpiration
Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly nonlinear environment (soil, atmosphere), which makes unclear what impact of each single root system on root system functioning actually is. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system instance a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits are compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches by providing a fast and reliable tool, which eventually can help performing plant virtual breeding. We describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations, and a review of architectural and hydraulic parameters of maize. To illustrate the tool’s potential, we generated contrasted maize hydraulic architectures that we compared with architectural (root length density) and hydraulic (root system conductance) observations. Observed variability of these traits was well captured by model ensemble runs We also analyzed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key parameters to vary for efficient virtual root system breeding. MARSHAL enables inverse optimisations, sensitivity analyses and virtual breeding of maize hydraulic root architecture. It is available as an R package, an RMarkdown pipeline, and a web application. One-sentence summary We developed a dynamic hydraulic-architectural model of the root system, parameterized for maize, to generate contrasted hydraulic architectures, compatible with field and lab observations and that can be further analyzed in soil-root system models for virtual breeding. Authors contributions F.M., X.D., M.J. and G.L. designed the study and defined its scope; F.M. and G.L. developed the model while associated tools were created by A.H. and G.L.; F.M. ran the model simulations and analyzed the results together with M.J and G.L.; F.M. and M.J. wrote the first version of this manuscript; all co-authors critically revised it.
Why it matches plant phenotyping methodsトウモロコシ根系の水理・構造形質を仮想生成・推定する計算ツールの開発が研究の中心であり、観測形質による比較検証も行っている。
abstractWe describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool
Abstract Afforestation projects for mitigating CO 2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( g smax ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m −2 y −1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.
Why it matches plant phenotyping methods植物器官・生態系の炭素固定と成長を推定する監視手法を、プロセスモデルと圃場データ、パラメータ最適化で構築・不確実性評価しており、植物の生理状態推定が中心です。
abstractWe proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' biometric data for E. ulmoides allometric relationships and the files related to parameter optimization and simulation results on Zenodo, a public repository with a DOI. This is a paper-specific, publicly actionable asset. The NCDC GSOD meteoricalDataset · publicThe biometric data for allometric relationships of E. ulmoides and the files related to optimization and simulation results are available on Zenodo ( https://doi.org/10.5281/zenodo.2815612 ).Open asset ↗Zenodo · 10.5281/zenodo.2815612lines:393-549Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafPhysiological trait estimationLeaf traitsWater status / transpiration
Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.
Why it matches plant phenotyping methods葉の光学特性から複数の生理・構造形質を非破壊かつ高スループットに推定する分光法と予測モデルを開発・独立検証しており、フェノタイピング手法が中心である。
abstractApproaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding.
Reproduction assets foundThe paper states that its leaf spectra, biochemical trait data, and PLSR R code were made publicly available via the EcoSIS spectral library (doi:10.21232/C2GM2Z). This is a paper-specific, public phenotyping asset (spectra–trait dataset plus analysis code), but no URL is present in the allowed_urls list, so it cannot Dataset · publicLeaf spectra, leaf trait data and the R code for the PLSR model are available from the EcoSIS
spectral library (ecosis.org), doi:10.21232/C2GM2Z.EcoSIS · doi:10.21232/C2GM2Zpdf-raw-page:17 lines:1-10Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article presents experimental data describing the physiology and morphology of sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France). Here, we provide data including specific leaf area, osmotic potential and adjustment, carbon isotope discrimination, leaf transpiration, plant architecture: plant height, leaf number, stem diameter. We also provide leaf areas of individual organs through time and growth rate during the stress period, environmental data such as temperatures, wind and radiation during the experiment. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the physiological basis of heterosis. It is available on the following repository: https://doi.org/10.25794/phenotype/er6lPW7V.
Why it matches plant phenotyping methods植物の形態・生理形質を高スループット表現型解析プラットフォームで取得した再利用可能なデータセットであり、表現型データ資源の提供が中心です。
abstractDrought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France).
Reproduction assets foundThe article is a Data in Brief paper whose entire content is the paper's own eco-physiological phenotyping dataset (24 sunflower genotypes, water deficit, Heliaphen platform). The authors explicitly deposit the data publicly in the SUNRISE Phenotype Archive with DOI 10.25794/phenotype/er6lPW7V, described as csv/xls/pdfDataset · publicData accessibility Data are with this article and also publicly available in the SUNRISE
Archive depository with following DOI: 10.25794/phenotype/er6lPW7VOpen asset ↗10.25794/phenotype/er6lPW7Vpdf-raw-page:3 lines:1-46Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination (δ 13 C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and δ 13 C further suggests that this variation has consequences on water-use efficiency. Genome wide association analyses indicate a complex genetic architecture underlying not only variation in stomatal patterning but also to its covariation with carbon uptake parameters. Yet, we report two novel QTL affecting δ 13 C independently of stomatal patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and covariation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favour the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動化共焦点顕微鏡による気孔サイズ・密度の表現型取得法を開発し、330系統へ大規模適用しているため、植物表現型計測が中心である。
abstractWe developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully sequenced Arabidopsis thaliana accessions collected throughout the European range of the species.
Reproduction assets foundThe paper's Data Accessibility statement explicitly deposits raw confocal image data, image analysis scripts, and phenotypic data in a Dryad repository, uploads genotypic phenotype means to AraPheno, and provides authors' GWAS and MTMM analysis scripts on GitHub. All are paper-specific, public, and actionable.Dataset · publicRaw image data and image analysis scripts are stored in a Dryad repository ( https://doi.org/10.5061/dryad.n068q74 ). Phenotypic data are provided as supplemental material and included in the Dryad repository.Open asset ↗Dryad · 10.5061/dryad.n068q74lines:153-213Code · publicGWAS scripts are available at https://github.com/arthurkorte/GWAS .Open asset ↗GitHub · arthurkorte/GWASlines:153-213Code · publicMTMM scripts are available at https://github.com/Gregor-Mendel-Institute/mtmm .Open asset ↗GitHub · Gregor-Mendel-Institute/mtmmlines:153-213Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Stomata control gas exchanges between the plant and the atmosphere. How natural variation in stomata size and density contributes to resolve trade-offs between carbon uptake and water-loss in response to local climatic variation is not yet understood. We developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species. We compared this to variation in water-use efficiency, measured as carbon isotope discrimination ({delta}13C). We detect substantial genetic variation for stomata size and density segregating within Arabidopsis thaliana. A positive correlation between stomata size and {delta}13C further suggests that this variation has consequences on water-use efficiency. Genome-wide association analyses indicate a complex genetic architecture underlying not only variation in stomata patterning but also to its co-variation with carbon uptake parameters. Yet, we report two novel QTL affecting {delta}13C independently of stomata patterning. This suggests that, in A. thaliana, both morphological and physiological variants contribute to genetic variance in water-use efficiency. Patterns of regional differentiation and co-variation with climatic parameters indicate that natural selection has contributed to shape some of this variation, especially in Southern Sweden, where water availability is more limited in spring relative to summer. These conditions are expected to favor the evolution of drought avoidance mechanisms over drought escape strategies.
Why it matches plant phenotyping methods自動共焦点顕微鏡法を開発し、330系統で気孔サイズ・密度という植物形質を大規模に測定しており、表現型取得法が研究の中心です。
abstractWe developed an automated confocal microscopy approach to characterize natural genetic variation in stomatal patterning in 330 fully-sequenced Arabidopsis thaliana accessions collected throughout the European range of the species.
Reproduction assets foundThe data accessibility statement lists public, paper-specific assets: phenotypic (stomata/δ13C) data to be deposited in AraPheno with a public URL, and the authors' GWAS and MTMM analysis scripts on GitHub. Raw images and image-analysis scripts are only available upon request (Dryad deposit pending acceptance), so theyCode · public1001genomes.org, (Seren et
920
al., 2017) and stored in a Dryad repository upon acceptance. Additionally, we provide an R
921
Markdown file, which contains all figures (except GWAS and MTMM) and the
922
corresponding R code used to create the figures and statistics in the supplemental material.
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GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
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available at https://github.com/Gregor-Mendel-Institute/mtmm.925
Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
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2016)
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Author contributions
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JdM, AK, and HD conceived the study. HD conducted the experiment and produced
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phenotypic data for stomaOpen asset ↗arthurkorte/GWASpdf-raw-page:35 lines:1-46Code · publicory upon acceptance. Additionally, we provide an R
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Markdown file, which contains all figures (except GWAS and MTMM) and the
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corresponding R code used to create the figures and statistics in the supplemental material.
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GWAS scripts are available at https://github.com/arthurkorte/GWAS. MTMM scripts are
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available at https://github.com/Gregor-Mendel-Institute/mtmm.925
Genomic data used is publicly available in the 1001 genomes database (Alonso-Blanco et al.,
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2016)
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Author contributions
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JdM, AK, and HD conceived the study. HD conducted the experiment and produced
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phenotypic data for stomata traits. TM and AW were responsible for 13
C measurements. GM
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provideOpen asset ↗Gregor-Mendel-Institute/mtmm.925pdf-raw-page:35 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.
Why it matches plant phenotyping methods温室自動フェノタイピング platformで測定する早期形質を選定・評価し、異なる実験と圃場データで検証しており、形質取得と評価フレームワークが研究の中心である。
abstractThe aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform
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 Table 1
Phenotypic traits evaluated during the Experiment 1 with GlyPh and four criteria considered for the evaluation of phenotyping efficiency.Open asset ↗lines:584-646Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The vulnerability of plant water transport tissues to a loss of function by cavitation during water stress is a key indicator of the survival capabilities of plant species during drought. Quantifying this important metric has been greatly advanced by noninvasive techniques that allow embolisms to be viewed directly in the vascular system. Here, we present a new method for evaluating the spatial and temporal propagation of embolizing bubbles in the stem xylem during imposed water stress. We demonstrate how the optical method, used previously in leaves, can be adapted to measure the xylem vulnerability of stems. Validation of the technique is carried out by measuring the xylem vulnerability of 13 conifers and two short-vesseled angiosperms and comparing the results with measurements made using the cavitron centrifuge method. Very close agreement between the two methods confirms the reliability of the new optical technique and opens the way to simple, efficient, and reliable assessment of stem vulnerability using standard flatbed scanners, cameras, or microscopes.
Why it matches plant phenotyping methods植物の茎木部の脆弱性を画像で定量する新規光学法を開発し、既存法との比較検証を行っており、植物状態の取得手法が中心である。
abstractHere, we present a new method for evaluating the spatial and temporal propagation of embolizing bubbles in the stem xylem during imposed water stress.
Reproduction assets foundThe paper's optical vulnerability image-capture and analysis scripts are publicly available at the authors' OpenSourceOV site; the caviplace URL is a facility page, not a data/code asset.Code · publicnd could be filtered from slow movements caused by drying. Thresholding of image differences allowed automated counting of cavitation events using the analyze-stack function in ImageJ. Full details, including an overview of the technique, image processing, as well as scripts to guide image capture and analysis, are available at http://www.opensourceov.org .
A time-resolved count of cavitations in each stem, quantified as the number of pixels per event during stem drying, was compiled, and this was converted to a percentage of total pixels cavitated. The psychrometer output was then used to determine a fitted function that described the change in stem water potential over time. TOpen asset ↗www.opensourceov.orglines:175-179Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 11 Sept 2026
High-throughput phenotyping produces multiple measurements over time, which require new methods of analyses that are flexible in their quantification of plant growth and transpiration, yet are computationally economic. Here we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array. Two rice diversity panels, indica and aus, containing a total of 553 genotypes, are phenotyped in waterlogged conditions. Using cubic smoothing splines to estimate plant growth and transpiration, we identify four time intervals that characterize the early responses of rice to salinity. Relative growth rate, transpiration rate and transpiration use efficiency (TUE) are analysed using a new association model that takes into account the interaction between treatment (control and salt) and genetic marker. This model allows the identification of previously undetected loci affecting TUE on chromosome 11, providing insights into the early responses of rice to salinity, in particular into the effects of salinity on plant growth and transpiration.
Why it matches plant phenotyping methods植物の成長・蒸散をハイスループットに定量する解析手法を開発し、イネ集団への適用と遺伝子座同定まで行っており、表現型取得・抽出が研究の中心である。
abstractHere we develop such analyses and apply this to a rice population genotyped with a 700k SNP high-density array.
Reproduction assets foundThe paper's phenotyping data (raw data underlying trait calculation, trait values) and the authors' analysis code (trait production code and GWAS interaction-model code) are publicly deposited in Dryad under doi:10.5061/dryad.3118j, with explicit availability language in the Data availability section.Code · publicthe codes used in producing the trait values and the code for the interaction model used for GWAS analyses are all available in Dryad ( http://datadryad.org/, doi:10.5061/dryad.3118j ).Open asset ↗Dryad · doi:10.5061/dryad.3118jlines:88-149Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
CoffeeGrapevineLeafMorphology / geometry measurementPhysiological trait estimationLeaf traitsWater status / transpiration
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1-1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400-900 kHz and 200-400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.
Why it matches plant phenotyping methods植物葉の水分量・膨圧を非接触超音波で測定するセンサー方式の要件分析、試作、応用を中心に扱っており、植物表現型取得法が明確に中心的である。
abstractThis work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure.
Reproduction assets foundThe paper's inverse-problem analysis code for extracting leaf parameters (thickness, density, ultrasound velocity, attenuation) from measured resonance spectra is explicitly stated to be publicly available via the authors' GitHub repository and the US-BIOMAT resource page. No phenotype dataset deposit is mentioned.Code · publicUS-BIOMAT
Available online: https://us-biomat.com/resources/code-2/ or https://github.com/usbiomat/ultrasonic-thickness-resonance (accessed on 12 July 2016)Open asset ↗usbiomat/ultrasonic-thickness-resonancelines:327-413Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Robustness in lettuce, defined as the ability to produce stable yields across a wide range of environments, may be associated with below-ground traits such as water and nitrate capture. In lettuce, research on the role of root traits in resource acquisition has been rather limited. Exploring genetic variation for such traits and shoot performance in lettuce across environments can contribute to breeding for robustness. A population of 142 lettuce cultivars was evaluated during two seasons (spring and summer) in two different locations under organic cropping conditions, and water and nitrate capture below-ground and accumulation in the shoots were assessed at two sampling dates. Resource capture in each soil layer was measured using a volumetric method based on fresh and dry weight difference in the soil for soil moisture, and using an ion-specific electrode for nitrate. We used these results to carry out an association mapping study based on 1170 single nucleotide polymorphism markers. We demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture. QTLs for below-ground traits were not detected at early sampling. Significant marker-trait associations were detected across trials for below-ground and shoot traits, in number and position varying with trial, highlighting the importance of the growing environment on the expression of the traits measured. The difficulty of identifying general patterns in the expression of the QTLs for below-ground traits across different environments calls for a more in-depth analysis of the physiological mechanisms at root level allowing sustained shoot growth.
Why it matches plant phenotyping methods水・硝酸の資源捕捉を定量する間接的な高スループット表現型計測法を明示的に評価し、信頼性と遺伝変異の定量能力を検証しているため、方法が研究の中心的要素です。
abstractWe demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture.
Reproduction assets foundThe paper's phenotypic measurements (soil water/nitrate capture, shoot traits) and genotype scores/linkage map are stated to be available as Supplementary Material (Tables S1 and S2) hosted at the article's public Frontiers URL. No author analysis code or trained models are mentioned.Supplement · publicey also thank Jan Velema, Marcel van Diemen and Pieter Schwegman, Vitalis Organic Seeds, for providing seeds, advice, and insight. The project was financially supported through the Top Institute Green Genetics (project number: 2CFD024RP).
Supplementary material
The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2016.00343
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References
Biddington N. L., Dearman A. S. (1985). The effects of mechanically-induced stress on water loss and drought resistance in lettuce, cauliflower and celery seedlings. Ann. Bot.
56, 795–8Open asset ↗lines:840-871