RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 15 Sept 2026
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
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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 confirmedOpenAlex · arXiv · checked 5 Sept 2026
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically composites spectral observations over fixed calendar windows, implicitly assuming phenological synchrony across fields. We present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction, for a quality trait whose physiology, senescence-linked nitrogen remobilization, contrasts with the season-integrating behavior of yield. Integrating Sentinel-2 imagery (31 vegetation indices, 10 spectral bands), ERA5-Land reanalysis, gSSURGO soil properties, and USGS 3DEP topography across 228 commercial winter wheat fields in western Kansas (2024–2025), we compared six temporal strategies (peakrelative vs. calendar × monthly, biweekly, growth-stage) using three ensemble tree models under nested cross-validation with Boruta feature selection. A single 30-day post-peak window (peak + [16,45] days) was the top-performing and most consistently selected window, chosen in 4 of 5 outer folds, reproducing prior accuracy under random cross-validation (R2 ≈ 0.28); though its advantage over the best calendar window was not statistically significant (paired bootstrap p = 0.08). Under leave-county spatial cross-validation, however, this skill did not transfer across counties (Sentinel-2–only R2 ≈ 0.01; per-county median R 2 = −0.23), indicating the satellite signal supports within-region interpolation but not spatial extrapolation to unseen counties; ablation shows that neither the spectral nor the static features transfer across counties on their own, and the residual crosscounty skill emerges only from their combination. A near-real-time application at ∼3 weeks before harvest retains most within-region skill at a modest accuracy cost. The results delineate where spectral-peak-relative alignment helps, concentrating a senescence-linked signal within region, and where it does not, providing an honest operational baseline for satellite-based grain-quality monitoring.
Why it matches plant phenotyping methods小麦の穀粒タンパク質濃度という植物形質を対象に、衛星時系列のスペクトルピーク相対アラインメントを開発・比較評価し、交差検証で性能と空間移 transfer 性を検証しているため、方法が中心的である。
abstractWe present a systematic evaluation of whether aligning multi-source remote sensing time series to field-specific, spectral-peak-relative windows improves field-level GPC prediction
Reproduction assets foundThe preprint explicitly releases the authors' analysis code (data-acquisition pipeline, feature engineering, cross-validation/modeling, figure scripts) at a public GitHub repository, and a de-identified field-level GPC dataset released alongside the code repository. Both are paper-specific, public, and actionable. The Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗Ciampitti-Labpdf-page:48 lines:1-55Dataset · publica de-identified version of the dataset is released alongside the code repositoryOpen asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Plants employ non-photochemical quenching (NPQ) to protect their photosynthetic apparatus from photodamage. The response latency of NPQ following changes in light intensity is thought to significantly decrease photosynthetic efficiency. The amount of NPQ is commonly quantified from chlorophyll-fluorescence techniques using the Stern–Volmer equation, which requires fully closed reaction centres (RCs) of photosystem II, yielding NPQ in the absence of photochemical quenching ( $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed ). However, in nature, NPQ and photochemical quenching are normally present simultaneously. Therefore, to obtain a full understanding of this process, NPQ should also be explored when the RCs are open. Here we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements. A detailed comparison in Arabidopsis thaliana plants reveals that the value of $${\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Open is ~35% lower than that of $${\rm{NPQ}}^{\rm{Closed}}$$ NPQ Closed . This difference is consistently observed across all measurements and is seen both upon closing ( $${\rm{NPQ}}^{\rm{Open}}\to {\rm{NPQ}}^{\rm{Closed}}$$ NPQ Open → NPQ Closed ) and upon reopening ( $${\mathrm{NPQ}}^{\mathrm{Closed}}\to {\mathrm{NPQ}}^{\mathrm{Open}}$$ NPQ Closed → NPQ Open ) of the RCs. We show that this difference can be explained by the presence of RC-induced ‘instantaneous’ switching of the NPQ quenching rate. This means that, in plants, NPQ is much more economical than is widely believed, it is large when its presence is needed, and it decreases instantaneously when the need disappears.
Why it matches plant phenotyping methods植物の光合成状態（NPQ）を測定するための蛍光寿命・蛍光収率に基づく2つの方法を開発し、比較検証しているため、方法開発が中心である。
abstractHere we developed two methodologies to obtain NPQ in the presence of photochemistry ( $${\rm{NPQ}}^{\rm{Open}}$$ NPQ Open ) using both fluorescence lifetime and fluorescence yield measurements.
Reproduction assets foundThe paper's custom ultrafast fluorescence analysis code (ICA-based PSII/PSI deconvolution and NPQ calculations) is explicitly deposited by the authors on GitHub, alongside the original data contributions.Code · publicr(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Funding
This work was supported by ‘Nanoscale regulators of photosynthesis’ NWO research project (project number: OCENW.GROOT.2019.86).
Data availability
The original contributions presented in the study are available via GitHub at https://github.com/L-Ramakers/Heimdall .
Code availability
The custom analysis code used in the study is available via GitHub at https://github.com/L-Ramakers/Heimdall .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afOpen asset ↗L-Ramakers/Heimdalllines:88-125Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract Purpose Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R 2 = 0.81–0.95; RMSE = 0.27–0.77 m 2 /m 2 ), followed by CCC (R 2 = 0.58–0.94; RMSE 2 ), while LCC remained less accurately estimated (R 2 = 0.26–0.78; RMSE 2 ). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.
Why it matches plant phenotyping methodsUAV・近接分光センサーとPROSAIL反転、ANNを用いてLAIや葉・群落クロロフィルを推定し、精度比較と圃場観測との整合性評価を行うことが研究の中心である。
abstractThis study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' UAV image processing code (irradiance normalization, vignetting, exposure compensation, radiometric calibration) in a public GitHub repository under GPL v3.0; other data are available only upon request.Code · publicData availability Code to perform irradiance normalization, vignetting, exposure compensation, and radio-
metric calibration is available at https://github.com/fieldSITES/scripts/tree/main/UAV under GNU General
Public License v3.0. Other data will be made available upon request.Open asset ↗UAVpdf-page:34 lines:1-40Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Idesia polycarpa Maxim. is a premier woody oil species in Guizhou Province, China, whose fruit yield and oil quality largely depend on effective pollination and fertilization. However, limited research on pollen viability and germination has hindered industrial progress. To address this gap, a comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay. Scanning electron microscopy (SEM) revealed that I. polycarpa pollen, while genetically conserved at the genus level-characterized by prolate shapes, tricolporate apertures, and reticulate exine ornamentation-exhibits notable micromorphological variation among genotypes. Of the nine staining protocols tested (2,3,5-triphenyl tetrazolium chloride [TTC], carbol fuchsin, acetocarmine, methylene blue, Alexander, peroxidase, 2,5-diphenylmonotetrazolium bromide [MTT], I2-KI, and red ink), TTC and red ink were the most effective, offering clear chromatic distinction between viable and non-viable pollen. Through orthogonal experimental designs, genotype-specific optimal media for in vitro germination were identified: 0.40 g/L H3BO3, 0.01 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.20 g/L KH2PO4 for STZ-6; and 0.20 g/L H3BO3, 0.02 g/L KNO3, 0.02 g/L Ca(NO3)2·4H2O, and 0.10 g/L KH2PO4 for STZ-9. Regression analysis confirmed a highly significant positive correlation (P < 0.01) between in vitro germination rates and the staining results from both TTC and red ink across various concentrations. Notably, 5% TTC and 30% red ink exhibited the highest coefficients of determination. A hierarchical evaluation strategy is thus proposed: the 5% TTC method is recommended for precise laboratory quantification due to its stability, while the 30% red ink method, due to its ease of use, is suited for rapid field-based screening. This study provides valuable insights into the morphological characteristics of I. polycarpa pollen and establishes a standardized evaluation framework, supporting germplasm innovation and optimizing pollination management.
Why it matches plant phenotyping methods花粉の生存性・発芽という植物の生殖形質を対象に、染色法とin vitro発芽法を最適化・検証し、標準化した評価フレームワークを開発しているため、方法論が中心である。
abstracta comprehensive evaluation framework for elite I. polycarpa germplasm was developed, integrating micromorphological analysis, optimized staining protocols, and in vitro germination assay.
Reproduction assets foundThe article's Data Availability statement points to a public Biostudies deposit containing the study's data (pollen morphology measurements, staining viability counts, and in vitro germination results). No author analysis code or trained models are mentioned.Dataset · publicData Availability: The data that support the findings of this study are openly available in Biostudies at https://doi.org/10.6019/S-BSST3125 .Open asset ↗Biostudies · S-BSST3125lines:176-186Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (ηp2 = 0.63). CIELUV v* explained the greatest model variation (R2m = 0.94) and responded most to fertilizer (ηp2 = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems.
Why it matches plant phenotyping methodsRGB画像から芝草キャノピー色を定量化するΔEgなどの指標を導入・比較し、クロロフィルや品質評価との技術的関連性を検証しており、植物表現型取得法が中心である。
abstractWe introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics.
Reproduction assets foundThe paper's Data Availability Statement deposits the phenotype data and the authors' Python image-processing/metric-computation scripts and R statistical analysis scripts in the USDA National Agricultural Library Ag Data Commons, a public repository. The full RGB imagery archive, however, is only available upon requestCode · public2025;23:673–687. doi: 10.1002/lom3.10705.
Associated Data
Data Availability Statement
Data and Python scripts used for image processing and %G, %Gr, %Y, ΔEg, DGCI, HSVi, BA SD , CIELUV v* metric computation, and R scripts used for statistical analysis are be available in the USDA National Agricultural Library Ag Data Commons ( https://agdatacommons.nal.usda.gov/ ), Data for—Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions, accessed on 27 July 2026. The full RGB imagery archive will be made available upon reasonable request.Open asset ↗USDA National Agricultural Library Ag Data Commonslines:691-695Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.
Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。
abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves
Reproduction assets foundThe paper's data availability statement explicitly makes the 3,454-image A. senegal leaf imaging dataset public on Zenodo and the ML implementation source code public on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe plant leaf imaging data used in the work is publicly available at https://doi.org/10.5281/zenodo.16531486.Open asset ↗zenodo · 10.5281/zenodo.16531486html-lines:480-497Code · publicThe source code of the implementation is available at https://github.com/softwareinnovationslabBITS/CDRF_ASenegal_MLImagingOpen asset ↗github · softwareinnovationslabBITS/CDRF_ASenegal_MLImaginghtml-lines:480-497Code / 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 confirmedEurope PMC · checked 5 Sept 2026
Objective This study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence. Methods Six commercial varieties, including orange, yellow, white, and purple genotypes, were evaluated under field and laboratory conditions using multispectral drone imagery, high-resolution spectroradiometric signatures, red green blue (RGB) images, and CIELAB color measurements. A hierarchical modeling framework was developed across two phases: (i) spectral modeling using uncrewed aerial vehicle (UAV)-based multispectral indices, textural and geometric metrics, and laboratory-generated hyperspectral signatures; and (ii) a colorimetric index from RGB images. Results Using UAV-based multispectral field data, phenological prediction indices achieved high classification performance (F1-scores > 0.90) when modeled with a Random Forest classifier, supported by distinct spectral signatures associated with canopy development and senescence. In parallel, carotenoid content estimation using a Random Forest regression model demonstrated strong predictive accuracy ( R 2 = 0.897; RMSE = 0.584), with the Plant Senescence Reflectance Index (PSRI) and Carotenoid Reflectance Index (CRI) identified as the most influential predictors. A complementary laboratory-based Random Forest regression model using high-resolution spectral signatures achieved near-perfect predictive performance ( R 2 = 0.987). SHapley Additive exPlanations (SHAP) analysis identified physiologically relevant wavelengths in the green (540-550 nm) and red-edge (∼700 nm) regions as the primary drivers of carotenoid concentration. Likewise, a novel colorimetric index (ICarot), derived from CIELAB parameters, enabled accurate image-based carotenoid estimation ( R 2 = 0.85). Conclusion This study introduces an innovative multi-sensor framework for precision agriculture and automated postharvest quality control, enabling rapid, objective, and scalable phenotyping in carrot production systems. Through the integration of spectral, colorimetric, and AI-based approaches, the proposed methodology effectively captures both internal nutritional attributes and external quality traits within a unified, non-destructive assessment pipeline.
Why it matches plant phenotyping methods複数センサー画像・分光計測とAIを統合し、ニンジンの生育段階およびカロテノイド含量を非破壊推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis study presents an integrated, multi-scale approach for the non-destructive estimation of phenological stages and carotenoid content in carrots by combining spectral techniques, colorimetry, and artificial intelligence.
Reproduction assets foundThe paper's Data Availability section explicitly deposits the study's data (and project materials) on GitHub and Zenodo, both with authors' public URLs matching allowed_urls. These qualify as paper-specific public assets for the carrot phenotyping measurements and analysis.Dataset · publicThe data is available at GitHub and Zenodo:
- https://github.com/agrocompuepidemlab/Carrot-value-chain-proyect/tree/mainOpen asset ↗github.com/agrocompuepidemlab/Carrot-value-chain-proyectlines:184-307Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Satellite-based prediction of grain protein concentration (GPC) in wheat typically relies on spectral observations composited over fixed calendar windows, implicitly assuming phenological synchrony across fields. This study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction. We integrated Sentinel-2 multispectral imagery (32 vegetation indices, 10 spectral bands), ERA5-Land meteorological reanalysis, gSSURGO soil properties, and USGS 3DEP topographic data, and systematically compared six temporal strategies, the factorial combination of two normalization approaches (peak-relative vs.\calendar) and three resolutions (monthly, biweekly, growth stages), across 228 commercial winter wheat fields in western Kansas (2024--2025). Three ensemble tree models (Random Forest, XGBoost, LightGBM) were trained under nested cross-validation with Boruta feature selection. Peak-relative monthly normalization achieved the highest accuracy (\((R^2 = 0.304 \pm 0.051)\), RMSE \((= 1.11)\)%), explaining an additional 5.1% of variance compared with the best calendar strategy (\((R^2 = 0.253)\)). A single 30-day post-peak window (M\((+)\)1, \((\sim)\)15--45 days after maximum canopy greenness) carried more predictive information than any broader aggregation. SHAP analysis identified topsoil organic matter, SWIR-based senescence indices (NBR2, MIRBI), and grain-filling temperature as the most influential predictors. Three-class quality classification reached 47--49% accuracy (versus 33.3% by chance), indicating practical utility for early grain segregation. While demonstrated for wheat GPC, the framework is transferable to other crop traits with temporally concentrated satellite signals, particularly those tied to specific developmental stages. The results highlight phenological alignment as a generalizable strategy for trait prediction from Earth observation data.
Why it matches plant phenotyping methods衛星リモートセンシング時系列を用いた小麦粒タンパク質濃度予測のため、フェノロジー整列と複数の時間集約戦略を体系的に比較・検証しており、植物形質推定手法が研究の中心である。
abstractThis study tests whether aligning multi-source remote sensing time series to field-specific phenology-based windows improves field-level GPC prediction.
Reproduction assets foundThe paper's data availability statement releases a de-identified field-level GPC dataset alongside a public authors' code repository (Ciampitti-Lab WheatGPCPipeline) implementing the data-acquisition, feature-engineering, and modeling pipeline. Both are paper-specific, public, and actionable.Code · publicthe figure-generation scripts is available at https://github.com/Ciampitti-Lab/Open asset ↗pdf-page:48 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurately monitoring alfalfa nutritional quality is essential for optimal pasture management. Yet, current UAV remote sensing methods rely on single-temporal imagery and empirical indices, limiting their ability to handle multi-stage growth dynamics, canopy spectral saturation, and canopy-to-whole-plant scale differences. Furthermore, small sample sizes often cause purely data-driven models to overfit correlations, yielding biologically unrealistic results. Overcoming these challenges, we designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator. After screening 14,960 spectral operators across original and log-transformed spaces, we applied a dual dimensionality reduction strategy to isolate optimal features. Four-band dual-difference structures proved highly sensitive to fiber components (ADF/NDF, |r| = 0.896), while logarithmic decoupling operators accurately isolated protein and nitrogen signals (CP/N, |r| = 0.868). We then engineered a Physics-Informed Sparse Shallow Network (PI-SSN). By leveraging temporal attention decoupling, it adaptively assigns growth-stage weights to different components and uses carbon-nitrogen metabolic constraints to maintain biological accuracy during multi-task retrieval. Multi-stage temporal data significantly boosted accuracy over single-period spectra. PI-SSN delivered exceptional test set coefficients of determination ( R2 ) of 0.812-0.848 and RPDs >2.0 for N, CP, ADF, and NDF, easily outperforming standard baselines. To bridge the canopy-only observation gap, we introduced a 3D allometric transfer operator that incorporates canopy coverage and plant height. This effectively corrected vertical stem-leaf observation biases, enhancing Relative Feed Value (RFV) predictions. Ultimately, this approach offers a powerful new framework for high-throughput forage phenotyping.
Why it matches plant phenotyping methodsUAVリモートセンシングと物理制約ネットワーク、3Dアロメトリック演算子を統合し、アルファルファの栄養品質を推定する手法を開発・検証しており、植物表現型取得が中心である。
abstractwe designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator.
Reproduction assets foundThe paper's authors publicly release the pre-trained PI-SSN model weights, inference code, and usage instructions on GitHub. The raw spectral and ground-truth quality datasets are not public and are available only on request, so they do not qualify as public assets.Code · publiceptualization, Resources, Supervision, Writing-review & editing. Dongyan Zhang: Conceptualization, Funding acquisition, Project Administration, Supervision, Writing-original draft, Writing-review & editing.
Data and code availability
The pre-trained model weights, inference code, and usage instructions are publicly available at https://github.com/AeroPheno/PI-SSN.git . The raw spectral data and ground-truth quality data used in this study are not publicly available due to ongoing collaborative projects, but are available from the corresponding author on reasonable request.
Funding
This work was supported by the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents (teamOpen asset ↗AeroPheno/PI-SSNlines:243-301Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll measurements, it is destructive and temporally limited, whereas portable optical meters such as the CCM-300 enable rapid, non-destructive measurement of the chlorophyll fluorescence ratio (CFR) but require species- and season-specific calibration. This study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom. Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. RF regression achieved the highest predictive performance within the calibration dataset, although substantial uncertainty remained at the leaf level; a simple linear model was therefore adopted for cross-year projection due to its stability under extrapolation. Applying this calibration to daily 2022 CFR measurements generated a continuous "virtual acetone" trajectory, enabling qualitative comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines; however, senescence, defined as the initiation of sustained post-peak decline, occurred earlier during the warmer and drier 2022 season. Mixed-effects modelling identified positive effects of temperature and wind speed on CFR in 2022, while generalised additive modelling of the 2023 dataset revealed a non-linear seasonal decline under comparatively mild conditions. Because cross-year projections rely on a low-fit linear calibration, interannual differences are interpreted primarily in terms of relative seasonal trajectory shape and timing rather than absolute chlorophyll magnitude.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を破壊的測定と比較し、校正モデルの開発・性能評価と季節軌跡の再構築を行っており、植物表現型取得法が研究の中心である。
abstractThis study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Traditional apple maturity assessment methods are destructive and time- and labour-intensive, yielding only population-level approximations. Hyperspectral imaging provides a non-destructive alternative to assess individual fruit, but progress has been constrained by the lack of large, diverse datasets that support robust model generalisation. This study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness. Using this dataset, we adopt an iterative modelling framework to evaluate deep learning architectures, image resolutions, cultivar encoding, seasonal effects, and feature-specific models. Wavelength and spatial region importance were also analysed. The best predictive performance was achieved using Vision Transformer (ViT) models trained on edge-cropped 40 × 40 pixel images with explicit cultivar encoding, with Brix and firmness modelled independently. Although seasonal specificity was observed, models trained across all three seasons achieved the strongest overall performance. A 50% reduction in spectral wavebands did not compromise prediction accuracy. Key wavelength ranges contributing to Brix and firmness prediction were identified across the visible-near-infrared spectrum. Spatial regions were unimportant for Brix prediction but showed relevance for firmness. The optimised ViT model achieved firmness prediction performance comparable to previous studies (RMSE = 0.76 kgf, R[Formula: see text] = 0.63), while Brix prediction accuracy was lower (RMSE = 0.91 [Formula: see text]Brix, R[Formula: see text] = 0.75), likely reflecting increased biological and environmental variability captured in the dataset. Overall, this work demonstrates that hyperspectral imaging combined with deep learning and large, diverse datasets enables robust, non-destructive prediction of apple quality attributes across production conditions.
Why it matches plant phenotyping methodsリンゴ果実の硬度とBrixという植物器官形質を、ハイパースペクトル画像と深層学習で非破壊推定するデータセット・モデル・汎化性能評価が研究の中心である。
abstractThis study presents a multi-cultivar, multi-season, multi-country hyperspectral apple dataset to enable generalisable prediction of soluble solids content (Brix) and firmness.
Reproduction assets foundThe paper explicitly states that the hyperspectral apple dataset (5756 apples, firmness/Brix/starch measurements) is deposited in the University of Essex research data repository and that the data cleaning, model training, and analysis code is on GitHub, both with public URLs.Dataset · publicThe datasets generated during and analysed during the current study are available in the University of Essex repository ( https://researchdata.essex.ac.uk/228/ )Open asset ↗researchdata.essex.ac.uk · 228lines:192-220Code · publicthe code used for data cleaning, model training and analysis are available on GitHub: ( https://github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaging.git )Open asset ↗github.com/EIS-Ressearch-Lab/Apple_maturity_hyperspectral_imaginglines:192-220Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Litchi is an important economic fruit in southern China, and its precision management relies on the rapid and accurate estimation of the Soil and Plant Analyzer Development (SPAD) values in leaves. Addressing the limitations of existing SPAD detection methods, such as limited rapid coverage, inadequate modeling of dynamic environmental interference, and shallow fusion of multi-source data, this study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features. A Long Short-Term Memory (LSTM) network model integrated with a feature level attention mechanism (MLSTM) was proposed to fuse IoT time-series data, vegetation index, and high dimensional texture features for dynamic SPAD value prediction. The results indicate that multi-source feature fusion significantly improves SPAD estimation accuracy. The MLSTM model achieved optimal performance under the all-features situation, with a coefficient of determination (R²) of 0.897 and a root mean square error (RMSE) of 2.638, outperforming other comparative models. The attention mechanism effectively enhanced the model's focus on key features, improving feature utilization efficiency and model interpretability. The multi-source data fusion method and MLSTM model proposed in this study enable high precision, dynamic estimation of SPAD values in litchi leaves, providing reliable data support for precision fertilization, stress diagnosis, and yield prediction in litchi orchards, as well as theoretical support for promoting the practical application of this technology in smart agriculture.
Why it matches plant phenotyping methodsIoT・UAVマルチスペクトル画像から葉のSPAD値を推定するデータ融合システムとMLSTMモデルを開発・評価しており、植物形質取得手法が研究の中心です。
abstractthis study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo repository containing the study's multi-source SPAD/IoT/multispectral dataset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/18308090 .Open asset ↗zenodo · 18308090lines:427-441Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published5 Jun 2026Proceedings of the National Academy of Sciences of the United States of AmericaCited by 0 · OpenAlex ↗
To improve crop yield and resilience, it is essential to identify the steps limiting [Formula: see text] assimilation rate in plant leaves. The combined effect of multiple traits can be resolved by mechanistic models of the underlying diffusion, biochemistry, and geometry. Yet the widely used simple serial resistance models overlook tissue geometry, and detailed anatomical models are computationally heavy and rely on parameters that are difficult to measure. Here, we develop a framework for systematic species and model comparison, and find that the necessary level of model resolution is species-specific. We apply a minimal reaction-diffusion model and reduce [Formula: see text] fixation in leaves to two key parameters. These parameters comprise a compact phase space in which three rate-limiting regimes emerge naturally: stomatal uptake, intercellular diffusion, and intracellular processes. Mapping diverse plant species into this phase space reveals: 1) dominant colimitations by stomatal and intracellular processes, 2) an equal partition between species that require spatially resolved leaf-scale models and species where intracellular models suffice. Taken together, we present a scalable path for interpreting complex trait data and bridging between models.
Why it matches plant phenotyping methods葉のCO2固定を機構モデルで2パラメータに縮約し、複数種の生理的制限状態と複雑な形質データを解釈・比較する計算フレームワークが中心であるため、植物生理形質の推定・解析手法として含める。
abstractHere, we develop a framework for systematic species and model comparison
Reproduction assets foundThe paper deposits its analysis code/scripts publicly on Zenodo (DOI 10.5281/zenodo.19087524) and GitHub (andreas-stillits/CarbonFixationModel), and uses the publicly deposited Knauer et al. leaf-trait/mesophyll-conductance dataset on Figshare (10.6084/m9.figshare.19681410) to map species into (τ, γ) space. All three, Code · publicCode and Scripts. All code is readily available at our github and at a public
repository (DOI: 10.5281/zenodo.19087524).Open asset ↗Zenodo · 10.5281/zenodo.19087524pdf-raw-page:8 lines:1-60Code / 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 · Crossref · checked 5 Sept 2026
Kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process and provide the means to identify factors limiting photosynthesis. However, the use of large-scale models is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型特異的な光合成動態パラメータを推定するニューラルネットワークであり、植物の生理形質の取得・推定手法の開発と検証が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network that can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper's Data Availability Statement provides a public GitHub repository with the authors' custom code for artificial dataset generation, neural network definition/training, and predicted maize genotype parameters. Zenodo datasets (gas exchange measurements and synthetic training data) are mentioned via DOIs but no Code · publicCustom code for the generation of the artificial dataset as well as code for neural model definition and training is available at https://github.com/pwendering/C4TUNE . This repository also contains the predicted parameters for the maize genotypes.Open asset ↗pwendering/C4TUNElines:223-270Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Chlorophyll estimation is fundamental in plant physiology, crop management, and ecological studies; however, destructive and non-destructive methods are often interpreted interchangeably despite differing measurement principles. The present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions. Significant interspecific variation was observed for all methods. Correlation and regression analyses revealed generally weak relationships among methods, particularly between leaf-level (SPAD, solvent extraction) and canopy-level (GreenSeeker) measurements, reflecting scale-dependent behavior and methodological differences. Moderate associations were observed between SPAD and acetone-extracted chlorophyll for certain traits, whereas GreenSeeker showed poor agreement with solvent-based estimates. Differences between DMSO and acetone extraction further highlighted solvent-specific extraction efficiency. The results demonstrate that chlorophyll estimation methods are not directly interchangeable and should be selected based on study objectives, biological scale, and leaf anatomical characteristics. Species-specific calibration and integration of canopy structural parameters are required to improve cross-method interpretability.
Why it matches plant phenotyping methods複数の葉・キャノピーのクロロフィル推定法を作物種間で比較し、相関、回帰、スケール依存性、互換性を評価しており、植物表現型測定法の技術的検証が中心である。
abstractThe present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions.
Reproduction assets foundThe preprint declares that the datasets generated in this chlorophyll-method comparison study (SPAD, GreenSeeker, acetone and DMSO measurements across eight crop species) are publicly deposited in Figshare under DOI 10.6084/m9.figshare.31817989. This is a paper-specific, publicly actionable phenotype dataset. No authorDataset · publicThe datasets generated during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.31817989Open asset ↗Figshare · 10.6084/m9.figshare.31817989lines:163-185Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / 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
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28 : 6Open asset ↗emf‐creaf/LFMC_FR_CATlines:253-664Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Under small-sample conditions, hyperspectral leaf chlorophyll estimation is affected by high-dimensional collinearity, measurement noise, and cross-source acquisition discrepancies. Existing studies often treat training-distribution expansion and model-error complementarity separately. This study proposed a physically constrained composite spectral augmentation-weighted ensemble framework for reproducible small-sample chlorophyll estimation. Methods Using 1,113 valid spectrum-label pairs from the leaf subset of the GreenHySpectra dataset in the 400-1000 nm range, spectra and chlorophyll reference values were matched by sample identifiers and divided into training and validation sets. Low-magnitude Gaussian noise and smooth wavelength warping were applied only to the training set. XGBoost, partial least squares regression, and ridge regression were optimized with Optuna using a CMA-ES sampler, and ensemble weights were calibrated by Bayesian optimization. An independent external set of 90 tomato leaf samples was used to evaluate transferability. Results Composite augmentation improved model stability and reduced validation error relative to the non-augmented baseline. The weighted ensemble model achieved the best internal performance, with R² = 0.6392 and RMSE = 8.8883. On the external samples, the model achieved R² = 0.498 and RMSE = 9.801. Discussion The proposed workflow integrates physically plausible augmentation, heterogeneous learner complementarity, and independent external validation. The external results indicate partial cross-source transferability while highlighting distributional and measurement-chain discrepancies that still limit absolute generalization.
Why it matches plant phenotyping methods葉のクロロフィル量という植物形質をハイパースペクトルから推定する手法を開発し、外部データで転移性を検証しており、表現型取得・推定が研究の中心である。
titleHyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning.
Reproduction assets foundThe paper's phenotyping analysis is built on the public GreenHySpectra hyperspectral dataset (leaf subset, 1,113 spectrum–chlorophyll pairs), which is a paper-specific, publicly available input with an authors' cited URL matching the allowed list. No author analysis code, trained models, or public deposit of the 90-solDataset · publicAvatarr05 ( 2023 ). GreenHySpectra/GreenHyperSpectra dataset (Hugging Face Datasets) [WWW document] . Available online at: https://huggingface.co/datasets/Avatarr05/GreenHySpectra (Accessed May 15, 2026).Open asset ↗Hugging Face Datasets · Avatarr05/GreenHySpectralines:749-785Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Colorimetric analysis of food using the CIELab/Ch colour space (i.e., from digital images of samples) is an accessible, non-destructive method for carotenoid and anthocyanin content prediction. Literature presents very well-fit, but rudimentary, models for pigment estimation (e.g., single/multiple linear regressions). However, standardised methods that statistically account for the high multicollinearity between CIELab/Ch colour parameters, varying light conditions and colour calibration, and samples with high genotypic variability are lacking. Methods An image analysis optimisation was developed for the prediction of carotenoid and anthocyanin content of 16 carrot genotypes of different colours. Samples were photographed under six light conditions with a digital camera and image colour was calibrated before analysis with the CIELab/Ch colour space. Total pigment contents and individual carotenoid contents were analysed chemically via spectrophotometry and high-performance liquid chromatography, respectively. Partial least squares (PLS) regressions were used to assess the colour-pigment relationships to correct for high multicollinearity amongst the independent variables (CIELab/Ch colour parameters). Results/discussion The PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions. The two models are suggested as robust approaches to total pigment prediction with multi-dimensional colour spaces, varying light conditions, and for a sample group of high genotypic variability. The carrot samples proved to have very high genetic diversity within each cultivar, resulting in unsatisfactory models for prediction of individual carotenoids ( ca. R 2 = 0.45) under the default light condition. However, all the results can be used to expand databases (towards artificial intelligence) and aid breeding programmes in search for higher concentrations of these interesting antioxidants for human health.
Why it matches plant phenotyping methodsニンジン試料の画像色解析を最適化し、化学分析値を用いてカロテノイド・アントシアニン含量を予測する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThe PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions.
Reproduction assets foundThe authors deposited the paper's data and protocols in public repositories (DOI links in the Data availability statement). The anthocyanin quantification protocol is explicitly linked (10.34894/BTPTSV), and the other two DOIs (10.34894/P37WCL, 10.34894/OUURRH) are stated to hold the paper's data. No separate author's'Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/P37WCLlines:641-686Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/OUURRHlines:641-686Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Genomic selection (GS) can accelerate crop breeding and enhance selection efficiency. However, accurately predicting genomic estimated breeding values (GEBVs) for complex traits and applying GS in diverse environments remains challenging. To address these issues, we developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions. This method offers precise predictions of phenotypic performance for complex traits, identifies haplotypes associated with desirable phenotypes, and enables prediction of optimal haplotypes tailored to specific environments. We evaluated the approach using a dataset of 855 barley lines, with phenotypic data for grain yield and flowering time collected across multiple environments. The model incorporated 30,543 SNPs, nine soil parameters, and six daily environmental variables, achieving high prediction accuracies, with correlation coefficients of 0.93 for flowering time and 0.82 for grain yield. Our method identified 10 haplotype blocks significantly associated with flowering time and 13 blocks with grain yield, collectively accounting for over 90% of the total genetic variance. Additionally, we predicted the phenotypic effects of each haplotype and identified elite varieties carrying the most favourable haplotypes for crossing design and selection. The method also allows prediction of untested genotype × environment combinations, enabling selection of optimal genotypes for targeted environments. To facilitate its application, we developed a web-based interface (accessible at [https://penghaowang.shinyapps.io/shinygui/]), which enables breeders to identify optimal haplotypes and the varieties that carry them, streamlining the process of haplotype-based, environment-informed breeding. We note that the reverse prediction framework is currently applied on a single-trait basis and does not resolve multi-trait trade-offs such as between flowering time and yield, which remains a topic for future extensions.
Why it matches plant phenotyping methods複雑形質の表現型性能を遺伝子型・環境情報から予測する新規計算手法を開発し、オオムギの収量・開花期で評価している。ウェブインターフェースも提供され、形質推定ワークフローが中心である。
abstractwe developed a novel hybrid method capable of modelling gene-gene and gene-environment interactions.
Reproduction assets foundThe paper deposits its barley genotype, phenotype, and environmental datasets at three DOI repositories, and its analysis source code on GitHub, plus a public Shiny web tool.Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000010lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000003lines:31-42Dataset · publicDetailed information on all experimental lines, including their genotypes, phenotypic, and environmental data, is available at https://doi.org/10.60867/00000010 , https://doi.org/10.60867/00000003 , and https://doi.org/10.60867/00000011 , respectively.Open asset ↗10.60867 · 10.60867/00000011lines:31-42Code · publicAll the data and source codes have been uploaded to GitHub and can be accessed under the GNU Open License at: https://github.com/pwang2019/GxE_Model .Open asset ↗github.com/pwang2019/GxE_Modellines:196-205Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Wheat kernel hardness, vitreousness, and creaseness are key determinants of milling performance, yet they reflect different physical scales of grain structure and are not necessarily coupled. Methods: We developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat. Pixel-level spectral unmixing resolved glassy, intermediate, and mealy endosperm components within individual kernels, enabling vitreousness to be expressed as a continuous spatial index. Results: The hyperspectral-derived vitreousness index showed moderate associations with kernel protein content and the protein-to-starch ratio, consistent with variation in endosperm packing density, but weak relationships with kernel hardness and crease geometry. Kernel hardness, primarily determined by puroindoline genotype, showed limited association with bulk protein and starch composition. Crease geometry, quantified using composite indices from RGB images, captured macroscopic grain features largely independent of both hardness and vitreousness. Discussion: These results demonstrate that hardness, vitreousness, and creaseness represent complementary but largely independent dimensions of grain quality, corresponding to molecular-scale adhesion, mesoscale packing, and macroscopic geometry, respectively. The proposed framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity, enabling improved digital phenotyping for wheat breeding and quality assessment.
Why it matches plant phenotyping methodsハイパースペクトル画像とスペクトルアンミキシングを用いて小麦粒の硝子質を定量するデジタル表現型解析フレームワークを開発しており、形質取得手法が中心的である。
abstractWe developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat.
Reproduction assets foundThe paper's data availability statement deposits full hyperspectral image cubes and RGB image datasets on Figshare, and the supplementary material includes Python analysis scripts (Supplementary Code S1–S2) and processed feature tables (Supplementary Table S3) directly reproducing the paper's phenotyping measurements.Dataset · publicfull hyperspectral image cubes and associated RGB imagedatasets are available via Research Datas 1 – 3 at Figshare: https://doi.org/10.6084/m9.figshare.31259530Open asset ↗Figshare · 10.6084/m9.figshare.31259530lines:151-201Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study developed a model to predict zeaxanthin content in peppers using multispectral imaging and chemical data. A one-dimensional convolutional neural network (1D CNN) model was identified as the optimal single-modal model after comparing four machine learning algorithms. On the prediction dataset, the model achieved a determination coefficient ( Rp 2 ) of 0.7639. Building upon the 1D CNN framework, a multimodal feature fusion model (MCSF) was constructed by integrating the chemical measurements of capsanthin and total carotenoid contents using a multilayer perceptron. This enhanced model demonstrated excellent predictive accuracy and robustness, with Rp 2 values of 0.9318 and 0.9211 across different spectral ranges. For high-throughput detection purposes, a simplified model that replaced measured capsanthin with a comprehensive red index still performed well, with an Rp 2 of 0.8912 and an RPD of 3.11. This strategy provides a new solution for the efficient spectral detection of plant chemicals affected by multicollinearity in their absorption spectra.
Why it matches plant phenotyping methodsマルチスペクトル画像と機械学習を用いて、トウガラシ果皮のゼアキサンチン含量という植物器官の形質を非破壊・高スループット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中心である。
abstractThis study developed a model to predict zeaxanthin content in peppers using multispectral imaging and chemical data.
Reproduction assets foundThe paper's data availability statement explicitly states that the datasets (multispectral imaging and chemical trait measurements) and the main model code are publicly available in the authors' GitHub repository, which is an allowed URL.Dataset · publicThe datasets and the main model code are available online at https://github.com/liang-wei-tian/Chili-Peppers-Zeaxanthin.Open asset ↗liang-wei-tian/Chili-Peppers-Zeaxanthinhtml-lines:303-325Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Quantifying the kinetics of net CO2 assimilation (A) and stomatal conductance (gs) under fluctuating light typically relies on gas exchange measurements, which are slow and thus unsuited for high-throughput phenotyping. As a result, faster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale. However, first the relationship between non-steady-state parameters must be examined in greater detail. In this study, we aimed to determine whether variations in non-steady-state values of chlorophyll fluorescence and leaf temperature reflect differences in key gas exchange traits under fluctuating light conditions. Here, the correlations between the times required for a change in non-steady-state A, gs, operating efficiency of PSII (ΦPSII), and leaf temperature (Tleaf) during stepwise changes in light intensity were evaluated across nine plant species. Both steady-state and non-steady-state photosynthetic traits varied significantly among species. Overall, we found significant positive correlations between non-steady-state A and ΦPSII for time to 50% and 90% of final steady-state values (t50; r2 = 0.70) and (t90; r2 = 0.33). The t90 of gs and that of Tleaf were also significantly correlated after both increases (r2 = 0.45) and decreases (r2 = 0.61) in light intensity. Our findings suggest that the times required for a change in ΦPSII (particularly t50) and Tleaf (particularly t90) can be used as indicators of dynamic A and gs, respectively, facilitating faster phenotyping of the complex processes of photosynthesis and stomatal conductance kinetics in the future.
Why it matches plant phenotyping methods非定常クロロフィル蛍光と葉温を用いて光合成・気孔コンダクタンス動態を推定する高速フェノタイピング手法を評価しており、相関検証が研究の中心である。
abstractfaster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale.
Reproduction assets foundThe paper's primary gas exchange, chlorophyll fluorescence, and leaf temperature phenotyping data are explicitly deposited in the WUR data repository (DOI 10.17887/WUR01-TMWYJN), stated in the Data availability section. No author analysis code repository is stated; the agricolae R package is a generic library, not a论文-Dataset · publicThe primary data and associated metadata are publicly available through the WUR data repository at https://doi.org/10.17887/WUR01-TMWYJN .Open asset ↗WUR data repository · 10.17887/WUR01-TMWYJNlines:406-446Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Protein content is an important quality trait in sorghum that influences breeding approaches, end-use applications, and market value. Influenced by genetic, agronomic, and environmental variability, sorghum is characterized by its wide variation in composition, which may also be evident in kernels from the same sample. This study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR). Applying different pre-processing techniques to the spectra collected from intact kernels, the calibration models were developed using partial least squares regression and the reference protein content values obtained from the LECO combustion method. The best model was obtained using multiplicative scatter correction as pre-processing, resulting in a standard error of prediction of 0.83% and a relative predictive determinant of 3.40. These were indicative of the good predictive ability of the model and the instrument to be applied in quality control and sorting applications. These results highlight the potential of SKNIR to capture the inter-kernel variability in sorghum protein content and enhance screening for grain quality in breeding and grain processing.
Why it matches plant phenotyping methods単一穀粒NIRによるソルガム種子のタンパク質含量推定法を開発・評価しており、植物器官の形質取得が研究の中心である。
abstractThis study developed and evaluated a method for a non-destructive and rapid prediction of protein content in individual sorghum kernels using single-kernel near-infrared spectroscopy (SKNIR).
Reproduction assets foundThe paper's Data Availability Statement deposits the original single-kernel NIR spectra and reference protein data openly in Ag Data Commons, a paper-specific public dataset directly reproducing this study's measurements.Dataset · publicThe original data presented in the study is openly available in Ag Data Commons [https://doi.org/10.15482/USDA.ADC/31316725].Open asset ↗Ag Data Commons · 10.15482/USDA.ADC/31316725html-lines:226-278Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Code / 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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D t v il ility Part of the datasets generated and analyzed during the current study are
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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 · checked 5 Sept 2026
Metabolic processes are essential for regulating and maintaining developmental transitions. However, the distinct metabolite-driven mechanisms that are crucial for development remain poorly characterized due to inherent challenges in measuring their localization and function in situ. We applied desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to generate near single-cell resolution (50-80 µm) images of metabolites in the maize root tip, which has a well-characterized longitudinal developmental gradient. We developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns. We employed this method to compare developmental enrichment of metabolites in Oaxacan Green, a salt-resilient maize variety, to B73, which is salt sensitive. DIMPLE uncovers specific differences in individual mass signatures and overall enrichment patterns between these varieties. Further characterization of these differences revealed meristem enrichment of D-erythrose, a metabolite that can improve stress tolerance in maize. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, informing biological hypotheses related to plant growth and stress response.
Why it matches plant phenotyping methods植物根端の発達勾配に沿った代謝物分布を画像化・解析する計算ツールを開発しており、植物の発達状態やストレス応答に関わる表現型抽出が研究の中心である。
abstractWe developed a new computational tool, called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE), which processes mass signatures along linear gradients and clusters metabolites based on their developmental enrichment patterns.
Reproduction assets foundThe paper's authors publicly deposited the DIMPLE analysis code and raw DESI-MSI data on the Dickinson Lab GitHub and Zenodo, as stated in the Technical aspects and Data availability sections.Code · publicThe full R code analysis can be found in the Dickinson Lab Github at https://github.com/dickinsonlab.Open asset ↗dickinsonlabhtml-lines:198-204Code · publicSource code and raw data for DIMPLE are available on the Dickinson Lab GitHub (https://github.com/dickinsonlab) and at https://zenodo.org/records/17187822.Open asset ↗17187822html-lines:198-204Code / 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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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 · Europe PMC · checked 15 Sept 2026
ABSTRACT Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1,423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (⍴=0.3, p 0.5, p<1x10 -16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.
Why it matches plant phenotyping methodsタワー型連続ハイパースペクトルセンシングを用いて多数の遺伝子型の生理・構造形質を時系列で取得し、表現型解析とGWASに substantively 適用しているため、フェノタイピング手法が中心的である。
abstractHyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales.
Reproduction assets foundThe paper's hyperspectral phenotype dataset (tower-based hyperspectral traits for 505 Populus trichocarpa genotypes) is explicitly stated to be publicly available through the Oak Ridge National Laboratory LabKey data portal with DOI 10.25983/CBI/3012775. This is a paper-specific, public, actionable phenotype dataset. ADataset · publicHyperspectral phenotype data are publicly available through the Oak Ridge National Laboratory LabKey data portal (DOI: 10.25983/CBI/3012775).Oak Ridge National Laboratory LabKey data portal · 10.25983/CBI/3012775lines:163-201Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
A central problem in soft and biological physics is how molecular-scale activity and remodelling coarse-grain into emergent mechanical laws at larger scales. In growing cell walls (polymeric composite materials that surround 90% of living organisms’ cells) irreversible deformation is not controlled by elastic stress alone. Instead, growth depends on the interplay between energy storage, dissipation, and the local timing of viscoelastic relaxation. Although dynamic atomic force microscopy (AFM) resolves storage and loss moduli ( E′, E″) of living walls at nanometre resolution, these observables have remained phenomenological and disconnected from constitutive field variables. Here we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ . By analysing the spatial gradients of E′ and E″, we uncover organized mechanical heterogeneities governed by cellular confinement and stress focusing. We demonstrate that the local relaxation time is encoded directly in the coupling between storage and dissipation, yielding the pointwise relation τ = (1/ ω ) ∂ E ’/∂ E ’’, where ω is the indentation frequency. This relation enables model-independent extraction of mechanical timescales and establishes a general route from nanoscale non-equilibrium rheology to continuum descriptions of growth in living and active soft materials. Significance How molecular-scale activity gives rise to tissue-scale form is a central challenge in biological physics. Although growth is fundamentally a non-equilibrium mechanical process, experimental measurements at the nanoscale have not been directly connected to the constitutive parameters that govern morphogenesis. We introduce a framework that converts dynamic atomic force microscopy maps of storage and loss moduli into spatially resolved fields of stiffness, viscosity, and relaxation time in living cell walls. By revealing that mechanical relaxation is encoded in the local coupling between elastic storage and viscous dissipation, our work provides a route from nanoscale rheology to growth-relevant mechanical timing. This establishes a quantitative bridge between molecular remodeling and continuum mechanics, enabling direct experimental constraints on multiscale theories of morphogenesis.
Why it matches plant phenotyping methods生きたArabidopsis細胞のAFM測定を物理ベースで反転し、剛性・粘性・緩和時間という植物細胞壁の機械的形質を空間的に抽出する新規フレームワークが研究の中心である。
abstractHere we introduce a physics-based inversion framework that converts AFM measurements of epidermal cells of living Arabidopsis plants into spatially resolved fields of stiffness k , viscosity η , and relaxation time τ .
Reproduction assets foundThe paper's custom AFM viscoelastic analysis code is explicitly deposited and publicly available on GitHub (ForceMetric). The underlying AFM phenotype/measurement data are only available upon request, not publicly.Code · publicAFM data were analysed in Python 3.5 using previously described routines [34] (code
available at https://github.com/jcbs/ForceMetric ).Open asset ↗jcbs/ForceMetricpdf-page:14 lines:1-56Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Lettuce ( Lactuca sativa ) is an important field crop, but our understanding of its phenotypic variation and underlying genetics under natural field conditions remains limited, posing challenges for identifying effective crop breeding targets. Longitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions. In this study, we used hyperspectral imaging to assess the phenotypic variation of almost 200 different field-grown lettuce varieties, following the same plants from just after seedling- to flowering-stage. With automated image processing, we extracted a wide range of spectral phenotypes related to metabolite content, growth efficiency, and environmental stress responses, creating a multi-dimensional time-resolved data set. Principal component analysis (PCA) revealed the major axes of spectral variation over time, and highlighted differences in spectral patterns among lettuce genotypes. Integrating on-site weather data, we modelled G×E interactions of reflectance, revealing regions of the lettuce vegetation spectrum that are primarily shaped by genotype and/or environment. We estimated phenotypic plasticity in response to time, temperature and rainfall using best linear unbiased predictions (BLUPs), capturing genotype-specific developmental trajectories and responses to the environment. We used genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs) of PC-based, single and BLUP-based phenotypes, disentangling the genetic architecture of spectral lettuce phenotypes from major axes of variation down to single wavelength spectral plasticity. These findings provide new insights into the genome-wide genetic regulation and dynamics of spectral phenotypes in field grown lettuce.
Why it matches plant phenotyping methods圃場レタスを対象に、縦断ハイパースペクトル画像と自動画像処理でスペクトル形質を抽出するフェノタイピング手法・データセットが研究の中心である。
abstractLongitudinal hyperspectral phenotyping allows for non-invasive monitoring of crop performance under diverse agricultural conditions.
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 · publicScripts used in this study can be found at https://github.com/SnoekLab/Hyperspec_Mehrem_etal_2025.Open asset ↗SnoekLab/Hyperspec_Mehrem_etal_2025pdf-page:9 lines:1-31Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Sun-induced fluorescence (SIF) has emerged as a promising tool for tracking photosynthetic dynamics, yet its application in monitoring biotic stress remains underexplored in field conditions. In this study, we investigated the effects of Cercospora leaf spot (CLS), a destructive foliar disease of sugar beet (Beta vulgaris L.), for which traditional monitoring methods often fail to capture subtle disease effects or distinguish between structural and physiological stress responses. CLS infection was induced through artificial inoculation and manually scored. Canopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform. The results demonstrate that SIF effectively detects CLS in sugar beet, with responses comparable with structural and disease- specific indices. Despite visible symptoms, PSII efficiency (Fq'/Fm') remained stable across treatments, indicating limited impairment of leaf photosynthetic efficiency at early stages. However, the canopy-level electron transport rate varied significantly and showed a strong relationship with red and far-red SIF, suggesting that CLS primarily affects canopy light absorption and utilization. After structural normalization, SIF yield remained largely unchanged, confirming that observed SIF reductions were mainly driven by canopy structural alterations. Overall the study demonstrates the effectiveness of SIF for large-scale disease monitoring and integration into high-throughput phenotyping, while also revealing structural and physiological factors influencing the SIF signal under disease stress.
Why it matches plant phenotyping methodsSIFおよびPSIIセンサーを搭載したハイスループット表現型解析プラットフォームで、サトウダイコンの病害状態と構造・生理応答を評価する手法の実質的な適用・検証が中心である。
abstractCanopy-level reflectance indices were acquired along with red and far-red passive SIF signals and active PSII efficiency traits using FloX and LIFT sensors mounted on an automated high-throughput phenotyping platform.
Reproduction assets foundThe paper's phenotyping dataset (SIF, reflectance indices, LIFT PSII traits, disease scores from the CLS sugar beet field trial) is deposited in the open access Jülich DATA repository under DOI 10.26165/JUELICH-DATA/FOQOFI. No separate author analysis code repository with explicit availability language is stated; R/lmeDataset · publicThe dataset has been deposited in the open access Jülich DATA reposi ease using UAV-supported image data and deep learning. Sugar Industry
tory: https://doi.org/10.26165/JUELICH-DATA/FOQOFI. 147, 79–86.
Ispizua Yamati FR, Bömer J, Noack N, Linkugel T, Paulus S, Mahlein
A-K. 2025. Configuration of a multisensor platform for advanced plant phe
References notyping and disease detection: case study on cercospora leaf spot in sugar
Ač A, Malenovský Z, Olejníč ková J, Gallé A, Rascher U, Mohammed beet. Smart AgricultOpen asset ↗Jülich DATA · 10.26165/JUELICH-DATA/FOQOFIpdf-layout-page:14 lines:52-72Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
1 Summary Zinc (Zn) deficiency limits rice productivity and poses a risk to human health, particularly in populations reliant on rice-based diets. Although rice germplasm exhibits wide variation in Zn-deficiency tolerance, the underlying physiological mechanisms remain poorly resolved. Evidence across the literature for Zn-deficiency–induced secretion of 2′-deoxymugineic acid (DMA) is inconsistent. This study clarifies the role of DMA secretion as a Zn-deficiency stress response. We developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates. Five rice genotypes with contrasting Zn-deficiency tolerance were grown hydroponically and DMA secretion measured. Zn-deficiency increased DMA exudation across all genotypes, with sensitive genotypes also showing higher secretion compared with control, supporting DMA’s role as a general response to Zn stress rather than being restricted to efficient genotypes. Fold-change responses exceeded previous studies, likely due to more severe stress exposure. Our results confirm that DMA secretion is induced under Zn-deficiency in rice as part of the micronutrient stress response. However, the lack of increased Zn uptake indicates that additional tolerance mechanisms are involved. These findings reconcile inconsistencies in the literature and position DMA secretion as an important, but not exclusive, component of Zn-deficiency adaptation in rice.
Why it matches plant phenotyping methodsイネ根滲出液中のDMAを選択的に検出するLC–MS法を開発・検証し、亜鉛欠乏応答という植物生理状態を測定しているため、化学分析が単なる付随測定ではなく中心的な方法貢献である。
abstractWe developed and validated a sensitive LC–ESI–Q–TOF–MS method for selective detection of DMA in rice root exudates.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this study (DMA exudation and Zn uptake measurements in rice).Dataset · publicthe experiments, developed the
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methods and analysed the results. The experimental data were collected by C.R. assisted by
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G.L.M., C.T. and D.J.W. Data analysis and writing of paper by all authors.
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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 in
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this version posted March 18, 2026.
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https://doi.org/10.64898/2026.03.16.71158Open asset ↗Zenodo · 18184803pdf-raw-page:21 lines:1-47Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana benthamiana , a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes. Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the contributions of six quenching components (qE V , qE A, qE Z, qE L, qZ, and qI) across different genotypes. It also suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries, aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop yields under dynamic light environments.
Why it matches plant phenotyping methods葉の蛍光寿命測定を基盤に、NPQ成分を分離・定量するモデルを構築しており、植物の光防護状態を取得・抽出する方法が研究の中心です。
abstractBased on these measurements, we construct a fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes.
Reproduction assets foundThe paper's fluorescence lifetime/pigment phenotyping data and the NPQ model code are both publicly deposited on Zenodo (DOI 10.5281/zenodo.16755870), per explicit Data availability and Code availability statements.Dataset · publicThe data supporting the findings of this study are available within the article and at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code · publicThe codes for NPQ models used in this study are available at https://doi.org/10.5281/zenodo.16755870 .Open asset ↗Zenodo · 10.5281/zenodo.16755870lines:171-237Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / 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 · checked 15 Sept 2026
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k -means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methods植物細胞のカルシウム動態を定量するマイクロ流体・蛍光イメージング系と自動画像セグメンテーションを開発し、植物の生理状態を抽出する方法が研究の中心である。
abstractHere, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens
Reproduction assets foundThe paper's Data Availability Statement explicitly makes analysis scripts and sample data publicly available on the authors' GitHub repository (albrechtLab/moss_calcium), and the MDPI supplementary materials (plants-15-00582-s001.zip) contain the paper's timelapse calcium-imaging videos and supplementary figures. Raw/全Code · publicData are available upon request. Analysis scripts and sample data are publicly available at https://github.com/albrechtLab/moss_calcium (accessed on 1 January 2026).Open asset ↗albrechtLab/moss_calciumlines:188-251Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.
Why it matches plant phenotyping methods植物と微生物の界面を共焦点画像から自動検出・分割し、ハウストリア等を定量解析する深層学習手法が中心であり、植物感染状態の画像ベース表現型解析に該当する。
abstractwe present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images.
Reproduction assets foundThe paper's HFinder pre-trained models (trained phenotyping models/checkpoints) are explicitly deposited on Zenodo with a public DOI matching an allowed URL. The training image dataset is also stated to be publicly available on Zenodo, but no separate authors' URL for it is given in the supplied blocks, so only the preModel / weights · publicFor
convenience, HFinder is distributed with pre-trained models that can be applied directly to confocal image
analysis (available on Zenodo: https://doi.org/10.5281/zenodo.17091805)Open asset ↗Zenodo · 10.5281/zenodo.17091805pdf-page:5 lines:1-47Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Common beanStem / branchObject detectionPhysiological trait estimationGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force measurement systems have limited capacity to capture weak forces in freely moving plant organs-such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Unlike many force measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing extraction of the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (e.g. growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with Phaseolus vulgaris shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not- an open question since Darwin's first observations.
Why it matches plant phenotyping methods自由に動く植物器官が発生する微弱な力を、カメラ追跡と力抽出により定量する測定システムを開発・実証しており、植物表現型の取得方法が研究の中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Reproduction assets foundThe authors deposited the full analysis workflow (data and code) for five example force-measurement trajectories on Zenodo, publicly accessible via DOI 10.5281/zenodo.15545548. This directly reproduces the paper's camera-based plant force phenotyping measurements and computational analysis. Other experimental data are仅Dataset · publicof interest
None declared.
Funding
YM acknowledges support from the Israel Science Foundation Research
Grant (ISF) no. 2307/22, and ERC grant GROWsmart 101165101. AO
acknowledges support from the Colton Foundation scholarship.
Data availability
We have put the full workflow for five example trajectories on a Zenodo
repository (https://doi.org/10.5281/zenodo.15545548; Ohad and
Meroz, 2025). Other experimental data are available upon request.
References
Autumn K, Liang YA, Tonia Hsieh S, Zesch W, Chan WP, Kenny TW,
Fearing R, Full RJ. 2000. Adhesive force of a single gecko foot-hair.
Nature 405, 681–685.
Backholm M, Bäumchen O. 2019. Micropipette force sensors for in vivo
force measurementsOpen asset ↗Zenodo · 10.5281/zenodo.15545548pdf-raw-page:9 lines:1-95Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by available field surveys and trait measurements. Recent expansions in biodiversity data aggregation-including vegetation surveys, citizen science observations, and trait measurements-offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution. Our approach achieves correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance understanding of plant community properties and ecosystem functioning, while serving as tools for modeling global biogeochemical processes and informing conservation efforts. Our framework highlights the power of crowdsourced biodiversity data in addressing longstanding extrapolation challenges in global plant trait modeling, with continued advancements in data collection and remote sensing poised to further refine trait-based understanding of the biosphere.
Why it matches plant phenotyping methods地球観測データと多様な植物形質データを統合して植物形質を推定・検証する方法が研究の中心であり、単なる生態学的測定ではないため。
abstractcombining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km 2 resolution
Reproduction assets foundThe paper's own trait maps (Zenodo), source data (Zenodo), and analysis code (GitHub + Zenodo archive) are explicitly public. Core trait inputs (TRY, sPlot) are restricted-access and require requests; GBIF citizen-science occurrence datasets are public inputs.Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗GitHub · dluks/cit-sci-trait-mapslines:249-343Code · publicThe code used to process data, train models, and generate trait maps in this study is available at https://github.com/dluks/cit-sci-trait-maps and archived on Zenodo at https://doi.org/10.5281/zenodo.18269445 .Open asset ↗Zenodo · 10.5281/zenodo.18269445lines:249-343Dataset · publicSource data underlying the figures are available at https://doi.org/10.5281/zenodo.18108765 .Open asset ↗Zenodo · 10.5281/zenodo.18108765lines:240-248Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves. These data have been used to develop machine learning models for predicting diverse plant traits, yet key challenges remain. We collected hyperspectral reflectance data together with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines grown over three seasons. Using these data, we systematically (1) compare the performance of PLSR and SVR across a wide range of traits, including also slow fluorescence kinetics, (2) assess model generalizability and transferability, and (3) investigate how different aggregation strategies affect predictive accuracy. Based on a nested cross-validation framework, single cross-validation with MSE as metric performed comparably to repeated cross-validation or PRESS-based calibration. Optimal performance of trait-specific predictions was found to be dependent on the combination of model and data aggregation levels. Structural and biochemical traits showed the best generalizability and transferability, whereas physiological traits, particularly those derived from gas exchange and fluorescence kinetics, exhibited markedly reduced transferability. Together, these results provide a rigorous benchmark for evaluating machine learning models for trait prediction from hyperspectral reflectance data, and highlight both the opportunities and limitations for achieving robust generalization across diverse environments and genotypes.
Why it matches plant phenotyping methodsハイパースペクトル反射データから植物形質を予測する機械学習手法を、複数形質・環境・遺伝子型で系統的に比較し、一般化性と転移性を厳密にベンチマークしているため、方法論が中心である。
abstractHyperspectral reflectance provides rapid, non-destructive phenotyping of plant leaves.
Reproduction assets foundThe paper's Data availability statement explicitly deposits all code and raw hyperspectral/trait data in a public GitHub repository, matching an allowed URL.Code · publicAll code and raw data to ensure reproducibility of the results can be accessed at: [https://github.com/Rudan-X/HyperspectralML](https:/github.com/Rudan-X/HyperspectralML).Open asset ↗Rudan-X/HyperspectralMLlines:158-246Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Precision management in high-density orchards requires individual-tree, nondestructive monitoring of canopy nitrogen concentration (CNC), but hyperspectral applications are limited by two factors: unmodeled vertical stratification of CNC within 3D canopies and mixed-pixel effects near canopy boundaries. We develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards. With this framework, we quantified layer-specific nitrogen-spectral relationships and assessed mixed-pixel effects across canopy positions. Stratified sampling, continuous wavelet transform (CWT), and partial least squares regression (PLSR) with variable importance in projection (VIP)-based band selection were used for spectral optimization, and K-means was applied to isolate representative canopy pixels. Field experiments over two consecutive years (2023-2024) revealed consistent CNC gradients, with the lower canopy exceeding the upper by 0.5-9.5 % across fertilization treatments. CWT-2 delivered the most accurate and robust performance across years. VIP-PLSR indicated layer-dependent CNC-informative wavelengths spanning the visible, red-edge, and near-infrared regions, with scale-dependent cross-layer overlap after CWT. Pixel clustering revealed distinct spatial structure: canopy-interior pixels exhibited characteristic vegetation spectra and achieved R 2 val of 0.69-0.76, substantially outperforming boundary-affected pixels with R 2 val of 0.48-0.57. These results demonstrate that coupling spectral feature optimization with layer-specific modeling and clustering-based pixel screening improves the accuracy of tree-level CNC estimation in complex canopies. The proposed framework provides a mechanistic and operational basis for robust biochemical retrieval in structurally complex orchard systems.
Why it matches plant phenotyping methodsUAVのRGB・ハイパースペクトルデータを統合し、個体樹の樹冠窒素濃度という植物形質を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心です。
abstractWe develop a cross-modal framework that co-registers RGB-derived 3D point clouds with hyperspectral orthomosaics, enabling individual-tree localization in dense orchards.
Reproduction assets foundThe paper's data availability statement explicitly deposits the apple canopy nitrogen concentration dataset and canopy original-reflectance validation dataset in a public GitHub repository, which is a paper-specific, publicly actionable phenotyping asset. No author analysis code or trained models are explicitly stated.Dataset · publicThe apple CNC dataset and the canopy OR independent validation dataset are available at https://github.com/Chenb94115/Plant-Phenomics . Additional supporting data are available from the corresponding author upon reasonable request.Open asset ↗Chenb94115/Plant-Phenomicslines:278-377Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Understanding plant responses to abiotic stress requires an insight into plant redox activity. This study proposes a novel and cost-effective method for assessing the redox state of plants. Methods The method utilizes the electrochromic properties of polyoxometalate phosphomolybdic acid hydrate (PMA). PMA is reduced proportionally by glutathione (GSH) and ascorbic acid (AsA), which results in a measurable color change. The validity of this method was confirmed through empirical experimentation in Arabidopsis thaliana under conditions of salinity and UV radiation. Results Salinity treatments revealed a non-significant, two-phase trend in redox activity with an increase at moderate levels followed by a decrease. UVC radiation led to a substantial decrease in redox activity, indicating distress. In contrast, UVA promoted resilience, also known as eustress. Notably, UVB significantly increased redox activity, suggesting the activation of an emergency antioxidant response. Discussion A demonstrable correlation has been identified between the redox activity of plants and various stress types. This correlation facilitates the classification of responses into two distinct categories: adaptive eustress and detrimental distress. This advancement contributes to the enhancement of plant metabolic and stress tolerance evaluation.
Why it matches plant phenotyping methods植物のレドックス状態を測定する新規手法を開発し、シロイヌナズナでストレス条件下の妥当性を検証しており、表現型取得が研究の中心である。
abstractThis study proposes a novel and cost-effective method for assessing the redox state of plants.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.17795112) containing the study's datasets, which underpin the PMA-based redox activity measurements (absorbance at 852 nm) in Arabidopsis thaliana under salinity and UV stress. No author analysis code or trained models are attDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.5281/zenodo.17795112 .Open asset ↗zenodo · 10.5281/zenodo.17795112lines:416-483Code / 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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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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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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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
Crop traits are the integrated outcome of genetic variation, environmental conditions, and their complex interactions, rendering accurate prediction from genetic markers alone a persistent challenge. Here, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes from a multiple-parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model in predicting the photosynthetic rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP enabled us to survey genetic variability in enzyme kinetic parameters, which can be used to identify targets for the improvement of photosynthesis. This approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the accuracy of photosynthetic trait predictions.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワーク自体が研究の中心であり、遺伝マーカーとガス交換測定を統合した植物生理形質の推定手法を開発・評価している。
abstractHere, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthetic traits across genotypes
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and data to ensure the reproducibility of the results can be accessed at https://github.com/Rudan-X/KineticGP .Open asset ↗GitHub · Rudan-X/KineticGPlines:231-264Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding 87.35% accuracy for tomato maturity and R 2 = 0.878 for fruit firmness. In cross-software benchmarks, PlantSpecLab matched the accuracy of ENVI and code-based Python pipelines while reducing end-to-end workflow time by >90% (>80 min to ∼8 min). PlantSpecLab provides a transparent, efficient analytical environment that lowers the technical barrier to HSI analysis. This enables researchers to prioritize biological interpretation while minimizing computational overhead.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から表現型特徴を抽出・モデル化するオープンソース基盤を開発し、既存ソフトウェアとの性能・処理時間を比較検証しているため、フェノタイピング手法が中心である。
abstractwe developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface.
Reproduction assets foundThe authors explicitly state the PlantSpecLab source code (the platform used for all phenotyping analyses in the paper) is publicly available on GitHub under an MIT license, with a versioned release archived alongside the data.Code · publicsis. Jingye Liu: Data curation. Chu Zhang: Supervision, Writing—review & editing. Wei Xu: Supervision, Funding acquisition, Writing—review & editing.
Data and code availability
All data and code that support the findings of this study will be made publicly available upon publication. The PlantSpecLab source code is available at https://github.com/Another-Train/PlantSpecLab (MIT License), with a versioned release archived alongside the data.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62265015 and 32360750), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant No. 2023B02028-3), and the Finance Plan Project of the 8th Division of the Open asset ↗Another-Train/PlantSpecLablines:458-487Code / 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 confirmedEurope PMC · checked 6 Sept 2026
This dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple ( Malus domestica ) trees. This article and the dataset it describes accompany an original research article submitted to Computers and Electronics in Agriculture entitled "Investigating the limits of spectroscopy for the estimation of foliar N and P in apple" [1]. Data were collected from a controlled potted experiment involving 150 'Golden Delicious' apple trees grown under varying nutrient supply regimes, including full nutrient supply, nitrogen- and phosphorus-deficient treatments, and trees infected with ' Candidatus Phytoplasma mali'. The experiment was conducted over the 2023 growing season at the Laimburg Research Centre in South Tyrol, Italy. All data and the accompanying code for its analysis is freely available in the associated GitHub repository [2]. Spectral data were collected using the Spectral Evolution SR-3500 field spectroradiometer with an attached leaf clip, producing high-resolution hyperspectral reflectance profiles (350-2500 nm) from the adaxial surface of fully expanded leaves. A total of 1189 leaf spectra were recorded and were matched to chemically analysed leaf samples. Corresponding foliar N and P concentrations (and others) were determined through laboratory analysis using the Dumas combustion method for nitrogen and ICP-OES following acid digestion for phosphorus. The dataset includes metadata detailing tree treatments, sampling dates, infection status, and shoot growth metrics. Additionally, R scripts used for data processing, spectral pre-treatment (including multiplicative scatter correction and Savitzky-Golay derivatives), feature selection (VIP and mRMR), and model development are provided. The dataset is suitable for reuse in the development and benchmarking of spectral models for nutrient estimation, especially in the context of field-based or remote sensing applications in horticulture. Its wide range of foliar nutrient values, inclusion of multiple physiological stresses, and detailed documentation make it a valuable resource for researchers working in precision agriculture, plant phenotyping, chemometrics, and hyperspectral data analysis.
Why it matches plant phenotyping methodsリンゴ葉のN・P濃度という植物生理形質を対象に、ハイパースペクトル反射データ、化学分析値、前処理・モデル開発コードを含む再利用可能なデータセットであり、植物フェノタイピング手法の開発・ベンチマークに直接資する。
abstractThis dataset was generated to support research investigating the use of hyperspectral reflectance for the estimation of foliar nitrogen (N) and phosphorus (P) concentrations in apple
Reproduction assets foundThe authors publicly release the paper's own hyperspectral leaf spectra (.sed files), matched foliar N/P concentrations, metadata, and R analysis scripts via a GitHub repository (also archived with Zenodo DOI 10.5281/zenodo.15600557), with explicit public availability and no registration required.Dataset · publicData accessibility
Repository name: Github
Data identification number: DOI 10.5281/zenodo.15600557
Direct URL to data: https://github.com/HyperspectralCameron/Investigating-the-Limits-of-Spectroscopy-for-the-Estimation-of-Foliar-N-and-P-in-Apple.gitInstructions for accessing these data: All data and code are publicly available through the GitHub repository listed above. The repository includes raw spectral files (.sed), metadata files, and R scripts for pre-processing, modelling, and visualisation. No registration or authentication is required.Open asset ↗GitHub · DOI 10.5281/zenodo.15600557html-lines:84-123Code · publicAll data and the accompanying code for its analysis is freely available in the associated GitHub repository [2].Open asset ↗GitHubhtml-lines:1-83Code / 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput process. This study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits. The research methodology involved collecting spectral data from 2,236 cassava clones from 19 field trials in Brazil, using two sample types: fresh roots and mashed roots. Six spectral pre-processing methods and three machine learning algorithms-Partial Least Squares (PLS), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB)-were evaluated to optimize predictive models. Model performance was assessed using the coefficient of determination in calibration ([Formula: see text]), the root mean squared error of calibration ([Formula: see text]), and the Kappa index to quantify the consistency of clone selection. Results show that mashed samples consistently yielded superior predictive performance across all models. Specific preprocessing methods, such as Savitzky-Golay filtering combined with Standard Normal Variate (SG + SNV) and first-derivative transformations, significantly enhanced model accuracy. Among the algorithms, PLS demonstrated the best overall performance, with high predictive accuracy ([Formula: see text] >0.96) and low prediction errors ([Formula: see text]<1.3 for DMCo), especially with mashed samples. High Kappa index values, consistently approaching 1.0, confirmed a good alignment between NIRS-based selection and traditional phenotypic methods. This study validates a portable spectrometer as a reliable and efficient tool for high-throughput phenotyping in cassava breeding programs. The findings confirm that portable NIRS devices, when used with optimal sample preparation (mashed roots) and robust modeling (PLS), can effectively yield good predictions for plant selection. This approach can significantly accelerate breeding cycles by enabling rapid, early-stage selection decisions, thereby overcoming a major bottleneck and contributing to a more efficient and sustainable genetic improvement of cassava.
Why it matches plant phenotyping methods携帯型NIRSによるキャッサバ根の品質形質予測モデルを開発・比較・検証し、育種選抜への適用性能を評価しており、フェノタイピング手法が中心である。
abstractThis study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits.
Reproduction assets foundThe paper's spectral and phenotypic data (NIRS spectra from 2,236 cassava clones, DMC/StC trait measurements) are openly deposited on Figshare per the Data Availability statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability: The data that support the findings of this study are openly available in Figshare at https://figshare.com/s/d2e947f467bd8f655ede .Open asset ↗Figsharelines:142-152Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
With the continuous progress in micro-optical machine technology, miniature spectral imaging devices have been rapidly developed; however, three-dimensional (3D) imaging measurement technology has become increasingly mature and widely used. The evolution of these technologies has established a robust foundation for the integration of three-dimensional imaging and spectral information. To achieve accurate alignment between 3D data and spectral information to obtain a more comprehensive spectral representation of objects in 3D space, we developed a binocular multispectral stereo imaging (BMSI) system. This system acquires images in synchrony with a binocular multispectral imager, thereby ensuring accurate alignment between 3D data and spectral data at the pixel level and facilitating the construction of a four-dimensional (4D) dataset. The segmentation of leaf regions from shadow backgrounds in two distinct plant species was achieved through optimal band fusion and hue-saturation value (HSV) color space transformation, significantly improving the segmentation accuracy, processing efficiency, and robustness across different plant species. A systematic evaluation was conducted to quantify the reconstruction precision and system stability at different measurement distances. The designed system acquired 4D image spectral data with plants as the objects to be tested. The distribution characteristics of chlorophyll (Chl) on the 3D surface of plants were obtained by first-order derivatives of the spectral data and the normalized difference red edge (NDRE) index. This technique provides a new means for plant phenotyping research and a more effective technical approach for the digitalization and precision monitoring of the agricultural industry.
Why it matches plant phenotyping methods植物の3D・マルチスペクトル画像取得、葉領域分割、再構成精度・安定性評価を中核とする新規フェノタイピングシステムの開発研究である。
abstractwe developed a binocular multispectral stereo imaging (BMSI) system.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw data and essential source code for the BMSI plant phenotyping analysis on a public GitHub repository.Code · publicThe raw data and the essential parts of the source code have been uploaded to Github: https://github.com/wwxsoul1234/BMSI/tree/master.Open asset ↗wwxsoul1234/BMSI · BMSIhtml-lines:278-306Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco ( Nicotiana tabacum ), a well-characterized model plant system for studies of plant health and productivity. Leaves were harvested from plants cultivated under two distinct conditions: low light (LL), representing unhealthy leaves with reduced photosynthesis and high light (HL), representing healthy leaves with highly active photosynthesis. Higher-order correlation data were collected and analyzed using machine learning (ML) techniques, specifically a Convolutional Neural Network (CNN), to classify the photon emitter states. This CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL, demonstrating significantly different quantum profiles between the two conditions. These quantum fingerprints serve as a foundation for a novel unified analysis of plant growth parameters associated with different photosynthetic states. By employing CNN, the emitter profiles were able to reproducibly classify the leaves as healthy or unhealthy. This model achieved high probability values for each classification, confirming its accuracy and reliability. The findings of this study pave the way for broader applications, including the application of advanced quantum and machine learning technologies in plant health monitoring systems.
Why it matches plant phenotyping methods量子発光体による葉の光子プロファイル取得とCNN解析を組み合わせ、光合成状態および植物の健康状態を分類する手法が研究の中心である。
abstractThis CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL
Reproduction assets foundThe article's Data availability statement explicitly deposits the paper's time-tagged photon correlation data (the raw measurements underlying the quantum fingerprinting and CNN analysis) in the Dryad Digital Repository, a public, paper-specific dataset.Dataset · publicay: conceptualization, funding acqui-
sition, supervision, project administration, visualization,
writing – original draft, writing – review & editing.
Conflicts of interest
There are no conflicts to declare.
Data availability
Data for this article, including time tangled photon data, are
available at Dryad Digital Repository at https://doi.org/10.5061/dryad.1rn8pk15f.Supplementary information is available. See DOI: https://
doi.org/10.1039/d5an00326a.
Acknowledgements
This research was funded in part by the Faculty Industry
Applied Research (FIAR) program, by the University at
Buffalo’s Center of Excellence in Materials Informatics.
References
1 E. Murchie and T. Lawson, J. Exp. Bot., 2013, 6Open asset ↗Dryad Digital Repository · 10.5061/dryad.1rn8pk15fpdf-raw-page:7 lines:1-96Code / 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 14 Sept 2026
Field / plotChlorophyll fluorescenceLeafPhysiological trait estimationGrowth / development / phenologyPhotosynthesis / fluorescencePigment / colour / senescence
Abstract Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll concentrations, it is destructive and temporally limited. In contrast, portable optical meters such as the CCM-300 enable rapid, non-destructive measurements of chlorophyll fluorescence ratio (CFR), but their calibration against extracted pigments is often species- and season-specific. This study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. Random Forest regression achieved the best predictive accuracy (R² = 0.51, RMSE = 0.51 mg cm⁻²), although a simple linear model was adopted for cross-year projection due to its stability. Applying this calibration to daily 2022 CFR measurements generated a “virtual acetone” chlorophyll time series, allowing comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines, but senescence occurred approximately ten days earlier in the warmer, drier 2022 season.Mixed-effects modelling of the 2022 data indicated positive effects of temperature (β = 0.0029 ± 0.0012 SE) and wind speed (β = 0.0053 ± 0.0021 SE) on CFR, whereas day of year and precipitation were not significant. A generalised additive model for 2023 explained 90% of deviance (adj. R² = 0.89) and revealed significant nonlinear effects of temperature, rainfall, and wind speed. Together, these results demonstrate that the CCM-300 can provide a robust non-destructive proxy for total chlorophyll when properly calibrated, and that Acer campestre chlorophyll dynamics are highly sensitive to interannual climatic variability.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を抽出クロロフィルと比較・較正し、季節時系列へ適用して信頼性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurately modeling the nonlinear relationships between near-infrared (NIR) spectral signatures and biochemical traits in corn remains a major challenge. A key difficulty lies in capturing multi-scale contextual dependencies-ranging from local absorption peaks to global spectral patterns-that jointly determine quality constituents such as protein and oil. To address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression. SpecTran integrates three key components: adaptive multi-scale patch embedding which extracts spectral features at multiple resolutions to capture both fine and coarse patterns, spectral-enhanced positional encoding which preserves wavelength order information more effectively than standard encoding, and hierarchical feature fusion for robust multi-task prediction. Evaluated on the public Eigenvector corn dataset, SpecTran had a performance across four key traits-moisture, starch, oil, and protein-with an average R2 of 0.483. It reduced the RMSE by 11.2% for protein and 10.7% for oil compared to the best-performing baseline, which is the standard Transformer model. These results demonstrate SpecTran's superior ability to model complex spectral dynamics while providing interpretable insights, offering a reliable framework for NIR-based agricultural quality assessment.
Why it matches plant phenotyping methodsトウモロコシのNIRスペクトルから水分・デンプン・油・タンパク質という種子品質形質を推定するTransformer手法を開発し、公開データセット上でベースライン比較検証しており、形質取得・推定法が研究の中心である。
abstractTo address this, we propose SpecTran, a spectral Transformer network specifically designed for NIR regression.
Reproduction assets foundThe paper uses the public Eigenvector corn NIR dataset and provides an authors' GitHub repository containing the spectral data, reference trait values, preprocessing scripts, and model implementation code.Dataset · publicnto wavelength-specific contributions for each quality constituent, thereby bridging data-driven prediction with domain knowledge.
2. Materials and Methods
2.1. Materials
Datasets
The corn near-infrared (NIR) spectral dataset used in this study was obtained from the publicly available repository hosted by Eigenvector Research ( http://www.eigenvector.com/data/Corn , accessed on 31 October 2025). This benchmark dataset has been widely adopted in chemometric studies for evaluating multivariate calibration models in agricultural spectroscopy. It comprises NIR absorbance spectra of 80 corn samples, measured in the wavelength range of 1100–2498 nm at 2 nm intervals, resulting in 700 discreteOpen asset ↗lines:27-35Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Canola is an important economic and agronomic crop globally, but its yield is under threat due to climate change. Stomata are a key breeding target because of their importance in carbon capture and water use efficiency. However, screening for elite stomatal traits could be laborious and time-consuming. We developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN that combines the use of high-resolution portable digital microscopy with machine learning to automate stomatal trait phenotyping in canola. We show that SCAN can rapidly measure stomatal density, size, and pore area in canola at 97-99% accuracy, and capture real-time stomatal pore status that strongly correlated with leaf porometer measurement in canola. Here we use SCAN to investigate how leaf stomatal traits vary through a canopy in different ecotypes of canola grown in the field and glasshouse conditions. SCAN revealed that stomatal density in canola decreases in more expanded leaves with the abaxial surface having up to 40% more stomata that are 2× more open than the adaxial surface. SCAN also showed that patterns of stomatal traits in canola vary between leaf position in the canopy and change with environment in an ecotype-dependent manner.
Why it matches plant phenotyping methods葉の気孔形質を自動取得する画像・機械学習ツールを開発し、精度検証と既存測定との相関評価を行っており、植物表現型取得法が研究の中心である。
abstractWe developed a new toolkit called Stomatal Comprehensive Automated Neural Network or SCAN that combines the use of high-resolution portable digital microscopy with machine learning to automate stomatal trait phenotyping in canola.
Reproduction assets foundThe paper publicly releases its authors' analysis code, trained model weights, and training image datasets for the SCAN stomatal phenotyping pipeline via two GitHub repositories and two Roboflow datasets, with explicit availability statements in the Data availability section. Raw phenotype measurements are in a journalCode · publicThe full details of the weights, hyperparameters, training scripts, and datasets of the models can be found at https://github.com/William-Yao0993/FD_detection .Open asset ↗William-Yao0993/FD_detectionlines:45-53Dataset · publicThe microscopy images used to train the SCAN model are available as two public datasets: https://app.roboflow.com/danila-lab/fd-project-1 and https://app.roboflow.com/danila-lab/pore-segmentation/ .Open asset ↗danila-lab/fd-project-1lines:233-272Dataset · publicThe microscopy images used to train the SCAN model are available as two public datasets: https://app.roboflow.com/danila-lab/fd-project-1 and https://app.roboflow.com/danila-lab/pore-segmentation/ .Open asset ↗danila-lab/pore-segmentationlines:233-272Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
Accurate estimation of chilling requirements (CR) is essential for breeding and selecting temperate fruit trees adapted to specific agroclimatic conditions, particularly under global warming scenarios. Among the available methodologies to determine CR, the Partial Least Squares (PLS) regression procedure, based on long-term phenological and temperature records, offers a suitable approach to delineate the effective chill accumulation period. In this study, we present an R-based workflow developed using the agroclimatic functions integrated into the chillR package for R to determine the genotype-specific CR of 282 apricot ( Prunus armeniaca L.) seedlings from two progenies grown in southwestern Spain. The pipeline generates standardized CR datasets suitable for downstream applications, including QTL mapping and the selection of promising genotypes for breeding purposes. This tool streamlines the estimation process, reducing the technical expertise and time required for CR estimation, thereby supporting efficient phenotypic selection and accelerating genetic research in temperate fruit trees. The complete code and associated datasets are freely available in a public repository (https://github.com/CEBASFruitBreed/R-workflow-ChillPLS), promoting the use across a range of temperate fruit species.•Uses long-term flowering observations and temperature records to determine genotype-specific chilling requirements.•Integrates PLS regression procedure within an R-based workflow to estimate chilling requirements from datasets comprising multiple genotypes.•Generates standardized outputs suitable to support genetic analysis and informed breeding decisions.
Why it matches plant phenotyping methods複数のアンズ遺伝子型の低温要求量という植物生理形質を、PLS回帰とRワークフローで推定する方法自体が中心であり、再利用可能なコードとデータも提供している。
abstractwe present an R-based workflow developed using the agroclimatic functions integrated into the chillR package for R to determine the genotype-specific CR of 282 apricot ( Prunus armeniaca L.) seedlings
Reproduction assets foundThe authors explicitly state that the complete R code (workflow for PLS-based chilling requirement estimation) and the associated datasets (flowering records and temperature data) are freely available in a public GitHub repository.Code · publicThe complete code and associated datasets are freely available in a public repository ( https://github.com/CEBASFruitBreed/R-workflow-ChillPLS )Open asset ↗CEBASFruitBreed/R-workflow-ChillPLSlines:1-47Code / 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.
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Data availability
The dataset is available at: https://doi.org/10.5281/zenodo.14677494 (Baartman et al., 2025).
Author contributions
Conceptualization: SLB, MCK, MEP, LW. Data curation: SLB. Formal analysis: SLB, NUL. Funding acquisition: MCK. Investigation: SLB, SMD, MW, LMJK, LM, AC, SH. Methodology: SLB, SMD, MW, LMJK, MEP. Resources: SMD, MW, LM, SH. Supervision: MEP, TR, MCK. Visualization: SLB, NUL. WritingOpen asset ↗Zenodo · 10.5281/zenodo.14677494lines:652-942Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Rapid prediction and control of flowering time is essential for breeding crops resilient to changing climates. Current models often fail to predict flowering time in new cultivars because molecular models lack integration of environmental signals, while physiological models inadequately capture the interactions of vernalization, photoperiod and temperature. This leads to mischaracterized genotypes and inaccurate forecasts. A new Cereal Anthesis Molecular Phenology (CAMP) model was developed for wheat. It explicitly integrates the regulatory roles of three major 'virtual' flowering genes (Vrn1, Vrn2, and Vrn3) with environmental cues. A novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration. CAMP predicted flowering time within 4-7 d across 64 genetically diverse wheat cultivars grown under contrasting environments. The leaf-number phenotyping method reduced phenotyping time by more than 80%, offering a practical alternative to resource-intensive field trials. Together, these advances enable accurate cultivar characterization and scalable prediction of flowering behaviour. CAMP enables the ability to predict flowering time directly from genotypic data (e.g. SNPs), eliminating the need for costly controlled-environment experiments. This represents a step change in molecular-physiological modelling, supporting faster deployment of new cultivars and more effective design of wheat for future climates.
Why it matches plant phenotyping methods主茎葉数に基づく新規フェノタイピング手法とCAMPモデルを開発し、多様なコムギ品種・環境で開花期予測を検証している。表現型取得の効率化が中心的貢献である。
abstractA novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration.
Reproduction assets foundThe paper's Data availability statement explicitly provides public repositories containing the CAMP model source code and analysis scripts used for the phenotyping data analysis and flowering-time prediction: the APSIM Next Generation framework repository, the standalone Python CAMP model and analysis scripts, and theCCode · publicAll the data and the source code of the model are freely accessible for research use through the APSIM General Use License at: https://github.com/apsimInitiative/apsimxOpen asset ↗apsimInitiative/apsimxlines:295-475Code · publicPython code and analysis scripts can be found at https://github.com/HamishBrownPFR/CAMPOpen asset ↗HamishBrownPFR/CAMPlines:295-475Code · publicC# implementation is available at https://github.com/APSIMInitiative/ApsimX/tree/master/Models/PMF/Phenology/CAMPOpen asset ↗APSIMInitiative/ApsimXlines:295-475Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using R2, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to R2=0.86 for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability.
Why it matches plant phenotyping methods植物のクロロフィル含量・活力・病害ストレスを画像／ハイパースペクトルから推定する新規深層学習フレームワークを開発・評価しており、植物表現型の取得・推定が中心である。
abstractthis study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' Agri-DSSA implementation (the computational analysis code for the phenotyping experiments) in a public GitHub repository with a commit hash. The seven benchmark datasets are cited third-party resources rather than paper-specific deposits, so only,Code · publicThe implementation is openly available at the GitHub repository https://github.com/
Fatema-Abdulqader/Agri-DSSA-Dual-Self-Supervised-Attention-Framework/tree/main, commit
98f3863Open asset ↗pdf-page:21 lines:1-61Code / 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. PanomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物の画像ベース表現型を含むマルチオミクス統合と機械学習解析を自動化するツールを開発・適用しており、表現型解析ワークフローが中心的です。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease.
Reproduction assets foundThe paper's tomato heat-stress phenotyping/FTIR data and pre-processed analysis inputs are publicly deposited at IPK e!DAL, and the panomiX analysis code is on GitHub with a Zenodo archive; the rnaseq-mapper pipeline is also public. ENA RNA-seq deposit is molecular omics and excluded.Dataset · publicPhenotyping and FTIR data as well as pre-processed inputs for reproducing the results of this article with panomiX are available at https://doi.org/10.5447/ipk/2025/3 .Open asset ↗10.5447/ipk/2025/3lines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗GitHub · NAMlab/panomiX-toollines:156-172Code · publicThe code for panomiX is freely available at https://github.com/NAMlab/panomiX-tool under the terms of the MIT license (also archived at Zenodo at time of publication: https://doi.org/10.5281/zenodo.15193421 ).Open asset ↗Zenodo · 10.5281/zenodo.15193421lines:156-172Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Code / 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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Raw data and source code are available in the following Github repository.
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https://github.com/rishavray/spectral-network
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References
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Albert R, Barabási A-L. 2002. Statistical mechanics of complex networks. Reviews of Modern
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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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Introduction Monitoring the growth status and aboveground biomass of wild and cultivated medicinal herbs remains a persistent challenge in precision agriculture. Methods In this study, we developed machine learning and deep learning models to estimate SPAD values and biomass of Lamiophlomis rotata (Benth.). The models used hyperspectral data and time-series phenotypic traits from 508 samples collected across different altitudes. Regions of interest (ROIs) were manually defined from plant contours. The corresponding mean spectral profiles were then preprocessed. To improve feature selection, we proposed a Dynamic Reptile Search Algorithm-enhanced CARS (DRSA-CARS) method. This method integrates a dynamic behavioral strategy into the CARS framework to identify informative spectral bands. Vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM)-based texture parameters were extracted and combined with spectral features to construct the PLSR, SVR, FNN, and CNN models. Results Compared to CARS, the DRSA-CARS method reduced feature dimensionality by up to 75.7% for SPAD and 29.2% for biomass, while improving prediction accuracy ( R ²) by 24.4% and 34.7%, respectively. Among all models, the FNN achieved the highest performance, with R ² values of 0.7732 (training) and 0.7502 (testing) for SPAD and 0.8260 and 0.7933 for biomass. Feature fusion further improved predictive accuracy by 11% for SPAD and 30% for biomass compared to models based on individual feature types. Discussion These results demonstrate that coupling DRSA-CARS-based feature selection with deep learning provides a robust, non-destructive approach for evaluating plant growth status. This framework highlights the potential of hyperspectral imaging as a rapid, reliable, non-invasive tool for precision cultivation of medicinal herbs.
Why it matches plant phenotyping methodsハイパースペクトル画像からSPAD値とバイオマスという植物形質を非破壊推定し、特徴選択法と深層学習モデルを開発・評価しており、フェノタイピング手法が中心である。
abstractwe developed machine learning and deep learning models to estimate SPAD values and biomass of Lamiophlomis rotata (Benth.).
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
Hyperparameters and settings of PLSR, SVR, FNN, and CNN models for above-ground biomass prediction.Open asset ↗lines:760-839Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Plant pigment content is a crucial indicator for assessing photosynthetic efficiency, nutritional status, and physiological health. Its spatial distribution is significantly influenced by variety, location, and environmental factors. However, existing methods for measuring pigment content are often destructive, inefficient, and costly, making them unsuitable for the demands of modern precision agriculture. This study proposes a cross-scale, non-destructive detection method for lettuce pigments by integrating hyperspectral imaging (HSI) technology with deep learning algorithms, addressing the limitations of existing techniques in high-throughput and spatial resolution analysis. In this study, we built a multidimensional dataset based on eight different types of lettuce and developed a deep learning model named LPCNet to predict the contents of chlorophyll a (Chl a), chlorophyll b (Chl b), carotenoids (Car), and total pigment content (TPC) in lettuce. The LPCNet model integrates convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and multi-head self-attention (MHSA) mechanisms, enabling automatic extraction of pigment-related key features and simplifying the complex preprocessing and feature selection procedures required in traditional machine learning. Compared to multivariate analysis methods in machine learning, LPCNet demonstrated superior predictive accuracy, with coefficients of determination ( RP2 ) of 0.9449, 0.8613, 0.9121, and 0.8476 for Chl a, Chl b, Car, and TPC, respectively. Additionally, by combining the hyperspectral reflectance of lettuce canopies with the leaf-level inversion model, we visualized the spatial distribution of pigment content on the canopy of lettuce, achieving cross-scale analysis from leaf to canopy. This study provides an innovative approach for the rapid and accurate assessment of lettuce pigment content and offers an effective visualization tool for revealing the physiological processes and growth development of lettuce.
Why it matches plant phenotyping methodsハイパースペクトル画像と深層学習により、レタスの色素含量を非破壊推定・可視化する植物フェノタイピング手法を開発しており、方法が研究の中心である。
abstractThis study proposes a cross-scale, non-destructive detection method for lettuce pigments by integrating hyperspectral imaging (HSI) technology with deep learning algorithms
Reproduction assets foundThe article's data availability statement points to a public GitHub repository containing the authors' spectral analysis code for the LPCNet pigment-inversion workflow. No public phenotype dataset or hyperspectral image deposit is stated; supplementary data is only a small docx.Code · publicFurther details of the code are available at: https://github.com/zhaoyyy620/spectral_analysis.Open asset ↗zhaoyyy620/spectral_analysishtml-lines:448-472Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafPhysiological trait estimationGrowth / development / phenologyLeaf traits
Summary Disentangling genotype × environment (G×E) effects is critical to understand the performance of wheat across different environments. A framework for doing this was previously presented in a model that integrated knowledge of crop physiology and the Vrn gene feedback loop to explain and predict the time of anthesis. The aims of this study were: 1) provide an updated description of the Cereal Anthesis Molecular Phenology (CAMP) model; 2) to verify the model’s assumptions regarding the relationship between Vrn gene expression and the timing of phenological stages in a set of diverse genotypes and environments; 3) to use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation. Six wheat genotypes with a range of cool temperature and photoperiod sensitivities were evaluated. Apical development, final leaf number (FLN) and temporal expression of Vrn1, Vrn2 and Vrn3 were compared with model predictions. There was a clear relationship between FLN responses to cool temperature and photoperiod, the timing of phenological events and the patterns of Vrn gene expression for all genotypes. There was general agreement between the temporal patterns of foliar gene expression observed with those assumed by CAMP, but some obvious discrepancies. These may be related to differences between gene expression in foliar (observed) and apical (assumed by the model) parts of the plant, or differences in the way observed and modelled gene expression are scaled. Overall, the model described all the observed development responses to environment and provides a basis for building quantitative predictions of field-based development from genotypic and environmental data. A protocol is presented for phenotyping wheat using FLN measured in specific combinations of temperature and photoperiod. It allows easy and unconfounded measure of key developmental phenotypes that clearly relate to the genetic make-up of the plants and underlying gene expression profiles.
Why it matches plant phenotyping methodsCAMPモデルの更新・検証と、FLNを用いた小麦発育形質のフェノタイピングプロトコル提示が研究の中心であり、単なる生物学的測定ではない。
abstractto use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation.
Reproduction assets foundThe paper's CAMP model code, analysis scripts, and data are explicitly stated as publicly available on the authors' GitHub repository, with specific URLs for the model notebook and the test/plotting script.Code · publicwere also validated and the best-performing sets selected. A
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description of each of the primers used in this study is given in the supplementary material
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(Table SA1).
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2.9 Verification of CAMP predictions
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2.9.1 Model set-up and operation.
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The CAMP model was coded into a Python script which is available at
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https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
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description of the code and parameterisation scheme is given in the supplementary material.
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The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
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derive the Vrn expression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicpression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
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Vrn gene expression could be compared with those observed. The script running the CAMP
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code and producing the graphs displayed in this paper can be viewed at
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https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.360
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 12, 2025.
;
https://doiOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicnd testing of the model in
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broader contexts. EW contributed substantially to the improvement of model concepts and the
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manuscript and all authors provided final checking.
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8. Data Availability
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All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
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publicly available at https://github.com/HamishBrownPFR/CAMP/694
9. References
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Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
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response of wheat vernalization to environmental variables indicates that vernalization is not
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a response to cold temperature. Journal of Experimental Botany 63: 847–857.
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Baumont M, Parent B, Manceau L, Brown HE, DOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:31 lines:1-68Code / 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 14 Sept 2026
Summary Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (gsw) from leaf temperature. The conductance index (Ig) was previously proposed to estimate gsw from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, Ig is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility. We developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw. This was achieved by capturing temperature variations in a set of ALs with a range of known constant pore conductances. This approach provided the Ig–conductance relationship, using ALs as a reference, to infer gsw of real leaves from their measured Ig. In fluctuating environments, gsw estimated using DynG showed greater accuracy and stability than gsw calculated from Ig alone, and was in good agreement with gsw determined using lysimetric and gas exchange methods. DynG's power was further showcased in distinguishing gsw of Arabidopsis genotypes differing in stomatal traits (Col‐0, epf1epf2, and EPF2OE). We conclude that Ig corrected with DynG can reliably estimate gsw in fluctuating environments without complex modeling, opening new avenues for gsw phenotyping and monitoring.
Why it matches plant phenotyping methods熱画像から気孔コンダクタンスを推定する補正係数を開発し、変動環境下で既存法と比較検証した、植物フェノタイピング手法の中心的研究である。
abstractWe developed a simple and novel correction factor (named DynG) for Ig that accounts for environmental fluctuations when scaling Ig to gsw.
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 · publicRelated codes are available on GitHub ( https://github.com/jiayu0903/dynamic‐conductance‐index.git ).Open asset ↗https://github.com/jiayu0903/dynamic‐conductance‐index.gitlines:805-819Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Chlorophyll breakdown is a central process during plant senescence or stress responses, and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure of the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and requires no specialized equipment, apart from a digital camera. Here, we developed the ImageJ plugin Green Leaf Visual Index that facilitates high-throughput image analysis for quantifying leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate Genome-Wide Association Study results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.
Why it matches plant phenotyping methods葉のクロロフィル含量を画像から推定するFIJIプラグインを開発し、複数植物種で信頼性と従来法との一致を検証しており、植物表現型取得法が中心である。
abstractHere, we developed the ImageJ plugin Green Leaf Visual Index that facilitates high-throughput image analysis for quantifying leaf chlorophyll content.
Reproduction assets foundThe paper's authors publicly released the GreenLeafVI FIJI plugin source code and documentation on GitHub, which is the computational tool used for the paper's image-based chlorophyll phenotyping. The underlying phenotype/trait datasets (RGB image measurements and chlorophyll extraction values) are not publicly posted;Code · publicThe data that support the findings of this study are available from the corresponding author upon reasonable request. The GreenLeafVI source code, documentation, and further information are available at https://github.com/jelmervanlieshout/GreenLeafVI .Open asset ↗jelmervanlieshout/GreenLeafVIlines:202-249Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The vigor of potato plants is of crucial importance for potato seed producers, who are interested in predicting it at scale by exploiting the dependence of plant growth and development on the origin and physiological state of the seed tuber. In this article we present the results of a three-year long experiment in which we studied six potato varieties in three test fields. We identify a 73-[Formula: see text] overall correlation in the vigor of plants from the same seedlot grown in different test fields. Similarly, the biochemical tuber data produce plant vigor predictions that correlate up to 70-[Formula: see text] with the measurements. However, these relatively large data and prediction correlations are mostly due to the strong dependence of the seedlot vigor on the tuber genotype. For five out of six studied varieties, variety-specific cross-field and cross-year vigor predictions produce negligible or even negative correlations when the seed tubers and young plants experience environmental stress. At the same time, for the variety that appeared to be less sensitive to environmental stresses, we obtained cross-field and cross-year vigor predictions correlating up to [Formula: see text] with the measurements. Analysis of individual predictor variables, such as the abundance of a particular metabolite, indicates that the vigor-enhancing properties of the seed tubers are also variety-specific and that the FTIR spectroscopy data is the most reliable predictor.
Why it matches plant phenotyping methodsFTIR・生化学データからジャガイモ植物の vigor を予測し、圃場間・年次間で予測性能を検証しており、植物形質の取得・推定法が中心です。
abstractinterested in predicting it at scale
Reproduction assets foundThe paper's Data Availability statement points to a public 4TU.ResearchData deposit containing the seed tuber and plant canopy (drone-derived vigor) datasets plus the Python code needed to reproduce the regression results — a paper-specific, publicly actionable asset.Dataset · publicBoth the seed tuber and plant canopy datasets are available at http://doi.org/10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55e . The data also includes the Python code necessary to reproduce the results of regression.Open asset ↗10.4121/3a97fa0c-8c7d-451a-b8fe-d521f1cec55elines:267-336Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Background and aims Rapid, large-scale monitoring is critical to understanding spatiotemporal plant stress dynamics, but current physiological stress markers are costly, destructive and time-consuming. This study aimed to evaluate the potential of machine learning to non-destructively predict leaf betalains - yellow to reddish pigments unique to Caryophyllales species - for the first time, and to explore intra-individual variation in betalains in a clonal species and its role in responding to stressful periods. Methods We characterized the betalainic profile of an invasive clonal plant for the first time, Carpobrotus edulis (the cape fig), via high-performance liquid chromatography. We measured multiple stress markers over a year, including betalain content using our optimized method, where the species is spreading. Additionally, 3735 digital images at the leaf level were taken. Machine learning regression algorithms were trained to predict betalain accumulation from digital images, outperforming classic spectroradiometer measurements. Key results Betalain content increased sharply in non-reproductive ramets during extreme abiotic conditions in summer and during senescence in reproductive ramets. The stress markers revealed a strong intra-individual functional mosaic, underscoring the importance of spatiotemporal dimensions in stress tolerance. Conclusions We developed a scalable, non-destructive tool for betalain research that integrates digital imaging with machine learning. This approach opens new possibilities for understanding spatiotemporal stress responses, particularly in clonal plant systems, using artificial intelligence.
Why it matches plant phenotyping methods葉のデジタル画像と機械学習によりベタレイン蓄積を非破壊推定する手法を開発しており、植物ストレス状態の表現型取得が研究の中心である。
abstractThis study aimed to evaluate the potential of machine learning to non-destructively predict leaf betalains
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete set of analysis scripts, collected leaf images, image features (predictors), and response variables (betalain/pigment measurements) in FigShare under DOI 10.6084/m9.figshare.28706588. This is a paper-specific, public phenotyping asset (images + MLDataset · publicThe complete set of scripts, collected images, image features
(predictors) and response variables are publicly available in
FigShare: 10.6084/m9.figshare.28706588.FigShare · 10.6084/m9.figshare.28706588pdf-raw-page:12 lines:1-81Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Chlorophyll breakdown is a central process during plant senescence or stress responses and leaf chlorophyll content is therefore a strong predictor of plant health. Chlorophyll quantification can be done in several ways, most of which are time-consuming or require specialized equipment. A simple alternative to these methods is the use of image-based chlorophyll estimation, which uses the color values in RGB images to calculate colorimetric visual indexes as a measure for the leaf chlorophyll content. Image-based chlorophyll measurement is non-destructive and, apart from a digital camera, requires no specialized equipment. Here, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content. Our plugin offers the option to white-balance images to decrease variation between images and has an optional background removal step. We show that this method can reliably quantify leaf chlorophyll content in a variety of plant species. In addition, we show that image-based chlorophyll quantification can replicate GWAS results based on traditional chlorophyll extraction methods, showing that this method is highly accurate.
Why it matches plant phenotyping methods葉のクロロフィル量を画像から推定するFIJIプラグインを開発し、複数植物種で信頼性とGWAS再現性を検証しており、植物フェノタイピング手法が中心である。
abstractHere, we developed the ImageJ plugin GreenLeafVI that facilitates high-throughput image analysis for measuring leaf chlorophyll content.
Reproduction assets foundThe paper's GreenLeafVI FIJI plugin (the authors' phenotyping analysis code) is publicly available on GitHub with explicit availability language. The underlying phenotype/trait datasets are only available upon request, so they do not qualify as public assets.Code · publicank BSc/MSc students Marion Larue, Karin Verkerk and Kim Roos for their help in phenotyping.
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30 Data availability
31 The data that support the findings of this study are available from the corresponding author upon reasonable
32 request. The GreenLeafVI source code, documentation and further information is available at
33 https://github.com/jelmervanlieshout/GreenLeafVI.
9Open asset ↗jelmervanlieshout/GreenLeafVIpdf-layout-page:9 lines:1-45Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Large-scale kinetic models of photosynthesis enable time-resolved predictions of traits related to this key process, and provide the means to identify factors limiting photosynthesis. However, their use is currently limited by the lack of efficient approaches to estimate the hundreds of genotype-specific kinetic parameters. Here, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves. C4TUNE was trained on a biologically-relevant synthetic dataset comprising matched samples of parameters and response curves obtained using a C 4 photosynthesis kinetic model. To speed up the training of C4TUNE, we devised a surrogate neural network to predict photosynthesis response curves directly from the model parameters and environmental inputs. Given response curves as input, we showed that over 99% of the parameter vectors predicted by C4TUNE could be used directly in simulation of the kinetic model and resulted in excellent fits. Finally, we applied C4TUNE to predict parameters for a population of 68 maize genotypes across two seasons. The predicted genotype-specific parameters allowed pinpointing factors that limit photosynthetic efficiency, validated using simulations. Therefore, the use of C4TUNE presents a fast and precise approach for parameter prediction based on minimal datasets.
Why it matches plant phenotyping methodsC4TUNEは光合成応答曲線から遺伝子型別の光合成パラメータを推定するニューラルネットワーク手法であり、植物生理形質の抽出が研究の中心です。
abstractHere, we present C4TUNE, an artificial neural network, which can efficiently predict parameters of a large-scale photosynthesis model from photosynthesis response curves.
Reproduction assets foundThe paper deposits its maize gas exchange phenotype measurements (Zenodo 15966533), the synthetic neural-network training dataset (Zenodo 15926601), and the C4TUNE analysis/training code with predicted genotype parameters (GitHub pwendering/C4TUNE), all with explicit availability statements and public URLs.Dataset · publicwere tuned as described above (“Surrogate
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model”). The final model was trained for 30 epochs with a batch size of 8.
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The neural networks were implemented using Python 3.10.14 using the PyTorch library version
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2.5.1 42
.
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Data availability
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The gas exchange measurements for maize genotypes are available at
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https://doi.org/10.5281/zenodo.15966533. Part of these data has been used in another study
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linking photosynthesis-related traits and hyperspectral reflectance data 43
. The generated
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artificial data set for neural network training is available at
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https://doi.org/10.5281/zenodo.15926601.657
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CC-BY-NC-ND 4.0 International license
made available under a
(whOpen asset ↗zenodo · 10.5281/zenodo.15966533pdf-raw-page:21 lines:1-94Code · public22
Code availability
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Custom code for the generation of the artificial dataset as well as code for neural model
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definition and training are available at https://github.com/pwendering/C4TUNE. This
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repository also contains the predicted parameters for the maize genotypes.
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References
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1. Zhu, X. G., Long, S. P. & Ort, D. R. Improving photosynthetic efficiency for greater
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yield. Annu. Rev. Plant Biol. 61, 235–261 (2010).
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2. Croce, R. et al. Perspectives on improving photosynthesis to increase crop yOpen asset ↗github · pwendering/C4TUNEpdf-raw-page:22 lines:1-69Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.
Why it matches plant phenotyping methods葉の光合成形質を予測する計算フレームワークの開発が研究の中心であり、植物生理形質の推定手法として適格。
abstractHere we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C 4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes and data to ensure reproducibility of
the results can be accessed at: https://github.com/Rudan-X/KineticGPOpen asset ↗GitHub · Rudan-X/KineticGPpdf-page:19 lines:1-43Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Common bean ( Phaseolus vulgaris L.) can fix atmospheric nitrogen (N) through symbiosis with Rhizobia species. This trait is often underutilized by growers and overlooked by breeders due to the laborious and costly evaluation techniques involved. There is a critical need for the development of new screening tools to enhance nitrogen fixation efficiency. Remote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation. In this study, we investigated the use of vegetation indices and machine learning (ML) methods in estimating symbiotic nitrogen fixation (SNF). Forty‐two black bean breeding lines from the Dry Bean Breeding Program at Michigan State University were grown and compared under both high and low N conditions. A random forest model developed to predict percent nitrogen derived from the atmosphere (%Ndfa) using remote sensing (RS) data resulted in an average accuracy of R 2 = 0.86. A 3‐year evaluation of these trials in Michigan demonstrated how seed yield under unfertilized conditions could be used as an indirect indicator of SNF ability. Two accurate prediction models for yield were developed using stepwise general linear modeling (StepwiseGLM) and Bayesian regularized artificial neural network (BRNeural Network) (stepwise general linear model r = 0.64; Bayesian regularized neural network r = 0.65). These results suggest that seed yield and RS data coupled with ML offer a promising tool to efficiently implement indirect selection for SNF in common bean.
Why it matches plant phenotyping methodsUASリモートセンシングと機械学習により、共生窒素固定という植物形質を推定するスクリーニング手法を開発・評価しており、表現型取得と予測モデルが研究の中心である。
abstractRemote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation.
Reproduction assets foundThe paper's data availability statement explicitly says the code and methodologies used in this study are available in the authors' public GitHub repository (msudrybeanbreeding). No phenotype dataset, imagery, or model checkpoint deposit is stated in the supplied blocks.Code · publicte helpful conversations and comments from J.D.
Kelly, which improved the quality of our final manuscript.
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
Code and methodologies used in this study are available in the
GitHub repository: https://github.com/msudrybeanbreeding
O RC I D
MasonJackson https://orcid.org/0009-0004-7635-0418
LeonardoVolpato https://orcid.org/0000-0003-1119-0615
EvanM. Wright https://orcid.org/0009-0003-7512-0963
ValerioHoyos-Villegas https://orcid.org/0000-0003-1080-9148
FranciscoE. Gomez https://orcid.org/0000-0002-2862-7118
R E F E R E N C E S
Ahamed, T., Tian, L., ZhangOpen asset ↗msudrybeanbreedingpdf-raw-page:13 lines:1-83Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
GreenhouseThermalLeafPhysiological trait estimationStress response / tolerancePlant / canopy temperature
ABSTRACT Extreme heat can push plants beyond their thermal safety margin ( TSM ) if maximum leaf temperature ( T leaf_max ) exceeds leaf critical temperature ( T crit ). The TSM is potentially useful for assessing heat vulnerability across species but needs further validation, so we exposed 50 tree/shrub species in controlled glasshouses to a 6‐day heatwave (peak air temperature = 41°C). Many species increased their mean T crit during the heatwave (42%), with Δ T crit ranging from +1°C to 4°C, but other species did not acclimate or were impaired by heat stress (58%). Species T leaf_max explained ~55% of the variation in species T crit and was a key correlate of the plasticity of T crit among species. Species with high Δ T crit also had higher Δ T leaf_max , with leaves being 7°‒12°C hotter during the heatwave than under baseline conditions. Both T leaf_max and TSMs were correlated with heatwave damage across diverse species from contrasting climate zones. Species differences in TSMs were stable across measurement temperatures, correctly identified the most vulnerable species, and were strongly associated with T leaf_max . Our results suggest that (1) T leaf_max alone is more informative than T crit for ranking species heat tolerance, and (2) species vulnerability to heatwaves is most reliably assessed by using TSMs that integrate T leaf_max with T crit across species.
Why it matches plant phenotyping methods葉温・熱安全余裕度（TSM）を用いた植物の熱脆弱性評価手法を、多種の植物で検証し、損傷予測性能や種間比較の妥当性を評価しているため、方法的役割が中心である。
abstractThe TSM is potentially useful for assessing heat vulnerability across species but needs further validation
Reproduction assets foundThe article's Data Availability Statement explicitly states the supporting data (phenotype measurements: Tcrit, Tleaf_max, TSM, damage indicators for 50 species) are openly available on Figshare at the authors' public DOI, which is an allowed URL.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.29345549.v1 .Open asset ↗Figshare · 10.6084/m9.figshare.29345549.v1lines:721-817Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.
Why it matches plant phenotyping methods植物プランクトンの沈降速度を高スループット蛍光顕微鏡で測定する手法を導入し、生活状態や集団スケールの生理・機能形質を定量化しているため、測定法が研究の中心です。
abstractWe introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains
Reproduction assets foundThe paper's sinking-velocity analysis code is explicitly stated to be openly available on the authors' GitHub repository. The raw phenotype data is promised for Dryad only upon acceptance, so it is not yet publicly actionable.Code · publicfunctional groups of cyanobacteria, diatoms strains with 156
diameter less than 10µm and diatoms strains with diameter larger than 10µm based on 157
growth light, viability state (living vs. dead and dying) and slow vs. fast sinking 158
velocity clustering groups. 159
The code used to analyse the data is public available at 160
https://github.com/maxberthold/PhytoplanktonSinkVelocities. 161
Sinking according to Stokes’ law 162
Sinking velocities of spherical objects falling under the case of Reynolds numbers 163
smaller than 1 can be described by Stokes’ law. Several studies have used Stokes law or 164
a modified version of Stokes’ law to estimate sinking velocities of plankton and marine 16Open asset ↗maxberthold/PhytoplanktonSinkVelocitiespdf-raw-page:8 lines:1-44Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Fluctuations and propagation of cytosolic calcium levels at both the cellular and tissue levels show complex patterns, referred to as calcium signatures, that regulate growth, organ development, damage responses, and survival. The quantitative analysis of calcium signatures at the cellular level is essential for identifying unique patterns that coordinate biological processes. However, a versatile framework applicable to multiple tissue types, allowing researchers to compare, measure, and validate diverse responses and recognize conserved patterns across model organisms, is missing. Here, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem. This tool has a graphical user interface and does not require software programming experience to perform calcium signal analysis. The open-source software has a modular framework with standardized functionalities that can be tailored for various research approaches. CalciumInsights provides descriptive statistical analysis through various metrics extracted from dynamic calcium transients and oscillations, such as peak amplitude, area under the curve, frequency, among others. The tool was evaluated with fluorescence imaging data from three model organisms: Danio rerio , Arabidopsis thaliana , and Drosophila melanogaster , demonstrating its ability to analyze diverse biological responses and models. Finally, the open-source nature of CalciumInsights enables community-driven improvements and developments for enabling new applications. Author Summary This manuscript introduces CalciumInsights, an open-source tool for calcium signature analysis. Designed to be a versatile tool that works with various tissue types and biological systems, CalciumInsights has an easy-to-use graphical user interface. Our program simplifies metrics extraction while maintaining the quality of the analysis by integrating several algorithms. CalciumInsights stands out for its user-friendliness, ease of use, and robust data exploration features, such as tunable filters for improved accuracy. These features promote inclusivity and lower barriers to scientific research by making calcium signature analysis accessible to users of all programming skill levels.
Why it matches plant phenotyping methods植物の蛍光イメージングからカルシウム動態という生理状態を抽出・定量するオープンソース解析ツールが中心であり、植物を含む複数生物種のデータで評価されている。
abstractHere, we present a post-processing tool, CalciumInsights, which leverages the R packages Shiny and Golem.
Reproduction assets foundThe paper describes CalciumInsights, an open-source R/Shiny tool for calcium transient analysis. The authors explicitly state their code is publicly available on GitHub, which constitutes the paper's computational analysis asset. No plant-phenotyping datasets, images, or trained models are described; the tool is tissueCode · publicnt for publication
All authors have reviewed the manuscript and approved the final draft for publication.
Resource availability
Lead contact: Further information and requests for data may be directed to and will be
fulfilled by Mauricio Cabrera (mauricio.cabrera1@upr.edu)
Code: All codes used are publicly available in GitHub at https://github.com/AOG-Lab/CalciumInsights
References
1. Berridge MJ, Lipp P, Bootman MD. The versatility and universality of calcium signalling. Nat
Rev Mol Cell Biol [Internet]. 2000 Oct [cited 2024 Oct 21];1(1):11–21. Available from:
https://www.nature.com/articles/35036035
2. Sanderson MJ, Charles AC, Boitano S, Dirksen ER. Mechanisms and function of intercellularOpen asset ↗AOG-Lab/CalciumInsightspdf-raw-page:20 lines:1-37Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
= 0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータで作物の光合成形質を推定する高スループット表現型解析が中心であり、センサープラットフォームの実質的な適用に該当する。
titleHigh-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits.
Reproduction assets foundThe paper's authors publicly deposited the calibration and validation datasets of UAV-based spectral indices and photosynthetic trait measurements (Vcmax/Jmax) in a GitHub repository, directly reproducing this paper's phenotyping measurements and analysis inputs.Dataset · publicThe calibration and validation datasets of UAV-based spectral indices and photosynthesis supporting our results are available in the GitHub repositories at https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.git .Open asset ↗https://github.com/ljs19930709/UAV-and-Photosynthesis-dataset-.gitlines:107-117Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf color patterns in nature, shaped by genetic and environmental factors, can be analyzed using hyperspectral reflectance imaging. This protocol details step-by-step procedures for hyperspectral image acquisition, correction of uneven lighting, and spectral component analysis to reveal distinct and sometimes previously undetectable features on leaves. We outline how to identify key spectral components and project hyperspectral cubes onto them to highlight specific spectral traits. For complete details of this protocol, please refer to Krishnamoorthi et al. 1 .
Why it matches plant phenotyping methods葉の色・スペクトル形質を抽出するハイパースペクトル画像取得、補正、成分分析の手順を扱うプロトコルであり、植物フェノタイピング手法が中心です。
abstractThis protocol details step-by-step procedures for hyperspectral image acquisition, correction of uneven lighting, and spectral component analysis to reveal distinct and sometimes previously undetectable features on leaves.
Reproduction assets foundThe protocol's authors publicly release their analysis code (Python script, Jupyter notebook, conda environment) and sample hyperspectral images of ornamental plants via GitHub, Figshare, and a Zenodo-archived repository version. These are paper-specific phenotyping assets (hyperspectral leaf images and spectral unmix/Code · publicontacts, Shalini Krishnamoorthi ( kshalini@tll.org.sg ) and Dr. Daisuke Urano ( daisuke@tll.org.sg ).
Materials availability
No new experimental materials were utilized in this protocol.
Data and code availability
The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), SinOpen asset ↗Plant-Hyperspectral · dr-daisuke-urano/Plant-Hyperspectrallines:398-433Dataset · publicand sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High Performance Precision Agriculture system (A19E4a0101), and by the Singapore-MIT Alliance for Research & Technology, National Research Foundation: DisOpen asset ↗Zenodo · 10.5281/zenodo.15354496lines:398-433Dataset · publicano ( daisuke@tll.org.sg ).
Materials availability
No new experimental materials were utilized in this protocol.
Data and code availability
The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ).
Acknowledgments
This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High PeOpen asset ↗Figsharelines:398-433Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Stomatal anatomy and physiology define CO 2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding. Advances in instrumentation and data analyses present an opportunity to screen stomatal traits at scales relevant to plant breeding. We present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy. The method allows measurements to be collected in p @0.5 of 97.1% for stomatal detection. When validated in large field trials of 200 wheat genotypes under two irrigation treatments, FieldDino captured wide diversity in stomatal traits. FieldDino enables stomatal data collection and analysis at unprecedented scales in the field. This will advance research on stomatal biology and accelerate the incorporation of stomatal traits into plant breeding programs for resilience to abiotic stress.
Why it matches plant phenotyping methodsFieldDinoは、圃場で気孔の生理・解剖形質を高スループットに取得するフェノタイピング手法として開発され、大規模圃場試験で検証されているため含める。
abstractWe present a high‐throughput robust field‐based phenotyping approach, FieldDino, for screening stomatal physiology and anatomy.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a public GitHub repository containing the 3D-printed leaf clip STL files, the Python stomatal annotation/measurement script, and the FieldDino app, plus a public Roboflow dataset hosting the training/validation stomatal image set used to train the YOLOv8-M模型.Code · publicrse.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtsalter/FieldDinoMicroscopy . Validation datasets for the method are available on Roboflow – https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x .
References
Baloch , M. J.
,
J.
Dunwell
,
K.
DrN
, et al. 2013 . “
Morpho‐Physiological Characterization of Spring Wheat Genotypes Under Drought Stress
.” InterOpen asset ↗williamtsalter/FieldDinoMicroscopylines:337-498Dataset · publicResearch Infrastructure Strategy (NCRIS). Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.
Data Availability Statement
The data that support the findings of this study are openly available in Roboflow at https://universe.roboflow.com/narrabri-plant-physiology-hclvi/fielddino-training-set-200x . As outlined, all files for the 3D printed leaf clip, the Python script for stomatal annotation and the files and instructions for installing and using the FieldDino App are provided in a public GitHub repository which guides users through each step – https://github.com/williamtOpen asset ↗narrabri-plant-physiology-hclvi/fielddino-training-set-200xlines:337-498Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Photosynthesis is fundamental to agricultural productivity, but its relatively low light-to-biomass conversion efficiency represents an opportunity for enhancement. High-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity. However, the heritability of chlorophyll fluorescence parameters measured during the day is often low as a result of high levels of variation introduced by environmental fluctuations. Methods: To address these limitations, we measured fluorescence phenotypes at night, leveraging natural dark adaptation to minimize environmental noise. Results: Night measurement significantly increased the heritability of fluorescence traits compared to daytime measurements, with the maximum quantum yield of photosystem II (Fv/Fm) showing an increase in heritability from 0.32 to 0.72. Genome-wide association studies (GWAS) conducted using three photosynthetic fluorescence traits measured at night across two growing seasons identified several significant single nucleotide polymorphisms (SNPs). Notably, two candidate genes near SNPs linked to multiple fluorescence traits, Zm00001eb271820 and Zm00001eb012130, have known roles in photosynthesis regulation. Four of the significant signal nucleotide polymorphisms identified in GWAS conducted using nighttime collected data also exhibited statistically significant associations with the same phenotypes during the day. In a majority of other cases, direction of effect was consistent but greater variance in day measured data relative to night measured data resulted in the differences not being statistically significant. Discussion: These results highlight the effectiveness of phenotyping photosynthetic traits at night in reducing environmental noise and enhancing the discovery of genomic intervals related to photosynthesis. While nighttime data collection may not be applicable for all photosynthetic traits, it offers a promising avenue for advancing our understanding of the genetic variation of photosynthesis in modern crop species.
Why it matches plant phenotyping methods夜間の蛍光測定による光合成形質フェノタイピングを開発・評価し、昼間測定との比較で環境変動低減と遺伝率向上を検証しているため、手法が研究の中心である。
abstractHigh-throughput phenotyping is crucial for unraveling the genetic basis of variation in photosynthetic activity.
Reproduction assets foundThe paper's fluorescence phenotype datasets and analysis outputs (trait QC cutoffs, BLUP/heritability model tables, GWAS significant SNP tables) are stated to be available via the article's online Supplementary Material hosted by Frontiers. No standalone author code repository or named data repository accession appearsSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1595339/full#supplementary-material
References
Ali W. Grzybowski M. Torres-Rodríguez J. V. Li F. Shrestha N. Mathivanan R. K.
. ( 2024 ).
Quantitative genetics of photosynthetic trait variation in maize
. bioRxiv , eraf198 . doi:
10.1101/2024.11.25.625283
PMC12448886
40365812
Alter P. Dreissen A. Luo F.-L. MatsubaOpen asset ↗lines:613-651Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Chlorophyll fluorescenceLeafRootPhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
The invasive aquatic macrophyte Pontederia crassipes (water hyacinth) exhibits exceptional adaptability across a wide range of light environments, yet the mechanistic basis of its photosynthetic plasticity under both high- and low-light stress remains poorly resolved. This study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes. The Ye model provided superior accuracy (R2 > 0.996) in simulating the net photosynthetic rate (Pn) and electron transport rate (J), outperforming empirical models that overestimated Pnmax by 36–46% and Jmax by 1.5–24.7% and failed to predict saturation light intensity. Mechanistic analysis revealed that P. crassipes maintains high photosynthetic efficiency in low light (LUEmax = 0.030 mol mol−1 at 200 µmol photons m−2 s−1) and robust photoprotection under strong light (NPQmax = 1.375, PSII efficiency decline), supported by a large photosynthetic pigment pool (9.46 × 1016 molecules m−2) and high eigen-absorption cross-section (1.91 × 10−21 m2). Unlike terrestrial plants, its floating leaves experience enhanced irradiance due to water-surface reflection and are decoupled from water limitation via submerged root uptake, enabling flexible stomatal and energy regulation. Distinct thresholds for carboxylation efficiency (CEmax = 0.085 mol m−2 s−1) and water-use efficiency (WUEi-max = 45.91 μmol mol−1 and WUEinst = 1.96 μmol mmol−1) highlighted its flexible energy management strategies. These results establish the Ye model as a reliable tool for characterizing aquatic photosynthesis and reveal how P. crassipes balances light harvesting and dissipation to thrive in fluctuating environments. These resulting insights have implications for both understanding invasiveness and managing eutrophic aquatic systems.
Why it matches plant phenotyping methods複数の光合成モデルを実測データで比較・検証し、植物の光合成生理形質を推定するモデルの精度と適用性を中心的に評価しているため。
abstractThis study integrated chlorophyll fluorescence and gas-exchange analyses to evaluate three photosynthetic models—rectangular hyperbola (RH), non-rectangular hyperbola (NRH), and the Ye mechanistic model—in capturing light-response dynamics in P. crassipes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology14060600/s1 : Table S1. Gas-exchange measurement data.Open asset ↗lines:329-346Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Various high-throughput screening methods have been developed to explore plant phenotypes, primarily at the organ and whole plant levels. There is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap. This study used double-resonator piezoelectric cytometry biosensors to capture the dynamic changes in mechanical phenotypes of living cells of two rice species, drought-resistant Lvhan No. 1 and drought-sensitive 6527, under PEG6000 drought stress. In rice cells of Lvhan No. 1 and 6527, mechanomics parameters, including cell-generated surface stress (ΔS) and viscoelastic parameters (G', G″, G″/G'), were measured and compared under 5-25% PEG6000. Lvhan No. 1 showed larger viscoelastic but smaller surface stress changes with the same concentration of PEG6000. Moreover, Lvhan No. 1 cells showed better wall-plasma membrane-cytoskeleton continuum structure maintaining ability under drought stress, as proven by transient tension stress (ΔS > 0) and linear G'~ΔS, G″~ΔS relations at higher 15-25% PEG6000, but not for 6527 cells. Additionally, two distinct defense and drought resistance mechanisms were identified through dynamic G″/G' responses: (i) transient hardening followed by softening recovery under weak drought, and (ii) transient softening followed by hardening recovery under strong drought. The abilities of Lvhan No. 1 cells to both recover from transient hardening to softening and to recover from transient softening to hardening are better than those of 6527 cells. Overall, the dynamic mechanomics phenotypic patterns (ΔS, G', G″, G″/G', G'~ΔS, G″~ΔS) verified that Lvhan No. 1 has better drought resistance than that of 6527, which is consistent with the field data.
Why it matches plant phenotyping methods植物細胞の機械的表現型を取得する高スループットなバイオセンサー手法を用い、乾燥ストレス応答を定量化しており、表現型取得法が研究の中心である。
abstractThere is a need to develop phenomics methods at the cellular level to narrow down the genotype to phenotype gap.
Reproduction assets foundThe paper's DRPC phenotyping measurements (frequency and motional resistance traces underlying the ΔS, G′, G″ analyses) are provided as downloadable supplementary figures at the MDPI supplementary URL. No standalone public dataset or author analysis code repository is stated; the Data Availability Statement only offersSupplement · publicansient softening under strong drought. The results presented in this work demonstrated the potential to develop a new cellular mechanical phenotype platform to screen for biotic and abiotic stress-resistant crop varieties, as shown in Figure 11 .
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bios15060334/s1 , Figure S1: Changes in frequency and motional resistance of 9 MHz AT and BT cut chips during the adhesions of Lvhan No.1 rice cells followed by the treatments of different concentrations of PEG6000 stresses. (A, B, C, D, E): AT cut, (A1, B1, C1, D1, E1): BT cut, (A, A1): 5% PEG6000, (B, B1): 10%PEG6000, (C, C1) 15%Open asset ↗lines:136-159Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R 2 and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R 2 and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.
Why it matches plant phenotyping methods作物生育情報を定量化するUAVマルチスペクトルセンサー、補正法、処理システムを開発し、LAIと地上部バイオマス推定を検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS)
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 · publicThe figures, tables and data mentioned in the article can be downloaded from https://github.com/ikjkj2/Plant-Phenomics .Open asset ↗ikjkj2/Plant-Phenomicslines:395-413Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Photosynthetic activity can be monitored using pulse amplitude modulated (PAM) fluorescence or gas exchange. While PAM provides insight into the light-dependent reactions, gas exchange reflects CO 2 fixation and water balance. Accurate, non-invasive prediction of photosynthetic performance under varying conditions is highly relevant for phenotyping and stress diagnostics. Despite their physiological link, data from both methods do not always correlate. To systematically investigate this relationship, photosynthetic parameters were measured in maize ( Zea mays , C4) and basil ( Ocimum basilicum , C3) under different photon densities and spectral compositions. Maize showed the highest CO 2 assimilation rate of 30.99 ± 1.54 µmol CO 2 /(m²s) under 2000 PAR green light (527 nm), while basil reached 10.56 ± 0.92 µmol CO 2 /(m²s) under red light (630 nm). PAM-derived electron transport rates (ETR) increased with light intensity in a pattern similar to CO 2 assimilation, but did not reliably reflect its absolute values under all conditions. To improve prediction accuracy, we applied a machine learning model. XGBoost, a gradient-boosted decision tree algorithm, efficiently captures nonlinear interactions between physiological and environmental parameters. It achieved superior performance (R² = 0.847; MSE = 5.24) compared to the Random Forest model. Our model enables accurate photosynthesis prediction from PAM data across light intensities and spectral conditions in both C3 and C4 plants.
Why it matches plant phenotyping methodsPAM蛍光データから光合成性能を推定する機械学習モデルを開発・比較評価しており、植物生理形質の取得・推定手法が研究の中心である。
abstractTo improve prediction accuracy, we applied a machine learning model.
Reproduction assets foundThe paper's data availability statement names a public GitHub repository (KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlation) hosting the study's datasets, which underpin the PAM fluorescence/gas-exchange measurements and the Random Forest/XGBoost analysis. The statement does not explicitly distinguish code vs. Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-CorrelationOpen asset ↗KlirS/Model-for-PAM-Fluorescence-Gas-Exchange-Correlationlines:486-500Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Assessing the nutritional quality traits of pastures is crucial for germplasm and breeding evaluations, enabling the selection of high-quality forages to enhance livestock productivity. However, traditional laboratory analytical methods are logistically demanding and costly, particularly in large-scale trials, underscoring the need for rapid, precise, and high-throughput evaluation methods. Near-Infrared Spectroscopy (NIRS) optimizes the estimation of forage nutritional quality parameters by developing chemometric models that predict these parameters with high accuracy and precision, based on the association between NIRS data and wet chemistry analyses. This dataset, collected over ten years by the Tropical Forages Program at the International Center for Tropical Agriculture (CIAT) in Colombia, comprises 1112 samples. It includes 995 measurements of Neutral Detergent Fiber (NDF), 996 of Acid Detergent Fiber (ADF), 995 of In Vitro Dry Matter (IVDMD), and 469 of Crude Protein (CP), all obtained through wet chemistry methodologies. Additionally, the 1112 samples contain absorbance data spanning 400 to 2498 nanometers (nm) in 2 nm intervals, generating 1050 spectral data points per sample. Finally, this dataset is a valuable resource for predicting forage nutritional quality beyond conventional parameters, incorporating plant reflectance attributes to enhance selection strategies for optimized forage selection.
Why it matches plant phenotyping methods牧草の栄養品質形質をNIRSスペクトルから推定するための大規模データセットであり、湿式化学値との対応付けとケモメトリックモデル構築が中心的な方法的貢献である。
abstractNear-Infrared Spectroscopy (NIRS) optimizes the estimation of forage nutritional quality parameters by developing chemometric models that predict these parameters with high accuracy and precision, based on the association between NIRS data and wet chemistry analyses.
Reproduction assets foundThe paper is a data descriptor for a paper-specific public dataset: 1112 Urochloa humidicola samples with wet-chemistry traits (NDF, ADF, IVDMD, CP) and 1050-point NIR absorbance spectra (400–2498 nm), deposited in the Harvard Dataverse (DOI 10.7910/DVN/XPNIQY). The deposit is explicitly public and actionable; however,Dataset · publicData accessibility
Repository name: Harvard database
Data identification number: 10.7910/DVN/XPNIQYHarvard database · 10.7910/DVN/XPNIQYlines:1-49Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Plant cells are contained within a rigid network of cell walls. Cell walls serve as a structural material and a crucial signaling hub vital to all aspects of the plant life cycle. However, many features of the cell wall remain enigmatic, as it has been challenging to map its functional properties in live plants at subcellular resolution. Here, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls. CarboTag uses a small molecular motif, a pyridine boronic acid, that directs its cargo to the cell wall. We designed a suite of cell wall imaging probes based on CarboTag in various colors for multiplexing. Additionally, we developed new functional reporters for live quantitative imaging of key cell wall characteristics: network porosity, cell wall pH and the presence of reactive oxygen species. CarboTag paves the way for dynamic and quantitative mapping of cell wall responses at subcellular resolution. Subject terms: Plant cell biology, Fluorescence imaging
Why it matches plant phenotyping methods植物細胞壁のライブ機能イメージング用ツールボックスを開発し、孔隙率、pH、活性酸素などの細胞壁特性を定量化する手法が中心である。
abstractHere, we introduce CarboTag, a modular toolbox for live functional imaging of plant walls.
Reproduction assets foundThe paper's Data availability and Code availability statements both point to a public 4TU repository DOI containing the raw imaging/phenotyping data and the analysis code for this paper's CarboTag cell wall imaging measurements.Dataset · publicThe raw data associated with the figures in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 . Source data are provided with this paper.Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code · publicCode developed to process and analyze data in this paper are publicly available at https://doi.org/10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984 .Open asset ↗10.4121/3464fadd-ccb8-4a6c-9463-e3014bcdf984lines:179-240Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant growth and development strongly depend on the uptake of soil minerals and their distribution within plants. Various electrophysiological techniques have been developed to study these ion transport processes, from the single molecule-to whole plant level. An important non-invasive method is provided by Scanning Ion-Selective Electrodes (SISE), which are used to detect ion fluxes. These SISE-measurements depend on software that coordinates the perpetually electrode movement between two positions, as well as data collection and analysis. We developed two LabView-based programs; the SISE-monitor and SISE-analyser that enable ion flux recordings and their analysis, respectively. These applications are freely available, both as windows-executable files that enable routine measurements, as well as the LabView source code that allows deep insights into the routines used for measurement and further development of the programs to include new functions.
Why it matches plant phenotyping methods植物のイオンフラックスを取得・解析するソフトウェアを開発した研究であり、植物生理状態の測定ワークフローが中心です。
abstractWe developed two LabView-based programs; the SISE-monitor and SISE-analyser that enable ion flux recordings and their analysis, respectively.
Reproduction assets foundThe paper's authors publicly released their LabView-based SISE-Monitor and SISE-Analyser software (used for ion flux measurements and analysis) as *.exe and *.vi files on GitHub, with explicit availability statements and a public repository URL.Code · publiche SISE-programs would be made publicly
440 available. This could provide a range of versions of the SISE-programs, with a variety of
441 helpful features that would enable SISE-users to find an optimal solution for their
442 needs.
443
444 Availability and requirements
445
446 Project name: SISE-Software
447 Project home page: https://github.com/Rob-Roelfsema/SISE-Software-April2025
448 Operating system: Windows
449 Programming language: LabView
450 Other requirements: Supporting Virtual Instrument (VI) files (for *.vi files only)
451 License: GNU GPL
452 Any restrictions to use by non-academics: none
453
454 List of abbreviations
455 AI channel, Analog Input channel
456 ASCII, American StaOpen asset ↗Rob-Roelfsema/SISE-Software-April2025pdf-layout-page:15 lines:1-60Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R2 values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems.
Why it matches plant phenotyping methods画像・VIS-NIRセンシング、画像セグメンテーション、特徴抽出、機械学習による牧草バイオマスおよび品質形質の推定が研究の中心であり、植物フェノタイピング手法の開発・応用に該当する。
abstractThis study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies.
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 Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/agriengineering7040111/s1. Database S1: Database of experiment.Open asset ↗Database S1pdf-page:27 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recordings of electric potential changes on plant surfaces have been utilized to identify the components and mechanisms involved in the formation and transmission of systemic signals elicited by stimuli such as herbivory, wounding, or burning. The recorded responses, commonly referred to as slow wave or variation potentials, exhibit striking variability in their waveform. The extent to which this variability is due to differences in experimental procedures or plant biological variability remains unclear. Here, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding. This protocol serves as a comprehensive tutorial covering plant growth, procedures for reproducible mechanical wounding, critical aspects of electrophysiological recordings, and statistical analysis of surface potential recordings. It particularly emphasizes the construction and maintenance of electrodes, placement of the reference or ground electrode, mechanisms for wounding, and data analysis. This protocol aims to promote and facilitate the adoption, standardization, and interoperability of plant surface potential recordings among research groups, thereby increasing the reproducibility and comparability of data within the field. Key features • Recording electric potential changes on the petiole of 5-week-old Arabidopsis plants using noninvasive surface electrodes, improving the wounding procedure, reproducibility, and data processing from [1]. • Genotype-independent method for phenotyping, including parallel recordings from multiple plants. • Guidelines for plant growth conditions, unambiguous leaf assignment by order of emergence, and detailed instructions for electrode fabrication and maintenance. • Instructions for constructing devices for standardized, reproducible mechanical wounding along with a custom script for unbiased and semi-automated data analysis.
Why it matches plant phenotyping methods植物の表面電位を再現性高く記録・解析する電気生理学的フェノタイピング手法の詳細プロトコルであり、電極、標準化創傷、データ解析、再現性・相互運用性が中心的に扱われている。
abstractHere, we provide a detailed and robust protocol refined from years of experience in conducting leaf surface potential recordings of Arabidopsis thaliana in response to mechanical wounding.
Reproduction assets foundThe authors publicly deposit their paper-specific assets on GitHub: the SWPanalyzer.Rmd analysis script, raw surface potential recordings, and 3D-printed wounding grid designs, all directly used for this protocol's phenotyping measurements and analysis.Code · publicis thaliana Col-0 ecotype, other ecotypes may be used. However, differences in rosette morphology could affect petiole accessibility for electrode placement.
2. Prior to the measurement, plants should be acclimatized to the new conditions.
3. The raw data, analysis, R script, and 3D design can be found under the following link: https://github.com/jucbca/SWP-data_analysis
Troubleshooting
Recording:
Problem: You are unable to record any changes in electric potentials.
Solutions:
1. If you do not detect a signal in the wounded leaf:
a. Use a lighter to burn the leaf from the bottom. This method is the most reliable trigger of SWPs and serves as a positive control for your setup.
b. Check with aOpen asset ↗jucbca/SWP-data_analysislines:260-334Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made to assess nitrogen content (NC) at the organ level and its association with nitrogen use efficiency (NUE). Also, little is known about the performance of UAV-based image texture features of different spectral bands in monitoring crop nitrogen and NUE. In this study, multi-spectral images were collected throughout different stages of winter wheat in two independent field trials - a single-variety field trial and a multi-variety trial in 2021 and 2022, respectively in China and Germany. Forty-three multispectral vegetation indices (VIs) and forty texture features (TFs) were calculated from images and fed into the partial least squares regression (PLSR) and random forest (RF) regression models for predicting nitrogen-related indicators. Our main objectives were to (1) assess the potential of UAV-based multispectral imagery for predicting NC in different organs of winter wheat, (2) explore the transferability of different image features (VI and TF) and trained machine learning models in predicting NC, and (3) propose a technical workflow for mapping NUE using UAV imagery. The results showed that the correlation between different features (VIs and TFs) and NC in different organs varied between the pre-anthesis and post-anthesis stages. PLSR latent variables extracted from those VIs and TFs could be a great predictor for nitrogen agronomic efficiency (NAE). While adding TFs to VI-based models enhanced the model performance in predicting NC, inconsistency arose when applying the TF-based models trained based on one dataset to the other independent dataset that involved different varieties, UAVs, and cameras. Unsurprisingly, models trained with the multi-variety dataset show better transferability than the models trained with the single-variety dataset. This study not only demonstrates the promise of applying UAV-based imaging to estimate NC in different organs and map NUE in winter wheat but also highlights the importance of conducting model evaluations based on independent datasets.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から小麦の器官別窒素含量と窒素利用効率を推定する技術ワークフローを開発・検証しており、特徴量、機械学習モデル、独立データセットでの転移性評価が中心である。
abstractOur main objectives were to (1) assess the potential of UAV-based multispectral imagery for predicting NC in different organs of winter wheat, (2) explore the transferability of different image features (VI and TF) and trained machine learning models in predicting NC, and (3) propose a technical workflow for mapping NUE using UAV imagery.
Reproduction assets foundThe authors deposited the study's datasets (multi-temporal nitrogen content measurements and associated UAV multispectral image-derived features) on Zenodo, with an explicit data availability statement and a reference-list dataset entry. This is a paper-specific, publicly accessible asset. No author analysis code or TrDataset · publicThe datasets generated for this study are available on Zenodo ( https://doi.org/10.5281/zenodo.13732404 ).Open asset ↗Zenodo · 10.5281/zenodo.13732404lines:212-240Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The quantification of plant drought resistance, particularly embolism formation, within and across species, is critical for ecosystem management and agriculture. We developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity ( P 50 ) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method. Methods and results Our protocol uses inexpensive USB microscopes, which are secured along with the plants to a pegboard base to avoid movement. A Python program automatized the image acquisition. This method was applied to quantify P 50 in an exotic species ( Nicotiana glauca ) and native species ( Rhus integrifolia ) of the Mediterranean vegetation in Baja California, Mexico. Conclusions The intra- and interspecific patterns of variation in stem P 50 of N. glauca and R. integrifolia were obtained using the low-cost optical method with widely available and affordable materials that can be easily replicated for other species.
Why it matches plant phenotyping methods植物の茎の水理的脆弱性（P50）を測定する低コスト光学プロトコルを開発し、USB顕微鏡とPythonによる画像取得を用いて適用・検証しており、表現型取得法が研究の中心である。
abstractWe developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity ( P 50 ) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method.
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 · publicGranados (CICESE) for the initial design of the microscope stands, and Alexis Crespo Michel (CICESE) for his assistance in developing the multi‐threaded version of the image capture Python program.
DATA AVAILABILITY STATEMENT
Data of all experiments are provided in the Supporting Information. The Python Program is available at: https://github.com/miguel-aalonso/lowcost_P50 .
REFERENCES
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McAdaOpen asset ↗miguel-aalonso/lowcost_P50lines:264-337Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で性能を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background: The dark green coloration of bunching onion leaf blades is a key determinant of market value, nutritional quality, and visual appeal. This trait is regulated by a complex network of pigment interactions, which not only determine coloration but also serve as critical indicators of plant growth dynamics and stress responses. This study aimed to elucidate the mechanisms regulating the dark green trait and develop a predictive model for accurately assessing pigment composition. These advancements enable the efficient selection of dark green varieties and facilitate the establishment of optimal growth environments through plant growth monitoring. Methods: Seven varieties and lines of heat-tolerant bunching onions were analyzed, including two commercial F1 cultivars, along with two purebred varieties and three F1 hybrid lines bred in Yamaguchi Prefecture. The analysis was conducted on visible spectral reflectance data (400-700 nm at 20 nm intervals) and pigment compounds (chlorophyll a , chlorophyll b and pheophytin a , lutein, and β-carotene), whereas primary and secondary metabolites were assessed by using widely targeted metabolomics. In addition, a random forest regression model was constructed by using spectral reflectance data and pigment compound contents. Results: Principal component analysis based on spectral reflectance data and the comparative profiling of 186 metabolites revealed characteristic metabolite accumulation associated with each green color pattern. The "green" group showed greater accumulation of sugars, the "gray green" group was characterized by the accumulation of phenolic compounds, and the "dark green" group exhibited accumulation of cyanidins. These metabolites are suggested to accumulate in response to environmental stress, and these differences are likely to influence green coloration traits. Furthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation. However, since the regression model developed in this study is based on data obtained from greenhouse conditions, it is necessary to incorporate field trial results and reconstruct the model to enhance its adaptability. Conclusions: This study revealed that cyanidin is involved in the characteristics of dark green varieties. Additionally, it was demonstrated that chlorophyll a can be predicted using visible spectral reflectance. These findings suggest the potential for developing markers for the dark green trait, selecting high-pigment-accumulating varieties, and facilitating the simple real-time diagnosis of plant growth conditions and stress status, thereby enabling the establishment of optimal environmental conditions. Future studies will aim to elucidate the genetic factors regulating pigment accumulation, facilitating the breeding of dark green varieties with enhanced coloration traits for summer cultivation.
Why it matches plant phenotyping methods可視スペクトル反射データから葉のクロロフィルa含量を推定する回帰モデルを構築・検証しており、植物形質の取得・推定法が中心的です。
abstractFurthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation.
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 · publicthe raw MS data can be downloaded from DROP Met database ( https://prime.psc.riken.jp/menta.cgi/prime/drop_index#DM0069 , accessed on 14 February 2025).Open asset ↗DROP Met · DM0069lines:156-172Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of vegetation surveys, citizen science observations, and trait measurements, offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km resolution. Our approach achieves high predictive power, reaching correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance our understanding of plant community properties and ecosystem functioning globally, and can serve as useful tools in modeling global biogeochemical processes and informing worldwide conservation efforts. Ultimately, our framework highlights the power and necessity of crowdsourced biodiversity data in high-resolution plant trait modeling. We anticipate that advancements in biodiversity data collection and remote sensing capabilities will further refine global trait mapping, fostering a dynamic trait-based understanding of the biosphere.
Why it matches plant phenotyping methods地球観測データと生物多様性データを統合し、植物機能形質を空間的に推定・検証する方法が研究の中心であり、単なる生態学的測定ではない。
abstractcombining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km resolution
Reproduction assets foundThe paper's global plant trait maps (COMB/SCI/CIT products with COV and AOA masks as GeoTIFFs) are publicly available via the authors' data page and interactive Earth Engine map viewer. Code availability is listed as 'TBA' (no public repository), and TRY/sPlot are external community databases rather than paper-specificDataset · publicsformed trait products
670 and matched sPlot CWMs was then used to describe agreement. The specific foliar traits were
671 selected due to their general commonality between the selected previous studies.
672 6 Data availability
673 The trait products can be obtained and visualized using the following resources:
674 • Data page: https://geosense-freiburg.github.io/global-traits/
675 • Online map viewer: https://global-traits.projects.earthengine.app/view/global-traits
676 • Download link: TBA
677 7 Code availability
678 • GitHub: TBA
679 8 Acknowledgments
680 This study was funded by the German Research Foundation (DFG) within the framework of Big-
681 PlantSens (Assessing the Synergies of BiOpen asset ↗pdf-layout-page:23 lines:1-57Dataset · publicagreement. The specific foliar traits were
671 selected due to their general commonality between the selected previous studies.
672 6 Data availability
673 The trait products can be obtained and visualized using the following resources:
674 • Data page: https://geosense-freiburg.github.io/global-traits/
675 • Online map viewer: https://global-traits.projects.earthengine.app/view/global-traits
676 • Download link: TBA
677 7 Code availability
678 • GitHub: TBA
679 8 Acknowledgments
680 This study was funded by the German Research Foundation (DFG) within the framework of Big-
681 PlantSens (Assessing the Synergies of Big Data and Deep Learning for the Remote Sensing of Plant
682 Species; projecOpen asset ↗pdf-layout-page:23 lines:1-57Code / 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 · checked 14 Sept 2026
Published1 Mar 2025The Plant journal : for cell and molecular biologyCited by 6 · OpenAlex ↗
High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO 2 assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.
Why it matches plant phenotyping methodsPlantCVを用いた熱画像・気孔顕微鏡画像・蛍光画像の高スループット解析手法を開発・適用し、植物温度、気孔関連指標、光合成効率を推定しているため、表現型取得手法が中心的です。
abstractHigh-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study.
Reproduction assets foundThe paper's raw phenotyping image data (thermal, fluorescence, stomata, confocal microscopy) are deposited on Zenodo, and the authors' PlantCV analysis workflows and R scripts are on GitHub, including three PlantCV tutorials for thermal, stomata, and photosynthesis analysis.Dataset · publicaxial side of the leaf rather than a cross section. While small lipid droplets were present in the WT stomatal guard cells and epidermis, large lipid droplets were present in the HLP guard cells under both control and after 7 days of treatment (representative control images in Figure 8A–D , complete dataset available on Zenodo, https://zenodo.org/records/10711864 ). In addition, while HLP oil appeared to form spherical droplets, it did not “line” the stomatal opening as in WT (Figure 8C,D ).
Figure 8
High lipid producing (HLP) had excessive oil droplets in stomatal guard cells.
Representative confocal microscopy images, shown as focused Z‐stack, of tobacco leaf tissue fixed in paraformaOpen asset ↗Zenodolines:115-123Code · publicmated marginal means (LSMEANS) to determine which sample types were significantly different from others. Means are reported in text with standard error. Plots were made using ggplot2 package (v.3.5.0) in R. Jupyter notebooks associated with PlantCV analyses and R scripts associated with this manuscript are available on Github ( https://github.com/danforthcenter/tobacco‐heat‐paper ).
AUTHOR CONTRIBUTIONS
DKA, MAG, PDB, BSJ and KMM designed experiments. KMM and BSJ performed experiments and data analysis. KJC designed and aided KMM in confocal and brightfield microscopy experiments and advised TEM experiments. JW performed TEM experiments, and KG‐O and SK performed data analysis of TEM images.Open asset ↗GitHublines:171-182Code · publictification was used to isolate only individual plants in each mask. Then, the mask was applied to the registered thermal image to calculate the average plant temperature, as well as a histogram of pixel temperatures for each plant. A PlantCV workflow was used to analyze the images in parallel. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐thermal?tab=readme‐ov‐file (Acosta‐Gamboa et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Stomatal aperture measurements
To measure stomatal number and aperture, leaf impressioOpen asset ↗GitHublines:142-146Code · publicpackage was then used to calculate the number of stomata and the area of the aperture. A limitation of this method is that it does not provide the width and length of stomata, or measurements of the guard cells themselves; instead, it provides the aperture area (a result of length and width). A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐stomata‐tutorial‐pcv4 (Murphy, 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Photosynthesis and gas exchangeOpen asset ↗GitHublines:142-146Code · publicPlantCV (Gehan et al., 2017 ) using the photosynthesis package; the chlorophyll fluorescence image was used to mask the image for only plant pixels, and average F
v / F
m , F q ′ / F m ′ , NPQ, chlorophyll index, and anthocyanin index were calculated as an average per plant at each timepoint. A tutorial is available on GitHub: https://github.com/danforthcenter/plantcv‐tutorial‐photosynthesis?tab=readme‐ov‐file (Schuhl et al., 2024 ). Scripts for this project are available at https://github.com/danforthcenter/tobacco‐heat‐paper . Raw image data are available on Zenodo, https://zenodo.org/records/10711864 .
Microscopy imaging of lipids
Leaf samples analyzed for lipid content were taken from thOpen asset ↗GitHublines:156-164Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant breeding efficiency is crucial to develop varieties able to cope with climate change and support food and feed value chains. Genomic prediction (GP) has been a major step in increasing this efficiency and is now routinely used in breeding programs. Recently, phenomic prediction (PP) has gained attention as a promising complementary approach to GP, further increasing the breeding programs’ efficiency. Factors impacting the predictive ability (PA) of PP have been studied on many species but are not fully clarified. In this context, we studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection on a large multi-parental sorghum population including 2498 genotypes. Our results show that PP can compete with GP, that it is less affected by population structure, and can reach its maximal PA with smaller training sets than GP, but its performances are trait dependant. We also show that NIRS can be acquired in a reference environment to perform prediction in other environments and that it is possible to randomly select as little as 10 wavelengths to perform predictions. Finally, we show that spectra pre-processing, and statistical methods have a limited and unclear impact on PA. Our study confirms that PP is a relevant trait prediction method that deserves attention to optimize breeding schemes. The main challenges for the future will be to better understand the information contained in the spectra and disentangle their genetic and proxy components to optimize the use of PP in breeding programs. Key message Phenomic prediction is promising for sorghum breeding. Geneticists’ methods may not be suited to optimally extract spectral information.
Why it matches plant phenotyping methodsNIRSスペクトルから植物形質を予測するフェノミック予測手法を、前処理・予測法・集団構造・学習セット規模・取得環境・波長選択の観点で比較検証しており、手法が研究の中心です。
abstractwe studied the impacts of spectra pre-processing, prediction methods, population structure, training set size, NIRS acquisition environment and wavelength selection
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 determined a unique genetic consensus map by projecting the physical distance of the 51,545
markers on a high-quality genetic consensus map (Guindo et al., 2019) using the R package ziplinR
(https://github.com/jframi/ziplinR).Open asset ↗jframi/ziplinRpdf-page:6 lines:1-43Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The cuticle is a polymeric membrane covering all plant aerial organs of primary origin. It regulates water loss and defends against environmental stressors and pathogens. Despite its significance, understanding of the micro-mechanical properties of the cuticle (cuticular membrane; CM) remains limited. In this study, non-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit. The BLS signal arises from the photon interaction with thermally induced pressure waves and allows for imaging with mechanical contrast. The derived loss tangent showed significant differences with wax extraction from the CM and further with carbohydrate extraction from the DCM, consistent with tensile test results. Spatial heterogeneity between anticlinal and periclinal regions was observed by BLS microscopy of CM and DCM, but not in CU. The key conclusions are: (1) BLS is sensitive to micro-mechanical variations, particularly the strain-stiffening effect of the cutin framework, offering insights into the CM's micro-mechanical behavior and underlying chemical structures; (2) CM and DCM exhibit spatial micro-mechanical heterogeneity between periclinal and anticlinal regions.
Why it matches plant phenotyping methodsリンゴ果実のクチクラの微力学特性を、BLS顕微鏡による非侵襲的イメージングで測定・比較しており、植物器官の物性形質取得が研究の中心である。
abstractnon-invasive Brillouin light scattering (BLS) spectroscopy was applied to probe the micro-mechanics of native CM, dewaxed CM (DCM), and isolated cutin matrix (CU) of mature apple fruit.
Reproduction assets foundThe paper deposits its underlying Brillouin light scattering measurement data (primary and supplementary figures) in a public LUIS repository (DOI 10.25835/xvsi5g6m). The Brillouin analysis python script is only available on request, so it is not a public code asset.Dataset · publicThe underlying data for all the primary and Supplementary Figs. has been deposited in a publicly accessible repository [ https://doi.org/10.25835/xvsi5g6m ] 67 . Raw data may be obtained from the authors upon reasonable request.Open asset ↗10.25835/xvsi5g6mlines:177-254Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Radopholus similis severely damages banana roots causing significant yield losses. Field screening for resistance is labor intensive and inconsistent due to environmental variation and mixed nematode populations. The screenhouse offers a controlled environment but is limited by the time needed for root development and variation in plant growth. We developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media. Tissue culture plantlets grown in sterilized sand-MS were inoculated with 50 female R. similis after root development and nematodes extracted eight weeks after inoculation to calculate the reproduction factor (RF). Although RF values were higher for in vitro than in the screenhouse, accession responses showed similar trends under both conditions. The in vitro method was rapid, cost-effective with higher throughput, accelerating phenotyping and enabling rapid assessment of banana accessions for breeding programs. Some accessions responded differently to the two methods indicating that additional methods, such as root necrosis scores are important to confirm resistance. This study is the first in vitro-based demonstration of phenotyping for nematode resistance using modified sand-MS media with improved root development and pathogen interactions.
Why it matches plant phenotyping methodsバナナの線虫抵抗性という植物状態を評価する高スループットin vitroフェノタイピング法を開発・検証し、既存のスクリーンハウス法と比較しているため、方法が研究の中心である。
abstractWe developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the plant entry list, raw datasets, and generated/analyzed datasets (Extended data) for this banana R. similis resistance phenotyping study on Figshare under CC-BY 4.0, matching an allowed URL. No author analysis code was deposited.Dataset · publicThe list of all plant entries, raw datasets, and datasets generated during and/or analyzed during the current study (Extended data) referred to in the manuscript text as supplementary materials are publicly available in Figshare: High-throughput resistance phenotyping of banana ( Musa spp.) against Radopholus similis . https://doi.org/10.6084/m9.figshare.28787480.v3 . The dataset has a CC-BY 4.0 license applied.Open asset ↗Figshare · 10.6084/m9.figshare.28787480.v3lines:139-144Code / dataset availability confirmedOpenAlex · arXiv · checked 14 Sept 2026
Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch. The primary novelty of the software lies in its computational efficiency, robustness of parameter estimation, and flexibility in handling different types of response curves and sub-model functional forms. PhoTorch can fit both steady-state and non-steady-state gas exchange data with high efficiency and accuracy. Its flexibility allows for optional fitting of temperature and light response parameters, and can simultaneously fit light response curves and standard A/Ci curves. These features are not available within presently available A/Ci curve fitting packages. Results illustrated the robustness and efficiency of PhoTorch in fitting A/Ci curves with high variability and some level of artifacts and noise. PhoTorch is more than four times faster than benchmark software, which may be relevant when processing many non-steady-state A/Ci curves with hundreds of data points per curve. PhoTorch provides researchers from various fields with a reliable and efficient tool for analyzing photosynthetic data. The Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch.
Why it matches plant phenotyping methods光合成ガス交換データから生理形質を推定するモデルフィッティングソフトウェアの開発・ベンチマークが中心であり、植物フェノタイピング手法に該当する。
abstractThis work developed novel software, PhoTorch, for fitting parameters of the Farquhar, von Caemmerer, and Berry (FvCB) biochemical photosynthesis model based the parameter optimization components of the popular AI framework PyTorch.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Python package is openly accessible from the repository: https://github.com/GEMINI-Breeding/photorch .Open asset ↗GEMINI-Breeding/photorchlines:1-57Code / 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 confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract As climate change continues to influence global weather patterns, the frequency and severity of drought conditions are expected to increase, posing a significant challenge to crop production. In sorghum ( Sorghum bicolor L. Moench), a key cereal crop, the stay‐green trait is of particular importance as a measure of how well a genotype can tolerate post‐anthesis drought conditions, which are critical for harvestable yield. Despite its importance, there is a pressing need for a more efficient, accurate, and precise method to phenotype stay‐green in sorghum to enhance breeding efforts. To address this need, this study explores the application of random forest and XGBoost machine learning models for phenotyping the stay‐green trait in sorghum. These models provide quantitative measurements that have the potential to enhance genomic studies and offer additional benefits. Although correlations with vegetation indices were occasionally high, they were not sufficiently reliable to be used exclusively. The machine learning models, in contrast, showed high percentages of genetic variation explained and had high repeatability. The values generated by these algorithms enable plant breeders to efficiently make selections in their stay‐green breeding programs. Further research is needed to assess the robustness of these models across different environments and genetic material. Additionally, comparing these models with other machine learning approaches will help determine if decision tree‐based models are the most effective for this application. Overall, the models presented in this study serve as a promising foundation for improving the efficiency of stay‐green breeding programs in sorghum, but they require further validation and comparison with alternative approaches.
Why it matches plant phenotyping methodsソルガムのstay-green形質をUAV画像と機械学習で定量化する手法を開発・評価しており、表現型取得・抽出が研究の中心である。反復性や遺伝的変異の説明率も評価している。
abstractthere is a pressing need for a more efficient, accurate, and precise method to phenotype stay‐green in sorghum
Reproduction assets foundThe paper's data availability statement explicitly releases the raw tabular stay-green phenotyping data and the authors' Python machine learning scripts in a public GitHub repository, directly supporting this paper's phenotyping measurements and analysis.Code · publicwould like to thank Bruce Spinhirne for
his assistance with the management of the experiment.
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
The raw tabular data and Python machine learning scripts
used in this study are available at: https://github.com/AcePugh/staygreen-prediction.git.O RC I D
N. AcePugh https://orcid.org/0000-0001-7129-6556
R E F E R E N C E S
Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., &
Younis, I. (2022). A review of the global climate change impacts,
adaptation, and sustainable mitigation measures. Environmental Sci-
ence and Pollution Research, 29(28), 42539–Open asset ↗AcePugh/staygreen-predictionpdf-raw-page:18 lines:1-81Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
A wide range of portable chlorophyll meters are increasingly being used to measure leaf chlorophyll content as an indicator of plant performance, providing reference data for remote sensing studies. We tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference. Optical Chl assessments included measurements taken by four chlorophyll meters: three transmittance-based (SPAD-502, Dualex-4 Scientific, and MultispeQ 2.0), one fluorescence-based (CCM-300), and vegetation indices calculated from the 400-2500 nm leaf reflectance acquired using an ASD FieldSpec and a contact plant probe. Three leaf types with different anatomy were included: dorsiventral laminar leaves, grass leaves, and needles. On laminar leaves, all instruments performed well for chlorophyll content estimation (R 2 > 0.80, nRMSE 2 > 0.90, nRMSE 2 = 0.45, nRMSE = 11%) and failed for SPAD. For Norway spruce needles, the relation of CCM-300 values to chlorophyll content was also weak (R 2 = 0.45, nRMSE = 11%). To improve the accuracy of data used for remote sensing algorithm development, we recommend calibration of chlorophyll meter measurements with biochemical assessments, especially for species with anatomy other than laminar dicot leaves. The take-home message is that portable chlorophyll meters perform well for laminar leaves and grasses with wider leaves, however, their accuracy is limited for conifer needles and narrow grass leaves. Species-specific calibrations are necessary to account for anatomical variations, and adjustments in sampling protocols may be required to improve measurement reliability.
Why it matches plant phenotyping methods携帯型クロロフィルメーターによる葉クロロフィル量推定を、葉の解剖学的差異と生化学測定を基準に比較・検証し、校正とサンプリング改善を提案しているため、植物表現型取得法が中心です。
abstractWe tested the effect of leaf anatomy on the relationship between optical assessments of chlorophyll (Chl) against biochemically determined Chl content as a reference.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's chlorophyll measurement and trait data in a public Zenodo repository, which is an allowed URL. No separate author analysis code URL is given (analyses were in Matlab/R), so the qualifying asset is the deposited dataset.Dataset · publicData are available in Zenodo repository found by https://zenodo.org/records/14615430.Open asset ↗Zenodo · 14615430pdf-page:14 lines:1-62Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Global climate change has driven breeding programs to develop abiotic stress‐resilient plant varieties. Traditionally, assessing drought resilience involves labor‐intensive and time‐consuming processes. This study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle. We grew transgenic maize hybrids in two trials, one irrigated and another subjected to drought stress, and used a drone equipped with red–green–blue (RGB) and multispectral sensors to capture images of the plots over time. Machine learning models and various prediction scenarios revealed significant correlations between vegetation indices over time. Interestingly, the RGB sensor outperformed the multispectral sensor in trait prediction. Prediction accuracy across scenarios with untested genotypes and environments ranged from 0.40 to 0.70 for grain yield, 0.43 to 0.69 for days to anthesis, 0.51 to 0.67 for days to silking, and 0.35 to 0.57 for plant height. Ridge and random forest models consistently delivered the most accurate predictions across traits and environments. The vegetation indices normalized green–red difference index, VARI, and RCC also effectively predicted and captured the plant response to drought. This study highlights the value of UAS phenotyping as a practical tool for assessing abiotic stress due to its straightforward implementation.
Why it matches plant phenotyping methodsUASによるRGB・マルチスペクトル画像と機械学習で、作物形質および干ばつ応答を予測するフェノタイピング手法を、複数環境・遺伝子型で検証しているため。
abstractThis study used an unmanned aerial system (UAS) to predict key phenotyping traits in maize ( Zea mays L.) and monitor plant response to drought during the crop cycle.
Reproduction assets foundThe paper's data availability statement says all codes and datasets (phenomic prediction scripts, folder 'Phenomic prediction', and described datasets) are publicly available at the authors' GCCRC publications page and on Dryad (doi:10.5061/dryad.0zpc8677b).Code · public14 of 16 PEREIRA ET AL.
in this work to perform phenomic prediction for all the eight
models and the four cross-validation scenarios were given as
examples in the folder “Phenomic prediction.” All the codes
and the datasets described are available at https://www.gccrc.unicamp.br/publications/ and https://doi.org/10.5061/dryad.0zpc8677b.O RC I D
HelcioDuartePereira https://orcid.org/0000-0002-2837-9396
Juliana Vieira Almeida Nonato https://orcid.org/0000-0003-4448-4652
Rafaela CarolineRangni MoltocaroDuarte https://orcid.org/0000-0003-2622-3758
Isabel Rodrigues Gerhardt https://orcid.org/0000-0003-1397-0199
RicardoAuOpen asset ↗GCCRCpdf-raw-page:14 lines:1-75Code / 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 confirmedEurope PMC · checked 15 Sept 2026
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employed a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrated superior modeling of environmental influences and out-performs PCA (principal component analysis), PLSR (Partial Least square regression) and vanilla autoencoders by 7 times for 'Days to Pollen' trait and 10 times improved predictive performance for 'Yield'. By disentangling latent features, the CAE provided a powerful tool for precision breeding and genetic research. This work has significantly enhanced trait prediction models, advancing agricultural and biological sciences.
Why it matches plant phenotyping methods植物形質（開花日数・収量）の予測を目的とする新規オートエンコーダを開発し、既存手法と比較評価しており、計算的な形質推定手法が研究の中心である。
abstractwe developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features.
Reproduction assets foundThe paper's data availability statement explicitly deposits the hyperspectral reflectance dataset and trained model weights on Figshare, and the authors' analysis code on a public Bitbucket repository (baskargroup/cae_hyperspectral). Both are paper-specific, public, and actionable.Dataset · publicte the technical advantages of disentanglement, it is not immediately clear how to connect these disentangled features to biological insights.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/articles/dataset/Hyperspectral_reflectance_data_molecular_and_weights_for_trained_model/24808491/4 ; https://bitbucket.org/baskargroup/cae_hyperspectral/src/main/ .
Author contributions
AP: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. TJ: Conceptualization, Open asset ↗figshare · 24808491lines:460-495Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Cover crops (CC) immobilize mineral soil N in their biomass, preventing N losses during crop rotation intervals. As the CC biomass is incorporated into the soil and decomposes, N is released for the following main crop. The efficiency of CC N uptake and release depends on CC quantity and quality, which can be enhanced in mixtures. Traditional N uptake measurements are labour-intensive and limited in capturing spatial variability. We calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures. This innovative approach combined vegetation indices, textural features, and a photogrammetry-derived canopy surface model to predict CC traits. Linear models were trained for biomass, N uptake, and C:N predictions, while a K-Nearest-Neighbour model was trained for N concentration. When evaluated on the test set, the calibrated remote sensing models accurately predicted CC aboveground biomass (R 2 : 0.71, RMSE: 287.1 kg/ha, NRMSE: 11.74 %), N concentration (R 2 : 0.80, RMSE: 1.77 gN /kg, NRMSE: 6.96 %), N uptake (R 2 : 0.56, RMSE: 9.38 kgN /ha, NRMSE: 15.08 %), and C:N ratio (R 2 : 0.62, RMSE: 1.86, NRMSE: 10.98 %). The field experiment included monocultures, bi-, and tri-species mixtures of common vetch ( Vicia sativa ), black oat ( Avena strigosa ), and fodder radish ( Raphanus sativus ). N uptake was similar between treatments, yet the CC species differed in strategies, producing high biomass with low N concentration or vice versa. This study provides a basis for spatially predicting key CC traits using UAV optical data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、テクスチャ特徴、フォトグラメトリ由来モデルを用いて、植物のバイオマス、窒素濃度、窒素吸収量、C:N比を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractWe calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures.
Reproduction assets foundThe paper's Data availability statement explicitly states the authors' R code for image processing, model training, and figure production is publicly available on the authors' WUR GitLab repository (uav4covercroptraits). No phenotype dataset or image deposit is stated separately.Code · publictal for the
UAV data acquisition.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2024.100608.
Data availability
The R code generated during this study to process the images, train
the models and produce the figures, is publicly available at https://git.wur.nl/dall002/uav4covercroptraits.References
[1] C. Aita, S.J. Giacomini, Crop residue decomposition and nitrogen release in singleOpen asset ↗git.wur.nl/dall002/uav4covercroptraitspdf-raw-page:10 lines:1-89Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Winter oilseed rape (WOSR, Brassica napus L.) is the third largest oil crop worldwide that also provides a source of high quality plant-based proteins. Nitrogen (N) and carbon (C) play a key role in plant growth. Determination of N and C contents of plant tissues throughout the growth cycle is crucial in assessing plant nutritional status and allowing precise input management. In the dataset presented in this article, 2427 WOSR samples arising from a large diversity of tissues collected on WOSR diversity were analyzed by near infrared spectroscopy from 4000 to 12,000 cm -1 . At the same time, reference chemical data for the N and C contents of the same samples were determined by elemental analysis using the Dumas method. Partial least squares regression has been used to develop predictive models linking spectral and chemical data, so that new samples can be characterized without the need for reference methods. This dataset could be used to test new calculation algorithms in order to enhance prediction performance or for training purposes. These models can be used as a rapid method for determining N and/or C content, adding to decision-support tools for fertilizer application throughout the plant developmental cycle.
Why it matches plant phenotyping methods植物組織の窒素・炭素含量を近赤外分光で推定する予測モデルと大規模データセットが研究の中心であり、植物形質・栄養状態の取得手法として実質的です。
abstractIn the dataset presented in this article, 2427 WOSR samples arising from a large diversity of tissues collected on WOSR diversity were analyzed by near infrared spectroscopy
Reproduction assets foundThe article is a Data in Brief describing a paper-specific public dataset of NIR spectra and N/C reference measurements for 2427 Brassica napus tissue samples, deposited in Data INRAE with an explicit DOI and direct URL. The dataset includes the raw spectral data (.csv), chemical reference data, and the PLS calibrationDataset · publicData source location
Institution:
Institute of Genetics, Environment and Plant Protection (IGEPP); INRAE,
Institut Agro, University of Rennes
City/Town/Region: 35,650 Le Rheu
Country: France
Data accessibility
Repository name: Data INRAE ( https://data.inrae.fr/ )
Data identification number: 10.57745/6VYUQN
Direct URL to data: https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/6VYUQN
Related research article
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Value of the Data
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The dataset establishes a link between spectral properties and chemical composition (N, C) of a wide variety of plant tissues in winter oilseed rape. The prediction models can be used by diverse communities (scientists, breeders, prOpen asset ↗Data INRAE · 10.57745/6VYUQNlines:1-63Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology.
Why it matches plant phenotyping methodsNIRスペクトルから植物形質を予測するソフトウェアおよびデータベースを開発しており、形質取得・推定手法が研究の中心である。
abstractWe developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits
Reproduction assets foundThe paper's NIRS spectra and 81 trait measurements for 5,325 Arabidopsis thaliana individuals are publicly hosted in the authors' NIRSpredict Shiny application, and the application's R code is deposited on the authors' GitHub repository (AxelVaillant/NirsPredict), as stated in the Data availability section. Both are直接,Dataset · publicTrait values are publicly available in the NIRSpredict database atOpen asset ↗pdf-page:10 lines:1-65Code · publicThe R code of the application is available on a GitHubOpen asset ↗pdf-page:10 lines:1-65Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing-retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance-based detection of the 535-nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy-based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications.
Why it matches plant phenotyping methods葉分光法とスペクトルアンミキシングにより、植物のFQEや熱散逸に関連する吸収変化を非破壊・定量的に取得し、ストレス診断への有効性を評価しているため、植物生理フェノタイピング手法の応用・評価が中心です。
abstractUsing leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the paper's raw and processed phenotyping/spectroscopy measurements open access on Zenodo (doi: 10.5281/zenodo.12800064). This is a paper-specific, public, actionable dataset. However, the Zenodo URL is not among the allowed_urls, so no asset URL is providedDataset · publicData Availability Statement
Raw and processed data are available open access through the Zenodo repository (doi: 10.5281/zenodo.12800064 ).Zenodo · 10.5281/zenodo.12800064lines:539-574Code / 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 · Crossref · checked 14 Sept 2026
Plant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red-green-blue (RGB) indices collected with standard settings in non-standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R² = 0.554, root mean square error [RMSE] = 219.29 kg ha⁻¹), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and carbon:nitrogen ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.
Why it matches plant phenotyping methods低コストRGB画像と画素分類を用いて、植物被覆、バイオマス、窒素含量、C:N比を推定する手法を開発・評価しており、表現型取得が研究の中心です。
abstractUsing low-cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high-throughput technologies in research and crop management.
Reproduction assets foundThe paper's authors state that all referenced analysis scripts for the RGB vegetation index processing, thresholding, and canopy cover estimation are publicly available on GitHub. The phenotype/trait data (images, biomass, N, C:N measurements) are deposited at a U of M repository (hdl.handle.net/11299/263900), but thatCode · publicreferenced scripts are available at https://github.com/RTGS- of nitrogen dictated by biomass and nitrogen content, and theOpen asset ↗pdf-page:4 lines:1-49Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Field / plotTissuePhysiological trait estimationGrowth / time-series analysisGrowth / development / phenology
Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-21) in Simoncouche and two growing seasons (2020-21) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter-weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.
Why it matches plant phenotyping methods樹木の形成層・木部における週次δ13C変動を追跡する測定法を開発し、複数年・地点で適用して検証しているため、植物の生理状態を取得する方法が中心である。
abstractwe developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species
Reproduction assets foundThe paper's weekly δ13C cambium/xylem measurements are stated to be publicly available via the authors' Quebec-Labrador tree-ring dashboard. A GitHub repository for figure data is mentioned but without a URL and only 'upon publication', so it is not actionable. NOAA GML and the Arizona repository URL are external/citedDataset · publicCanada, 490 de La Couronne, Québec, QC G1K 9A9, Canada.
Conflict of interest
None declared.
Funding
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) to É.B. (RGPIN 2021-04216).
Data availability
The weekly carbon isotope measurements published in the study will be available here: https://quebeclabradortr.shinyapps.io/TRdashboard4/ . Additional data used to produce the figures will be available from a GitHub repository, upon publication of the article.
References
Alvarez C, Bégin C, Savard MM, Dinis L, Marion J, Smirnoff A, Bégin Y. (2018). Relevance of using whole-ring stable isotopes of black spruce trees in the perspective of climate reconstrOpen asset ↗TRdashboard4lines:362-389Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Identification of high carotenoid germplasm is crucial to assist breeders in provitamin-A biofortification of sorghum (Sorghum bicolor [L.] Moench). High-performance liquid chromatography is the gold standard for carotenoid quantification, however, it is not feasible for large scale phenotyping due to its high cost and low throughput. In this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding. We hypothesized that visual, color-based selection can be an effective strategy to identify high-carotenoid accessions. Yellow grain had significantly higher carotenoid content than red, brown, and white grain. The degree of yellowness could distinguish the presence or absence of carotenoids, but could not distinguish carotenoid concentrations within yellow-only accessions. The degree of luminosity of the grain, however, was able to better predict carotenoid concentrations within yellow-only accessions. Genome-wide association studies identified significant marker-trait associations for qualitative and quantitative grain color traits and carotenoid concentrations near carotenoid pathway genes-ZEP, PDS, CYP97A, NCED, CCD, and LycE-three of which were common between grain color and carotenoid traits. These findings suggest that using grain color as a method for screening germplasm may be an effective high-throughput selection tool for prebreeding and early-stage breeding in carotenoid biofortification.
Why it matches plant phenotyping methods穀粒色を用いたカロテノイド含量推定・高スループット選抜法の実現可能性を検証しており、植物形質取得法が研究の中心である。
abstractIn this study, we tested the feasibility of using grain color as a high-throughput method of carotenoid biofortification breeding.
Reproduction assets foundThe paper's grain-color/carotenoid phenotyping data are in public supplementary files (Supplementary Data S1–S3: GRIN color traits, visual scores, colorimeter measurements), and the authors' analysis code is publicly deposited on GitHub with an explicit availability statement.Code · publicAll other data files are available in the supplemental files and code is available at: https://github.com/rmcdower/sorghumbiofortification/tree/a8457f87068867eb687235c255a6222863102e1aOpen asset ↗rmcdower/sorghumbiofortification · a8457f87068867eb687235c255a6222863102e1alines:134-147Dataset · publicThree grains each per accession were scored independently by two individuals and classified as white, yellow, red, or brown (Supplementary Data S2).Open asset ↗lines:71-78Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations.
Why it matches plant phenotyping methods安価なカメラ画像から芝草の被覆・色などの形質を抽出し、画像補正の感度、他センサーおよび既存測定との相関を検証しており、植物表現型取得法が中心である。
abstractThis study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations.
Reproduction assets foundThe paper deposits its phenotype measurement datasets (raw data, ANOVA statistics, time series) both in MDPI Supplementary Materials and in a public AgDataCommons dataset. Plant images are only available upon request, and no author analysis code repository with a public URL is stated.Dataset · publicDatasets are supplied in the Supplementary Materials and at https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_Visualizing_Plant_Responses_Novel_Insights_Possible_through_Affordable_Imaging_Techniques_in_the_Greenhouse/26527447 , accessed on 13 August 2024; images are available upon request.Open asset ↗agdatacommons.nal.usda.gov · 26527447lines:97-173Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24206676/s1 . Supplementary S1: F-values. Supplementary S2: p -values for experimental effects ANOVA ( Table 4 ), nine additional individual time series charts. Supplementary S3: of BA, %C, %G, DGCI, HSVi, NDRE, NIR, RED, and RE metrics. Supplementary S4: Additional discussion text. Supplementary S5: Raw data.Open asset ↗lines:97-173Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Temperature is a critical environmental factor affecting nearly all plant processes, including growth, development, and yield. Yet, despite decades of research, we lack the ability to predict plant performance at different temperatures, limiting the development of climate-resilient crops. Further, there is a pressing need to bridge the gap between the prediction of physiological and molecular traits to improve our understanding and manipulation of plant temperature responses. Here, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures. We showed that the model can be employed for in silico identification of genes that affect plant growth at suboptimal growth temperature. Using mutant lines, we validated the genes predicted to affect plant growth, demonstrating the potential of metabolic modeling in accurately predicting plant thermal responses. The temperature-dependent enzyme-constrained metabolic model provides a template that can be used for developing sophisticated strategies to engineer climate-resilient crops.
Why it matches plant phenotyping methods温度依存性の酵素制約代謝モデルを開発し、成長関連表現型の予測と変異体による検証を行っており、植物表現型推定手法が研究の中心です。
abstractHere, we developed the first enzyme-constrained model of Arabidopsis thaliana ’s metabolism, facilitating predictions of growth-related phenotypes at different temperatures.
Reproduction assets foundThe paper's computational analysis code (simulations and statistics), the machine-learning tool for protein thermostability optima, and the refined AraCore metabolic model are all explicitly deposited in public GitHub repositories by the authors. No standalone public phenotype dataset URL is given; compiled RGR and CO2Code · public30
Declaration of interests
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The authors declare no competing interests.
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Code availability
685
Custom computer code that was developed for simulations and statistical analyses in this
686
study are publicly available at https://github.com/pwendering/AraTModel. The code
687
developed for machine learning of protein thermostability optima was deposited in a
688
separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
690
https://github.com/pwendering/ArabidopsisCoreModel. All remaining data areOpen asset ↗pwendering/AraTModelpdf-raw-page:30 lines:1-31Code · public686
study are publicly available at https://github.com/pwendering/AraTModel. The code
687
developed for machine learning of protein thermostability optima was deposited in a
688
separate repository, which is publicly available at https://github.com/pwendering/topt-689
predict. The refined AraCore model can be retrieved from
690
https://github.com/pwendering/ArabidopsisCoreModel. All remaining data are provided
691
with this manuscript and supplementary material.
692
693
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder foOpen asset ↗pwendering/ArabidopsisCoreModelpdf-raw-page:30 lines:1-31Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R² value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R² ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R² = 0.74) and with the Sentinel-2 (R² = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R² = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.
Why it matches plant phenotyping methods航空ハイパースペクトル画像とSentinel画像、複数の推定モデルを用いて小麦の収量・タンパク質濃度・窒素出力を定量化し、センサー間の性能を比較しているため、表現型取得・推定法が研究の中心である。
abstractThis study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery.
Reproduction assets foundThe paper's Data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.21865410.v1) containing the data supporting the study's winter wheat trait estimations from airborne hyperspectral and Sentinel imagery. This is a paper-specific, publicly accessible dataset with an authors' URL. No作者分析Dataset · publicatory work,
and QuantaLab-IAS-CSIC staff members A. Hornero, A. Vera, D. Notario, and R. Romero for airborne and
laboratory assistance.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Data availability The data that support the findings presented in this study are available online at https://doi.org/10.6084/m9.figshare.21865410.v1.Declarations
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the oOpen asset ↗figshare · 10.6084/m9.figshare.21865410.v1pdf-raw-page:20 lines:1-46Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN 15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光によるダイズの生物的窒素固定活性の非侵襲的定量法を開発し、IRMSで検証しているため、植物生理状態の取得手法が中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the original Raman spectra/BNF measurement data on FigShare, a public, paper-specific asset directly reproducing the study's phenotyping measurements.Dataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare
at https://figshare.com/articles/dataset/dx_doi_org_10_6084_m9_figshare_25909780/25909780 (ac-
cessed on 28 May 2024).Open asset ↗FigSharepdf-page:13 lines:1-60Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Chlorophyll fluorescence is a well-established method to estimate chlorophyll content in leaves. A popular fluorescence-based meter, the Opti-Sciences CCM-300 Chlorophyll Content Meter (CCM-300), utilizes the fluorescence ratio F735/F700 and equations derived from experiments using broadleaf species to provide a direct, rapid estimate of chlorophyll content used for many applications. We sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use. We linked CCM-300 measurements of broadleaf, conifer, and graminoid samples in 2018 and 2019 to high-performance liquid chromatography (HPLC) and/or spectrophotometric (Spec) analysis of the same leaves. We observed a significant difference between the CCM-300 and HPLC/Spec, but not between HPLC and Spec. In comparison to HPLC, the CCM-300 performed better for broadleaves (r = 0.55, RMSE = 154.76) than conifers (r = 0.52, RMSE = 171.16) and graminoids (r = 0.32, RMSE = 127.12). We observed a slight deterioration in meter performance between years, potentially due to meter calibration. Our results show that the CCM-300 is reliable to demonstrate coarse variations in chlorophyll but may be limited for cross-plant functional type studies and comparisons across years.
Why it matches plant phenotyping methodsCCM-300による葉のクロロフィル含量測定法を、HPLCおよび分光測定と比較して信頼性・校正性能を検証しており、植物表現型取得法が研究の中心である。
abstractWe sought to quantify the performance of the CCM-300 relative to more intensive methods, both across plant functional types and years of use.
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/s24154784/s1 , Figure S1: HPLC measurements from the UW-Madison dataset ( n = 26).Open asset ↗lines:262-279Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The analysis of photosynthetic traits has become an integral part of plant (eco-)physiology. Many of these characteristics are not directly measured, but calculated from combinations of several, more direct, measurements. The calculations of such derived variables are based on underlying physical models and may use additional constants or assumed values. Commercially available gas-exchange instruments typically report such derived variables, but the available implementations use different definitions and assumptions. Moreover, no software is currently available to allow a fully scripted and reproducible workflow that includes importing data, pre-processing and recalculating derived quantities. The R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations. In addition, the package facilitates performing sensitivity analyses on variables or assumptions used in the calculations to allow researchers to better assess the robustness of the results. The use of the package and how to perform sensitivity analyses are demonstrated using three different examples.
Why it matches plant phenotyping methods植物の光合成形質データを標準化・再計算・感度分析するRパッケージを開発しており、植物生理形質の取得後処理と再現可能な解析ワークフローが中心である。
abstractThe R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations.
Reproduction assets foundThe paper's gas-exchange phenotyping data (poplar, tobacco, GFS-3000 examples) and all analysis code for the gasanalyzer package are publicly available in the authors' GitLab repository, with a stable release on CRAN.Code · publicd by an LI-6400 and 13CO2/12CO2 ratios were obtained using a Los Gatos Research CCIA-36d isotope analyser. Details of the growth conditions and experimental design are given in Tholen et al. (2012).
All data and code used to generate the figures in this article are available at the GitLab repository for the gasanalyzer package (https://gitlab.com/plantphys/gasanalyzer).
Results and Discussion
The R package gasanalyzer provides methods for importing data from different instruments and presents the data in a consistent format with a standardized nomenclature [for a complete list, seeSupporting Information—Table S1]. The package can be used not only to pre-process data for analysis but also toOpen asset ↗plantphys/gasanalyzerhtml-lines:105-113Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.
Why it matches plant phenotyping methodsPlantEyeを用いたデジタルフェノタイピングの適用と精度評価が研究上の主要要素であり、植物のキャノピー温度や成長状態を測定し、植物サイズによる測定精度低下も検討している。
abstractPlants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the datasets generated and analysed in this potato pot-binding phenotyping study.Dataset · publicThe datasets generated and analysed for this study can be found in the Zendo repository at https://doi.org/10.5281/zenodo.10707587 .Open asset ↗Zenodo · 10.5281/zenodo.10707587lines:845-856Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularization strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans.
Why it matches plant phenotyping methodsラマン分光と回帰モデルを用いてダイズの生物的窒素固定活性を定量する手法を開発・検証しており、植物の生理状態の取得が研究の中心である。
abstractThis study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans.
Reproduction assets foundThe authors state that the original data (Raman spectra and BNF/IRMS measurements) are openly available on FigShare under DOI 10.6084/m9.figshare.25909780. This is a paper-specific, publicly deposited dataset directly reproducing the study's phenotyping measurements. No author analysis code or trained model is reportedDataset · publicData Availability Statement: The original data presented in the study are openly available in FigShare at
10.6084/m9.figshare.25909780FigShare · 10.6084/m9.figshare.25909780pdf-raw-page:13 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods高解像度画像・深層学習による器官レベル老化モニタリングと熱画像による連続的なキャノピー温度測定を開発・適用し、stay-green機能の表現型評価法として検証しているため、方法が中心的である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Reproduction assets foundThe paper publicly deposits its manually annotated segmentation datasets (target-domain patches for the off-nadir stem/ear segmentation model) via the ETH Zurich research repository. All other raw phenotyping data (thermal images, physiological measurements) is only available on request from the authors. Generic tools/Dataset · publicd through logical operations to obtain the fractions of green, chlorotic, and necrotic tissues for each vegetation component. For details, refer to ( Anderegg et al., 2023 ). The annotated data sets representing the target domain will be made freely available via the Repository for Publications and Research data of ETH Zürich ( https://doi.org/10.3929/ethz-b-000668219 ).
Figure 2
Effects of canopy shading on agronomic traits and canopy characteristics. Effects of shading on (A) grain yield, (B) above ground vegetative dry biomass (total above ground biomass after threshing), (C) peduncle length, (D) plant height, (E) spike volume, (F) thousand kernel weight, (G) grain protein concentration.Open asset ↗Repository for Publications and Research data of ETH Zürich · 10.3929/ethz-b-000668219lines:58-67Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Early sowing can help summer crops escape drought and can mitigate the impacts of climate change on them. However, it exposes them to cold stress during initial developmental stages, which has both immediate and long-term effects on development and physiology. To understand how early night-chilling stress impacts plant development and yield, we studied the reference sunflower line XRQ under controlled, semi-controlled and field conditions. We performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots. We observed morphological reductions in early stages under field and controlled conditions, with a decrease in root development, an increase in reactive oxygen species content in leaves and changes in lipid composition in hypocotyls. A long-term increase in leaf chlorophyll suggests a stress memory mechanism that was supported by transcriptomic induction of histone coding genes. We highlighted DEGs related to cold acclimation such as chaperone, heat shock and late embryogenesis abundant proteins. We identified genes in hypocotyls involved in lipid, cutin, suberin and phenylalanine ammonia lyase biosynthesis and ROS scavenging. This comprehensive study describes new phenotyping methods and candidate genes to understand phenotypic plasticity better in response to chilling and study stress memory in sunflower.
Why it matches plant phenotyping methods全身部位のハイスループット画像化と新規フェノタイピング手法が明示され、低温応答の形態評価における手法が中心的に記述されている。
abstractWe performed high-throughput imaging of the whole plant parts and obtained physiological and transcriptomic data from leaves, hypocotyls and roots.
Reproduction assets foundThe paper's plant-phenotyping measurements (morphological, chlorophyll/anthocyanin/flavonoid, root traits, yield and seed composition across 12 experiments) were deposited on Recherche Data Gouv under doi:10.57745/4HNS1J, with a specific sub-dataset (persistentId doi:10.57745/4HNS1J.2598) referenced in Table 1. No codeDataset · publicby the French National Association for Research and
Technology (ANRT).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available
in Recherche Data Gouv at https://entrepot.recherche.data.gouv.fr/,
reference number https://doi.org/10.57745/4HNS1J.REFERENCES
Abbass, K., Qasim, M.Z., Song, H., Murshed, M., Mahmood, H. & Younis, I.Open asset ↗Recherche Data Gouvpdf-raw-page:16 lines:1-76Dataset · publicter
dynamics,
chlorophyll
content,
anthocyanin
content,
flavonoid
content,
nitrogen
balance,
fatty
acids
of
seeds
Tables
S2
and
S3
21TE01‐02
22EX01‐02
Early
and
late
Field
2
(n
=
22)
Vigour,
total
leaf
area
and
plant‐height
dynamics,
flowering
date,
yield,
yield
components
Table
S4
Note:
Data
were
submitted
to
the
public
portal
https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/4HNS1J.2598 | LECONTE ET AL.
13653040,
2025,
4,
Downloaded
from
https://onlinelibrary.wiley.com/doi/10.1111/pce.14941
by
Mount
Vernon
Nazarene
University,
Wiley
Online
Library
on
[30/12/2025].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
OnliOpen asset ↗doi:10.57745/4HNS1J.2598pdf-raw-page:3 lines:1-265Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. Results In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Conclustion Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.
Why it matches plant phenotyping methodsリンゴ果肉の赤色形質をRGB画像と色解析で定量する手法を開発し、遺伝子型間の識別に実質的に適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images
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 · publicScript designed for image analysis is public and can be found at: https://github.com/pibouillon/colour_val/blob/main/colour_val.pyOpen asset ↗https://github.com/pibouillon/colour_val · colour_val.pylines:166-220Code / 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 · OpenAlex · checked 15 Sept 2026
Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Why it matches plant phenotyping methods植物の葉縁形態を3D再構築して定量化する手法を開発し、仮想葉と実葉で性能評価・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。
abstractThis study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets and analysis code for this 3D leaf edge reconstruction study in a public GitHub repository (MorphometricsGroup/Murata-2024), and the virtual-leaf simulation inputs (Sketchfab 3D leaf models) are publicly available. Generic libraries (Detectron2, 3Dataset · publicto S4
Data Availability Statement
The datasets used and/or analyzed during the current study are available in the repositories on Zenodo (10.5281/zenodo.10836254, 10.5281/zenodo.10836258, 10.5281/zenodo.10836260, 10.5281/zenodo.10065546, 10.5281/zenodo.10828962, 10.5281/zenodo.10121073, and 10.5281/zenodo.10829007) and GitHub ( https://github.com/MorphometricsGroup/Murata-2024 ).Open asset ↗MorphometricsGroup/Murata-2024lines:311-320Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in Rubisco carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (Vcmax), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.
Why it matches plant phenotyping methods植物の光合成生理形質を測定するCO2応答プロトコルを比較・検証し、測定時間、精度、再現性を評価しているため、方法検証が中心です。
abstractWe compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements
Reproduction assets foundThe paper's primary A/Ci gas-exchange measurement data are openly deposited in Dryad. The msuRACiFit GitHub repository is cited prior work, not this paper's analysis code, and no author analysis code URL is given for this study.Dataset · publicAll primary data to support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.pk0p2ngst ( Tejera-Nieves and Walker, 2024 ).Open asset ↗Dryad · 10.5061/dryad.pk0p2ngstlines:120-161Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100 , TracesCS6B03G0356400 , and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
Why it matches plant phenotyping methodsUAV時系列スペクトル指標を用いてコムギのstay-green（老化動態）を定量化し、RSGS指標と解析パイプラインを開発・適用しており、表現型取得法が研究の中心である。
abstractSpectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's genotype and phenotype data (the RSGS stay-green phenotypes and SNP genotypes for the 565-accession wheat panel) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code or raw UAV imagery depositDataset · publicThe genotype and phenotype data presented in this study are available at the website https://github.com/zengqd/PopulationGenetics/tree/main/Wheat/StayGreen .Open asset ↗zengqd/PopulationGenetics · Wheat/StayGreenlines:298-318Code / 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,
841
particularly in an era in which plant resilience and adaptation are of paramount
842
concern.
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Data Availability
846
The source code, trained model, user installation and training guideline, and all the
847
labeled images of leaf stomata are available at
848
https://github.com/YaoChengLab/StomaVision. The web portal of extracting stomatal
849
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 confirmedEurope PMC · checked 15 Sept 2026
The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023. The UAV flight took place at 30 m from the canopy level with the multispectral camera titled at an angle of 900. The SPAD Chlorophyll Meter was used to collect ground truth data, which is proportional to the nitrogen level of the leaf. There were 50 randomly selected readings throughout the paddy field. Relevant climate data for five days was provided by the Rice Research and Development Institute, Bathalagoda, which belongs to the paddy field. The purpose of this data creation was to aid researchers who are generally interested in disease diagnosis. Moreover, this dataset allows for studying the effect of using different tilt angles on the 3D reconstruction of the paddy fields and the generation of orthomosaics.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSPADによる地上真値を含む、植物キャノピーの状態推定に再利用可能なデータセットであり、画像取得・オルソモザイク生成・3D再構成が中心的な方法的貢献です。
abstractIn this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening)
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own UAV multispectral images, SPAD ground-truth readings, GPS shapefile, and climate data for paddy nitrogen phenotyping. This is a paper-specific, publicly available dataset with an explicit direct URL and DOI.Dataset · publicructions, the flight path was configured to fly on its own (DJI). The dataset includes a shapefile containing the GPS positions of the BG300 rice clusters. The same dates were used to gather SPAD meter values.
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/h8s5mn52j6.1
Direct URL to data: https://data.mendeley.com/datasets/h8s5mn52j6/1
Data source location
Institution: Rice Research and Development Institute
City/Town/Region: Batalagoda, Ibbagamuwa, Kurunegala
Country: Sri Lanaka
Latitude and longitude (and GPS coordinates) for collected samples/data: 7.53240 N, 80.43400E
1.
Value of the Data
•
Data is useful for researchers interested in UAV (unmannedOpen asset ↗Mendeley Data · 10.17632/h8s5mn52j6.1lines:1-80Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy has often been tested on closed green plant canopies, little is known about how nutrient content estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions of bare soil (BS) and non-photosynthetic vegetation (NPV), may significantly and nonlinearly reduce the spectral features relied upon for nutrient content retrieval. We attempted to define the loss of prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR imaging spectroscopy data from the Global Airborne Observatory (GAO) and a large collection of lab-calibrated field samples of nitrogen (N) content collected across numerous crop species grown in several farming regions of the United States. Fractional cover values of PV, NPV, and BS were estimated from the GAO data using the Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors in prediction from a partial least squares N model applied to the spectral data were examined in relation to the fractional cover of the unmixed components. We found that the most important factor in the accuracy of the partial least squares regression (PLSR) model is the fraction of photosynthetic vegetation (PV) cover, with pixels greater than 60% cover performing at the optimal level, where the coefficient of determination (R2) peaks to 0.66 for PV fractions of more than 60% and bare soil (BS) fractions of less than 20%. Our findings guide future spaceborne imaging spectroscopy missions as applied to agricultural cropland N monitoring.
Why it matches plant phenotyping methodsVSWIR画像分光とスペクトル混合分解・PLSRを用いて作物キャノピー窒素含量の推定精度を検証しており、植物形質取得法が研究の中心である。
abstractAccurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy.
Reproduction assets foundThe authors' PLSR nitrogen-retrieval Python code is publicly available on GitHub (NitrogenRetrieval repository) and archived on Zenodo (10.5281/zenodo.7967292). The AutoMCU code, airborne imaging spectroscopy data, and spectral reflectance data are only available by request from the corresponding author, so those are 'Code · publicAdditional
details regarding the algorithm employed for N retrieval and the corresponding Python
code can be found in the NitrogenRetrieval repository on our GitHub page, accessible at
the following URL: https://github.com/CMLandOcean/NitrogenRetrievalOpen asset ↗CMLandOcean/NitrogenRetrievalpdf-page:8 lines:1-56Code / 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
Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO 2 and O 2 , and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO 2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.
Why it matches plant phenotyping methodsRBAモデルを用いて植物細胞の生長率やC:N比などの表現型を定量予測する計算手法の開発が中心であり、単なる生物学的実験ではない。
abstractHere we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana
Reproduction assets foundThe authors publicly release the paper-specific RBA leaf model (XML) and the PlantCellRBA simulation/analysis software on Forgemia, with explicit availability statements in the Data availability and Supplementary material sections. No plant image/sensor/phenotype measurement datasets from this paper are deposited; the Code · publicinterest, such as the seed, in order to define and forecast quality
determinants under diverse environmental conditions. These insights will also be valuable in
fine-tuning plant breeding programs.
Data availability
The RBA leaf model (encoded in XML files) and the PlantCellRBA software for running simulations
are available at https://forgemia.inra.fr/anne.goelzer/rba-plant-cell-model.
Acknowledgements
We thank Wolfram Liebermeister, Ana Bulovic, Sophie Colombié and Jean-Denis Faure for critical
comments on the manuscript and the Métaprogramme Digitbio of INRAE for funding.
Author Contributions
AG and VF conceived the study. AG developed, implemented and simulated the different models
(RBA, Open asset ↗forgemia.inra.fr/anne.goelzer/rba-plant-cell-modelpdf-layout-page:28 lines:1-50Supplement · publicSupplementary Table 1) led to changes in growth rate
greater than 1% (Fig.Open asset ↗pdf-raw-page:20 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Correlative light and electron microscopy (CLEM) is an important tool for the localisation of target molecule(s) and their spatial correlation with the ultrastructural map of subcellular features at the nanometre scale. Adoption of these advanced imaging methods has been limited in plant biology, due to challenges with plant tissue permeability, fluorescence labelling efficiency, indexing of features of interest throughout the complex 3D volume and their re-localization on micrographs of ultrathin cross-sections. Here, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow. Importantly, we demonstrate that the use of a particular type of embedding resin is not only compatible with single-molecule localization microscopy but shows improvements in the fluorophore blinking behavior relative to the whole-mount approaches. Here, we use a commercially available Click-iT ethynyl-deoxyuridine cell proliferation kit to visualize the DNA replication sites of wild-type Arabidopsis thaliana seedlings, as well as fasciata1 and nucleolin1 plants and apply our in-section CLEM imaging workflow for the analysis of S-phase progression and nucleolar organization in mutant plants with aberrant nucleolar phenotypes.
Why it matches plant phenotyping methods植物組織に適用するin-section CLEMおよび超解像イメージングのワークフローを開発・実証しており、植物細胞の構造・複製状態を取得する方法が研究の中心である。
abstractHere, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow.
Reproduction assets foundThe paper's raw TEM and SMLM phenotyping image datasets (Arabidopsis nucleolar ultrastructure and DNA replication CLEM analysis) are publicly deposited in the BioImage Archive under accession S-BIAD700, with an explicit authors' URL. Source data quantification sheets are only provided with the paper, not as a separate址Dataset · publicature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.
Data availability
The raw datasets of TEM imaging (Spurr and Lowicryl) and SMLM data for quantitative analysis have been deposited to the BioImage Archive, under accession code S-BIAD700 ( https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD700 ). Source data are provided with this paper (sheet 1—IRF quantification, sheet 2—FC quantification, sheet 3—DBSCAN analysis). Source data are provided with this paper.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurOpen asset ↗BioImage Archive · S-BIAD700lines:116-150Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO 2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO 2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.
Why it matches plant phenotyping methods低コスト2D画像フェノタイピングを用いて、シュート構造やキャノピー被覆を定量化し、圃場での植物形態評価法としての有用性を扱っているため、フェノタイピング手法が実質的に中心である。
abstractIn addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput 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。Supplement · publicSupplementary Material 4: Table S3: (download XLSX )
Reliability estimates, BLUPs calculated for the traits measured in this study and data associated with each trait measured in the study.Open asset ↗lines:429-492Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Published6 Mar 2024TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 10 · OpenAlex ↗
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
Why it matches plant phenotyping methodsドローン由来マルチスペクトルデータを用いたフェノミック予測をジャガイモ育種に適用し、複数の予測シナリオやゲノム予測との性能比較を行っており、植物形質推定法が中心である。
abstractexplore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding
Reproduction assets foundThe paper's phenotypic and multispectral datasets are not publicly available (company secret, available upon request in encoded form), but the authors' R analysis scripts are explicitly stated to be publicly available on GitHub.Code · publicCode availability
R scripts for data analysis are available on GitHub: https://github.com/AlessioMR/ps_in_potato_breeding .Open asset ↗AlessioMR/ps_in_potato_breedinglines:179-254Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Most plant thermal tolerance studies focus on single critical thresholds, which limit the capacity to generalise across studies and predict heat stress under natural conditions. In animals and microbes, thermal tolerance landscapes describe the more realistic, cumulative effects of temperature. We tested this in plants by measuring the decline in leaf photosynthetic efficiency (F V /F M ) following a combination of temperatures and exposure times and then modelled these physiological indices alongside recorded environmental temperatures. We demonstrate that a general relationship between stressful temperatures and exposure durations can be effectively employed to quantify and compare heat tolerance within and across plant species and over time. Importantly, we show how F V /F M curves translate to plants under natural conditions, suggesting that environmental temperatures often impair photosynthetic function. Our findings provide more robust descriptors of heat tolerance in plants and suggest that heat tolerance in disparate groups of organisms can be studied with a single predictive framework.
Why it matches plant phenotyping methods植物の熱耐性を、温度と曝露時間の累積効果および葉の光合成効率から定量・比較する予測フレームワークが研究の中心であり、植物生理状態のフェノタイピング手法に該当する。
abstractWe demonstrate that a general relationship between stressful temperatures and exposure durations can be effectively employed to quantify and compare heat tolerance within and across plant species and over time.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the authors' R scripts and datasets (including the FV/FM heat-tolerance measurements and analysis data) at the Dryad Digital Repository with a public DOI, making this a paper-specific, publicly actionable asset.Dataset · publicProgram Scholarship; University of
Technology.
PEER REVIEW
The peer review history for this article is available at
https://www.webofscience.com/api/gateway/wos/peer-re-view/10.1111/ele.14416.DATA AVAILABILITY STATEMENT
The R scripts and datasets used to conduct the data
analyses are available at the DRYAD Digital Repository
(https://doi.org/10.5061/dryad.wdbrv15v4).ORCID
Alicia M. Cook https://orcid.org/0000-0003-3594-3220
Enrico L. Rezende https://orcid.org/0000-0002-6245-9605
Katherina Petrou https://orcid.org/0000-0002-2703-0694
Andy Leigh https://orcid.org/0000-0003-3568-2606
REFERENCES
AGBoM. (2018a) Climate statistics for Australian Locations: Port
Augusta AERO. Available at: http:Open asset ↗DRYAD Digital Repository · 10.5061/dryad.wdbrv15v4pdf-raw-page:10 lines:1-102Code / 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
Premise The adaptive significance of amphistomy (stomata on both upper and lower leaf surfaces) 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 amphistomy informs its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy). Humidity modulated stomatal conductance and thus enabled comparing photosynthesis at the same total stomatal conductance. We estimated AA $\text{AA}$ and leaf traits in six coastal (open, sunny) and six montane (closed, shaded) populations of the indigenous Hawaiian species 'ilima (Sida fallax). Results Coastal 'ilima leaves benefit 4.04 times more from amphistomy than montane leaves. Evidence was equivocal with respect to two hypotheses: (1) that coastal leaves benefit more because they are thicker and 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 conductance being through the lower surface. Conclusions This is the first direct experimental evidence that amphistomy increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase CO 2 supply to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained by the increased benefit of amphistomy in "sun" leaves, but the mechanistic basis remains uncertain.
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実験手法を開発し、複数集団で適用しているため、植物の生理形質取得法が中心である。
abstractWe developed a method to quantify "amphistomy advantage" ( AA $\text{AA}$ ) as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper surface (pseudohypostomy).
Reproduction assets foundThe paper's raw phenotyping data (stomatal traits, leaf thickness, gas exchange) are publicly deposited on Dryad, and the authors' custom analysis scripts are on GitHub with a Zenodo archive; both are paper-specific and directly actionable.Dataset · public7341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. Yellow ʻilima (Sida fallax). https://www.inaturalist.org/taxa/54995-Sida-fallax. iNaturalist.
Ball, J. T., I. E. Woodrow, and J. A. Berry. 1987. A model prediOpen asset ↗Dryad · 10.5061/dryad.rxwdbrvfwpdf-raw-page:9 lines:1-93Code · publicfor advice on leaf sectioning. Startup
funds were provided by the University of Hawaiʻi, NSF
Award 1929167 to C.D.M., and T.N.B. received NSF Award
2307341. This is publication #213 from the School of Life
Sciences, University of Hawaiʻi at Mānoa.
DATA AVAILABILITY STATEMENT
Custom scripts are available on a GitHub repository
(https://github.com/cdmuir/stomata-ilima) and archived
on Zenodo: https://doi.org/10.5281/zenodo.10369114 (Muir,
2023). Raw data are deposited on Dryad: https://doi.org/10.5061/dryad.rxwdbrvfw (Triplett et al., 2024).
ORCID
Thomas N. Buckley http://orcid.org/0000-0001-7610-7136
Christopher D. Muir http://orcid.org/0000-0003-2555-3878
REFERENCES
Anonymous. 2022. YellowOpen asset ↗GitHub · cdmuir/stomata-ilimapdf-raw-page:9 lines:1-93Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.
Why it matches plant phenotyping methods携帯型および自動クロロフィル蛍光フェノタイピングによる光合成効率の反復測定と、収量予測への技術適用が研究の中心であるため。
abstractUnder fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping.
Reproduction assets foundThe paper's field MultispeQ chlorophyll fluorescence phenotyping data (Fq'/Fm' with PPFR and environmental covariates for the Dar18B, Dar19B, and Pal19D trials) are publicly available on the PhotosynQ platform via three author-provided project URLs. Glasshouse ChlF/biomass data are only in supplementary files without aDataset · publicThe MultispeQ data are also available on the PhotosynQ data base after creating an account (Darién 2018: https://photosynq.org/projects/climbers-in-darien-2018Open asset ↗PhotosynQ · climbers-in-darien-2018lines:374-422Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO 2 assimilation to variable intercellular CO 2 concentrations (A/C i response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V cmax ) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; J PAR ). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO 2 (g m ) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g m in cotton (Gossypium hirsutum cv. Sicot 71) and derived V cmax and J 2000 (J PAR at 2000 µmol m -2 s -1 PAR) from cotton A/C i curves incrementally measured at 15°C-40°C using cotton and other species-specific sets of input parameters with our new automated fitting R package 'OptiFitACi'. Notably, parameterisation by a set of tobacco parameters produced unrealistic J 2000 :V cmax ratio of cmax above 15°C, up to 2.3-fold higher estimates of J 2000 and more variable estimates of V cmax and J 2000 , for our cotton data compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g m,25 of 2.3 mol m -2 s -1 MPa -1 or less and Rubisco CO 2 -affinities in 21% O 2 (K C 21%O2 ) at 25°C outside the range of 46-63 Pa to model A/C i responses in cotton. We show how the A/C i modelling capabilities of 'OptiFitACi' serves as a robust, user-friendly, and flexible extension of 'plantecophys' by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to reduce variability when modelling V cmax and J 2000 .
Why it matches plant phenotyping methods植物のガス交換から光合成形質を推定する新規Rパッケージを開発し、種特異的パラメータによる推定性能を検証しているため、方法が研究の中心である。
abstractwith our new automated fitting R package 'OptiFitACi'
Reproduction assets foundThe paper's authors publicly released the OptiFitACi R package containing the fitacis4 function used for all A/Ci curve fitting analyses in this study, with an explicit GitHub URL. The phenotype data (A/Ci response measurements) are stated to be in the article's Supporting Information, which is part of the article and,Code · public(Walker et al., 2013). KC
21%O2
and Γ* were calculated as
described above for tobacco. Equation (2) in Walker et al. (2013) was
used to calculate the gm of antirbcS Arabidopsis at each temperature.
2.6 | Design and implementation of function
fitacis4 in R package ‘OptiFitACi’
A new R function fitacis4 (in package ‘OptiFitACi’; https://github.com/jsamthor/OptiFitACi/tree/master/R) was designed to enhance the A/
Ci analysis capabilities of functions fitaci, fitacis, fitacis2 in the packages
‘plantecophys’ (Duursma, 2015) and ‘plantecowrap’. The function
fitacis4 is used for the batch analysis of leaf photosynthetic gas
exchange data to estimate Vcmax and J2000 using the FvCB C3 model of
leaf pOpen asset ↗jsamthor/OptiFitACi · OptiFitACipdf-raw-page:5 lines:1-114Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our correlation results ranged from r 2 = 0.81 for protein in wheat and oats to r 2 = 0.99 for polyphenol content in lettuce in both the Reflectometer and laboratory spectrophotometer assessment, suggesting the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Repeatability evaluation demonstrated good reproducibility of the Reflectometer to assess crop phytochemical content. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物の植物化学成分量を測定する低コスト反射計を、実験室用分光光度計と比較して精度・再現性検証しており、植物形質の取得手法の技術的検証が中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all Bionutrient Institute data (reflectometer/spectrometer phytochemical measurements used in this study) are publicly available in the authors' GitLab repository, and the authors' data-processing pipeline code is also publicly hosted on GitLab.Dataset · publicAll data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset . The data used in this manuscript covers samples submitted up to 7/31/2022.Open asset ↗our-sci/bionutrient-institute/datasetlines:156-212Code · publicAn automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.Open asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:132-143Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent years have seen the development of novel, rapid, and inexpensive techniques for collecting plant data to monitor the nutritional status of crops. These techniques include hyperspectral imaging, which has been widely used in combination with machine learning models to predict element concentrations in plants. When there are multiple elements, the machine learning models are trained with spectral features to predict individual element concentrations; this type of single-target prediction is known as single-target regression. Although this method can achieve reliable accuracy for some elements, there are others that remain less accurate. We aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values. To evaluate the multi-target method, the concentrations of 17 elements in tomato leaves were predicted and compared with the single-target regression results. We trained 5 machine learning models with hyperspectral data and predicted element concentration values and found a significant improvement in the prediction accuracy for 10 elements (Mg, P, S, Mn, Fe, Co, Cu, Sr, Mo, and Cd). Furthermore, our multi-target regression method outperformed single-target predictions by increasing the coefficient of determination ( R 2 ) for elements such as Mn, Cu, Co, Fe, and Mg by 12.5%, 10.3%, 11%, 10%, and 8.4%, respectively. Hence, our multi-target method can improve the accuracy of predicting 10-element concentrations compared to single-target regression.
Why it matches plant phenotyping methodsトマト葉の元素濃度という植物状態を、ハイパースペクトル画像とマルチターゲット回帰で推定する手法を開発・比較評価しており、フェノタイピング手法が中心である。
abstractWe aimed to improve the accuracy of element concentration predictions by using a multi-target regression method that sequentially augmented the original input features (hyperspectral imaging) by chaining the predicted element concentration values.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll codes for data cleaning and analysis associated with the current submission are available at https://github.com/anaguilarar/MT_elements .Open asset ↗anaguilarar/MT_elementslines:428-472Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
Why it matches plant phenotyping methods複数作物の果実・器官について、ハイパースペクトル画像から乾物含量を非破壊推定するデータセットを構築しており、形質取得・推定手法と再利用可能なベンチマークが研究の中心である。
abstractThe dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models.
Reproduction assets foundThe paper is a data descriptor for the SpectroFood hyperspectral dataset; all five Zenodo deposits (meta-dataset plus per-crop hyperspectral image data) are public, paper-specific phenotype/trait datasets with direct URLs in the Specifications Table.Dataset · publicce), Rc: corrected hyperspectral image.
Data source location
Data are stored at Agricultural University of Athens (AUA) premises. Iera Odos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https:/Open asset ↗Zenodo · 10.5281/zenodo.8362947lines:1-65Dataset · publicOdos 75, 11855 Athens, Greece, Department of Horticultural Engineering
Data accessibility
Repository name:Zenodo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302Open asset ↗Zenodo · 10.5281/zenodo.10301753lines:1-65Dataset · publicdo
Table data
Data identification number: 10.5281/zenodo.8362947
Direct URL to data: https://zenodo.org/record/8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
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Spectra were acquired using calibrated hyperspectral imaging systems under the sameOpen asset ↗Zenodo · 10.5281/zenodo.10302438lines:1-65Dataset · public8362947
Hyperspectral image data
1) Data identification number: 10.5281/zenodo.10301753
Direct URL to data: https://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
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Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and acrossOpen asset ↗Zenodo · 10.5281/zenodo.10302426lines:1-65Dataset · publicps://zenodo.org/records/10301753
2) Data identification number: 10.5281/zenodo.10302438
Direct URL to data: https://zenodo.org/records/10302438
3) Data identification number: 10.5281/zenodo.10302426
Direct URL to data: https://zenodo.org/records/10302426
4) Data identification number: 10.5281/zenodo.10302386
Direct URL to data: https://zenodo.org/records/10302386
1.
Value of the Data
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Spectra were acquired using calibrated hyperspectral imaging systems under the same controlled conditions for four crops with high variation of dry matter values amongst the same crop and across all four.
•
The dry matter content of the four crops is the common variable when considering the quality of theOpen asset ↗Zenodo · 10.5281/zenodo.10302386lines:1-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Globally, wheat ( Triticum aestivum L.) is a major source of proteins in human nutrition despite its unbalanced amino acid composition. The low lysine content in the protein fraction of wheat can lead to protein-energy-malnutrition prominently in developing countries. A promising strategy to overcome this problem is to breed varieties which combine high protein content with high lysine content. Nevertheless, this requires the incorporation of yet undefined donor genotypes into pre-breeding programs. Genebank collections are suspected to harbor the needed genetic diversity. In the 1970s, a large-scale screening of protein traits was conducted for the wheat genebank collection in Gatersleben; however, this data has been poorly mined so far. In the present study, a large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means. Four genomic prediction approaches were compared based on the ability to accurately predict the traits of interest. High-quality phenotypic data of 558 accessions was leveraged by engaging the best performing prediction model, namely EG-BLUP. Finally, this publication incorporates predicted phenotypes of 7,651 accessions of the winter wheat collection. Five accessions were proposed as donor genotypes due to the combination of outstanding high protein content as well as lysine content. Further investigation of the passport data suggested an association of the adjusted lysine content with the elevation of the collecting site. This publicly available information can facilitate future pre-breeding activities.
Why it matches plant phenotyping methods小麦のタンパク質・リジン含量という植物形質について、歴史的表現型データを整理し、複数のゲノム予測法を比較して大規模コレクションの予測表現型を生成しており、計算的な形質推定とデータセット活用が研究の中心です。
abstracta large historical dataset on protein content and lysine content of 4,971 accessions was curated, strictly corrected for outliers as well as for unreplicated data and consolidated as the corresponding adjusted entry means.
Reproduction assets foundThe authors deposited the paper's curated historical protein/lysine phenotype data (ISA-Tab), the R code for BLUE calculation and genomic prediction with all input files, and key output files (BLUEs and predicted phenotypes) in the public e!DAL repository under DOI 10.5447/ipk/2023/20. This is a paper-specific, public,Dataset · publicn with all input files, and the most important output files of the analysis. The output files include BLUEs of protein and lysine content as well as the predictions of protein content, lysine content and adjusted lysine content. The aforementioned information is available via the e!DAL ( Arend et al., 2014 ) online repository ( https://dx.doi.org/10.5447/ipk/2023/20 ).
Author contributions
MB: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft. SW: Data curation, Writing – review & editing. JR: Conceptualization, Methodology, Supervision, Writing – review & editing. AS: Conceptualization, Methodology, Supervision, Validation, WOpen asset ↗e!DAL · 10.5447/ipk/2023/20lines:302-323Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Abstract. Senescence is a highly ordered biological process involving resource redistribution away from ageing tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned/unoccupied/uncrewed aerial system/vehicle (UAS, UAV and drone) flights captured the senescence window across two experiments while functional principal component analysis effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: the red chromatic coordinate (RCC) index and the transformed normalized difference green and red (TNDGR) index. Convolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis. The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1–M6). Model performance was strongest for FPC1 scores from VSRs (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to-moderate for RCC index (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis. This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methods単一個体の綿花について、時系列UAS画像とCNN・機能的データ解析を用いて老化表現型を推定する方法が研究の中心であり、性能評価も実施している。
abstractConvolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis.
Reproduction assets foundThe paper's DATA AVAILABILITY section states that all code for FPCA, ANOVA and CNN regression, plus all files needed to run the scripts including the raw single-plant UAS images, are publicly available in the authors' GitHub repository. This is a paper-specific, public, actionable asset covering both the phenotyping (cCode · public)
graduate program. OGR and SMD were partially supported
by Cotton Incorporated Awards 18-201 and 20-724, and NSF
Award 1739092.
DATA AVAILABILITY
All of the code used for FPCA, ANOVA and CNN regres-
sion is available at the GitHub repository [see Supporting
Information—Notes S1] associated with this manuscript
(DeSalvio 2024): https://github.com/ajdesalvio/cotton-sand-wiches. All files necessary to run the scripts, including the raw
images, are available in the repository.
NSF STATEMENT
Any opinion, findings and conclusions or recommendations
expressed in this material are those of the authors(s) and do not
necessarily reflect the views of the National Science Foundation.
REFERENCES
Adak A,Open asset ↗ajdesalvio/cotton-sand-wichespdf-raw-page:15 lines:1-93Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract The potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated. Various polarimetric parameters along with coherent and incoherent decomposition techniques were assessed for its sensitivity toward crop parameters like Wet and Dry Biomass, Crop Height, Leaf Area Index and Vegetation Water Content. A set of 39 polarimetric observables extracted from the Quad‐Pol data were used for regression analysis. In this study two Machine Learning techniques Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were assessed for the prediction of Wet Biomass (gm −2 ) and Height (cm). The most significant (6 out of 39) variables were applied for prediction. The results revealed that RFR algorithm performed better than MLR. The coefficient of determination ( R 2 ) and root‐mean‐square‐error of estimating wet biomass and height were 0.646, 655.65 (gm −2 ) and 0.71, 14.5 (cm) respectively in RFR and 0.566, 683.86 (gm −2 ) and 0.65, 16.14 (cm) respectively in MLR. Thus this study explored the effective application of quad‐pol data for assessing sensitivity and accurate retrieval of parameters using optimum PolSAR observables.
Why it matches plant phenotyping methodsPolSARセンサーデータと機械学習により、作物のバイオマスおよび草丈を推定し、回帰手法の性能を比較・評価しているため、表現型取得手法が中心である。
abstractThe potential of single date fully Polarimetric RADARSAT‐2 data in retrieving crop biophysical parameters using Machine Learning techniques was investigated.
Reproduction assets foundThe paper's Data Availability Statement deposits two paper-specific public assets: the ground-truth field campaign dataset (GT Points, Zenodo 10.5281/zenodo.10403380) and the authors' RFR/MLR prediction code (Zenodo 10.5281/zenodo.10403352). Generic ESA software (PolSARpro, SNAP) is excluded as a general library.Dataset · publicEarth and Space Science
THULASIRAMAN ET AL.
10.1029/2022EA002799
17 of 20
Appendix A: Supplementary Data
Supplementary data to this article, ground truth points collected during 12 August 2019, campaign is provided in
.xlsx format (https://doi.org/10.5281/zenodo.10403380).Appendix B: RFR and MLR Algorithm
The algorithm applied for Random Forest and MLR is displayed below in Figure B1.
Data Availability Statement
The fully polarimetric RADARSAT-2 data was purchased from MDA corporation. The field data collected
during the study can be accessed from Supporting Information section (Appendix A) (htOpen asset ↗Zenodo · 10.5281/zenodo.10403380pdf-raw-page:17 lines:1-77Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
CucumberPepper / chilliTomatoThermalLeafPhysiological trait estimationPlant / canopy temperature
Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.
Why it matches plant phenotyping methods熱画像と葉エネルギーバランスモデルにより、植物キャノピー内の吸収光量を迅速に推定・可視化するフェノタイピング手法を開発し、点測定で検証しているため。
abstractWe developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature.
Reproduction assets foundThe paper's authors explicitly state that all analysis code (R/STAN energy-balance fitting and Julia absorbed-irradiance mapping) is publicly available on their GitHub repository, which directly reproduces the paper's phenotyping computations. No separate phenotype dataset or image deposit is stated in the supplied.Code · publicData analysis
Calculations to solve Eqns 2 and 3 were run in R (R project,
v.4.2.0). The absorbed irradiance map was calculated in JULIA
(v.1.40.1; https://julialang.org/). All codes are available on
GitHub (https://github.com/jiayu0903/leaf-absorbed-irradiance.git). Statistical analysis was performed using a Student’s t-test for
paired samples to determine significant differences (P < 0.05)
between means.
Results
The temperature of sweet pepper (Fig. 2a), cucumber (Fig. S6a),
and tomato leaves (Fig. S7) showed a near-linear increase when
exposed to a brief 30 s period of irradiance fromOpen asset ↗https://github.com/jiayu0903/leaf-absorbed-irradiance.gitpdf-raw-page:7 lines:1-87Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Laboratory / benchtopCell / cellular structurePhysiological trait estimationVisualization / data managementGrowth / development / phenology
Abstract Background Plant defense activators offer advantages over pesticides by avoiding the emergence of drug-resistant pathogens. However, only a limited number of compounds have been reported. Reactive oxygen species (ROS) act as not only antimicrobial agents but also signaling molecules that trigger immune responses. They also affect various cellular processes, highlighting the potential ROS modulators as plant defense activators. Establishing a high-throughput screening system for ROS modulators holds great promise for identifying lead chemical compounds with novel modes of action (MoAs). Results We established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables. Our screening strategy comprised four phases: (1) development of a ROS inference system based on a deep neural network that combines ROS production data in plant cells and multidimensional chemical features of chemical compounds; (2) in silico extensive-scale screening of seven million commercially available compounds using the ROS inference model; (3) secondary screening by visualization of the chemical space of compounds using the generative topographic mapping; and (4) confirmation and validation of the identified compounds as potential ROS modulators within plant cells. We further characterized the effects of selected chemical compounds on plant cells using molecular biology methods, including pathogenic signal-triggered enzymatic ROS induction and programmed cell death as immune responses. Our results indicate that deep learning-based screening systems can rapidly and effectively identify potential immune signal-inducible ROS modulators with distinct chemical characteristics compared with the actual ROS measurement system in plant cells. Conclusions We developed a model system capable of inferring a diverse range of ROS activity control agents that activate immune responses through the assimilation of chemical features of candidate pesticide compounds. By employing this system in the prescreening phase of actual ROS measurement in plant cells, we anticipate enhanced efficiency and reduced pesticide discovery costs. The in-silico screening methods for identifying plant ROS modulators hold the potential to facilitate the development of diverse plant defense activators with novel MoAs.
Why it matches plant phenotyping methods植物細胞のROS蓄積という生理状態を推定する深層学習モデルと、実測による検証を組み合わせたスクリーニング手法の開発が中心である。
abstractWe established a novel in silico screening system for plant defense activators using deep learning-based predictions of ROS accumulation combined with the chemical properties of the compounds as explanatory variables.
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 learning algorithm codes used during the current study are available on the GitHub address ( https://github.com/ma1206ko/in_silico_screening ).Open asset ↗ma1206ko/in_silico_screeninglines:168-249Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
ABSTRACT Background The prediction of desirable traits in wheat from imaging data is an area of growing interest thanks to the increasing accessibility of remote sensing technology. However, as the amount of data generated continues to grow, it is important that the most appropriate models are used to make sense of this information. Here, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models. Results Neural networks had greater accuracies than partial least squares regression models and gaussian naïve Bayes models for prediction of grain asparagine content, yield, genotype, and fertiliser treatment. Genotype was also more accurately predicted from seed data than from canopy data. Conclusion Using wheat canopy spectral data and combinations of wheat seed morphology and spectral data, neural networks can provide improved accuracies over other models for the prediction of agronomically important traits.
Why it matches plant phenotyping methods画像・スペクトルデータから穀粒成分や収量などの植物形質を予測するニューラルネットワークを他手法と比較評価しており、形質推定法の性能検証が中心である。
abstractHere, the performance of neural network models in predicting grain asparagine content is assessed against the performance of other models.
Reproduction assets foundThe preprint states that the data and code used in this study (neural network/PLSR/GNB modelling of wheat canopy spectral and seed imaging data) are publicly available in the author's GitHub repository, which matches an allowed URL.Code · publicData and code used in this study are available at: https://github.com/JosephOddy/wheat-Open asset ↗JosephOddy/wheat-pdf-page:7 lines:1-50Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Why it matches plant phenotyping methodsカッサバ根の乾物含量とアミロース含量という植物器官形質を対象に、NIRS予測モデルを開発・検証しており、形質取得法が研究の中心です。
abstractnear-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC
Reproduction assets foundThe paper's field experiment phenotyping data (cassava clones used for NIRS calibration of dry matter and amylose content) is openly available in Cassavabase at the trial 4384 URL, per the authors' explicit availability statements. No author analysis code, NIRS spectra files, or trained model/calibration equations are指Dataset · publicexperiment is available in an open access data repository at
(https://www.cassavabase.org/breeders/trial/4384?format=). The
pre-breeding set of germplasms used in the present study con-
tained genotypes that are from diverse backgrounds (from Inter-
national Institute of Tropical Agriculture (IITA), International
Center for Tropical Agriculture (CIAT) and NaCRRI), for which
diversity is important in development of NIRS calibrations. They
are cOpen asset ↗Cassavabase · trial/4384pdf-raw-page:4 lines:1-91Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS: The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION: Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
Why it matches plant phenotyping methods屋内自動植物フェノタイピング環境で、熱画像・ハイパースペクトル画像による干ばつストレス、水利用、蒸散速度の推定手法を評価・モデル比較しており、フェノタイピング手法が中心である。
abstractthe transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated
Reproduction assets foundThe article's Availability of data and materials statement deposits the datasets generated and analyzed in this study (thermal/hyperspectral phenotyping data and analyses) in three Zenodo repositories with public DOIs. These are paper-specific, publicly accessible assets. No author analysis code with an explicit publicDataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.7807989lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8164473lines:198-347Dataset · publicThe datasets generated and analyzed during the current study are available in the zenodo repository ( https://doi.org/10.5281/zenodo.7807989 , https://doi.org/10.5281/zenodo.8164473 , https://doi.org/10.5281/zenodo.8033640 ).Open asset ↗zenodo · 10.5281/zenodo.8033640lines:198-347Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
LeafPhysiological trait estimationPhotosynthesis / fluorescencePlant / canopy temperature
We present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods. The FAsTeR method subjects plant leaves to a linearly increasing temperature ramp while taking rapid, nonequilibrium measurements of gas exchange variables. Two postprocessing steps are employed to correct measured assimilation rates for nonequilibrium effects and sensor calibration drift. Results obtained with the new method are compared with those from two conventional stepwise methods. Our new method accurately reproduces results obtained from conventional methods, reduces measurement time by a factor of c. 3.3 (from c. 90 to 27 min), and increases data density by a factor of c. 55 (from c. 10 to c. 550 observations). Simulation results demonstrate that increased data density substantially improves confidence in parameter estimates and drastically reduces the influence of noise. By improving measurement speed and data density, the FAsTeR method enables users to ask fundamentally new kinds of ecological and physiological questions, expediting data collection in short-field campaigns, and improving the representativeness of data across species in the literature.
Why it matches plant phenotyping methods葉レベル光合成の温度応答を高速・高密度に測定する新手法を開発し、従来法と比較検証しているため、植物フェノタイピング手法が中心です。
abstractWe present the Fast Assimilation-Temperature Response (FAsTeR) method, a new method for measuring plant assimilation-temperature (AT) response that reduces measurement time and increases data density compared with conventional methods.
Reproduction assets foundThe paper's Data availability statement explicitly provides full data and R code (postmeasurement corrections, analyses, figures, and FAsTeR protocol) at the authors' public GitHub repository.Code · publicSTM. JCG wrote the first
draft of the manuscript, and JCG and STM revised the manu-
script.
ORCID
Josef C. Garen https://orcid.org/0000-0002-3338-6662
Sean T. Michaletz https://orcid.org/0000-0003-2158-6525
Data availability
Full data and code used for the production of figures and statis-
tics in this article are available at https://github.com/garenj/Faster-method.New Phytologist (2024) 241: 1361–1372
www.newphytologist.com
Ó 2023 The Authors
New Phytologist Ó 2023 New Phytologist Foundation
Research Methods
New
Phytologist
1370
14698137,
2024,
3,
Downloaded
from
https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.19405
by
Mount
Vernon
Nazarene
University,
Wiley
Online
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[20/12/Open asset ↗garenj/Faster-methodpdf-raw-page:10 lines:89-144Code / 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 · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / 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 15 Sept 2026
Diets consisting of greater quantity/diversity of phytochemicals are correlated with reduced risk of disease. This understanding guides policy development increasing awareness of the importance of consuming fruits, grains, and vegetables. Enacted policies presume uniform concentrations of phytochemicals across crop varieties regardless of production/harvesting methods. A growing body of research suggests that concentrations of phytochemicals can fluctuate within crop varieties. Improved awareness of how cropping practices influence phytochemical concentrations are required, guiding policy development improving human health. Reliable, inexpensive laboratory equipment represents one of several barriers limiting further study of the complex interactions influencing crop phytochemical accumulation. Addressing this limitation our study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer. Our results suggest the Reflectometer provides an accurate accounting of phytochemical content within evaluated crops. Additionally, we confirmed large variation in phytochemical content within specific crop varieties, suggesting that cultivar is but one of multiple drivers of phytochemical accumulation. Our findings indicate dramatic nutrient variations could exist across the food supply, a point whose implications are not well understood. Future studies should investigate the interactions between crop phytochemical accumulation and farm management practices that influence specific soil characteristics.
Why it matches plant phenotyping methods作物中の植物化学物質量を測定する低コスト反射計を商用分光光度計と比較検証しており、植物の化学的形質取得が研究の中心である。
abstractour study validated the capacity of a low-cost Reflectometer ($500) to measure phytochemical content in selected crops, against a commercial grade laboratory spectrophotometer.
Reproduction assets foundThe paper explicitly states that all data derived from the Bionutrient Institute methods (the crop phytochemical measurements underlying this study) are publicly available in the authors' GitLab repository, and that the automated data pipeline scripts used to calculate, merge, and QC the sample data are also publicly可用Dataset · publicd redefined extraction protocols utilized for crop phytochemical assessment, developed protocols, and provided technical oversight for the usage of reflectometer and offered editorial review of the manuscript.
3 Data Availability
All data derived from the Bionutrient Institute methods are available publicly from our repository: https://gitlab.com/our-sci/bionutrient-institute/dataset. The data used in this manuscript covers samples submitted up to 7/31/2022 . Our Sci seeks to increase transparency and access to research via open-source hardware and software and open-access data [19].
4 Competing Interests Statement
Author G. Austic and D. Ter Avest are co-founders of OurSci, LLC, the companyOpen asset ↗our-sci/bionutrient-institute/datasetlines:108-124Code · publice created to guide users through each aspect of the protocol, including Reflectometer measurements, instructions, and questions for entering metadata (ex: crop type, amount of extractant used, etc). An automated data pipeline was built using SurveyStacks API’s to merge data from each completed survey and mongoDB scripts ( https://gitlab.com/our-sci/real-food-campaign/lab-data-review-dashboard/-/tree/main ) calculated measurement outcomes.
4.2 Crop Phytochemical Variability Study
4.2.1 Crop Sample characteristics
Crop samples were submitted from both producer and consumer volunteers from 2019–2022 representing 10,000 unique samples with accompanying geographical and management datOpen asset ↗our-sci/real-food-campaign/lab-data-review-dashboardlines:86-97Code · publiccation was built using NodeJS/express with mongoDB on the Server and Vue with Vuetify on the Client. Hardware integration between SurveyStack and the Reflectometer occurs via the SurveyStack Kit mobile application written in Kotlin for Android devices. The source code for all applications is available and documented on Gitlab ( https://gitlab.com/our-sci/software/surveystack ). The Android application is available for download over the Google Play store ( https://play.google.com/store ). SurveyStack and SurveyStack Kit are licensed under the GNU General Public License v3.0.
All data collection was completed using SurveyStack forms. For each data collection activity, forms were created to guiOpen asset ↗our-sci/software/surveystacklines:86-97Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
Why it matches plant phenotyping methods葉色画像から個体を自動検出し、深層学習で葉色およびアントシアニン含量を推定する撮像・解析手法を開発し、大規模フィールドデータで適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically.
Reproduction assets foundThe paper deposits its field time-series plant images and annotation/labeling data in two Dryad repositories, and the PlantServation demo set (scripts plus demo data) on Zenodo. All are paper-specific, public, and actionable.Dataset · publicThe time-series image data for the Swiss site generated in this study have been deposited in a Dryad repository [ https://doi.org/10.5061/dryad.1g1jwsv11 ] 93 .Open asset ↗Dryad · 10.5061/dryad.1g1jwsv11lines:200-258Dataset · publicThe time-series image data for Japanese site generated in this study as well as the labeling data for image analysis used in this study are available in a Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Dryad · 10.5061/dryad.h70rxwdnklines:200-258Code · publicThe PlantServation demo set (ca. 600 MB) including scripts and demo data for PlantServation software is available at Zenodo [ https://zenodo.org/record/7321725 ] 95 accessible via Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Zenodo · 7321725lines:200-258Code / 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 confirmedbioRxiv · checked 14 Sept 2026
O_LIAccurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data is limited. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. C_LIO_LIWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine. Feature importance was quantified by AutoGluon and SHAP value. The final model was evaluated and compared with previous models for its performance under different climate conditions. C_LIO_LIThe final model achieved an overall 1.36 {degrees}C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model might have adequately extracted some biological mechanisms during training. C_LIO_LIThe final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-2023 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time. C_LI
Why it matches plant phenotyping methodsブドウの凍結耐性という植物状態を大規模・リアルタイムに推定する自動機械学習モデルを開発し、既存モデルとの性能比較と実運用展開まで行っており、表現型推定手法が中心です。
abstractWe combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automatic machine learning engine.
Reproduction assets foundThe authors publicly released the original LT50 training data and the source code for feature extraction, model training, and deployment in a GitHub repository explicitly stated in the Data availability section. The ACIS URL is a generic external climate data service, not a paper-specific asset.Code · publiclly yielding with a more generalizable model to help understand the biology of grapevine
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freezing tolerance and quantify the threat of freezing under climate change.
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5. Data availability
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All the original training data and source code for feature extraction, modeling training and model
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deployment are available at https://github.com/imbaterry11/NYUS.2
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6. Acknowledgements
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The authors would like to thank Lynn Mills (WA), Beth Ann Workmaster (WI), Katherine
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Benedict (NS), Alexander Campbell and Jessee Tinslay (QC), Don Smith and Meredith Persico
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(PA) and Hanna Martins, Felex Pike, and Bill Wilsey (NY) for their help in LT50 data collection.
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This work was parOpen asset ↗https://github.com/imbaterry11/NYUS.2 · NYUS.2pdf-raw-page:25 lines:1-64Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across– and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low G×E. A genome-wide association study (GWAS) implicated known (i.e., MADS69 , ZCN8, sh1, wx1, du1 ) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key message Genomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.
Why it matches plant phenotyping methodsNIRSを用いた植物試料の表現型推定と、ゲノム予測との比較検証が研究の中心であり、複数のトウモロコシ農業形質・種子組成形質を対象としているため。
abstractOne phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition.
Reproduction assets foundThe paper's Data Availability section and Methods explicitly state that the annotated R analysis script, plus the data files (CSVs.zip, SNP60000.hmp.zip) needed to reproduce the prediction results, are publicly available in the authors' GitHub repository ajdesalvio/Maize-NIRS-GBS.Code · public52
1038
Data Availability
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An annotated script of the R code used in this research can be accessed via GitHub
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(https://github.com/ajdesalvio/Maize-NIRS-GBS.git). Supplementary Data 1
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(Supplementary_Data_1.xlsx) contains prediction results, GWAS results, and variable importance
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scores for NIRS bands. Files necessary to run the R script and reproduce the prediction results are
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available in the CSVs.zip folder and the SNP60000.hmp.zip folder. Supplementary figures are
1044Open asset ↗ajdesalvio/Maize-NIRS-GBSpdf-raw-page:52 lines:1-26Code / 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.
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Availability of data and materials
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The datasets generated and analyzed during the current study are available in the zenodo repository
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(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
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https://doi.org/10.5281/zenodo.8033640)
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Competing interests
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The authors declare that this study received funding from BASF. The funder had the following
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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
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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.
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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)
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Competing interests
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The authors declare that this study received funding from BASF. The funder had the following
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involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
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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
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Not applicable.
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Consent for publication
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Not applicable.
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Availability of data and materials
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The datasets generated and analyzed during the current study are available in the zenodo repository
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(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
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https://doi.org/10.5281/zenodo.8033640)
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Competing interests
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The authors declare that this study received funding from BASF. The funder had the following
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involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
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and W.B. were employed by BASF Corporation, USA.
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This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate ( A c ) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in A c between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in A c (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with A c and plant architecture traits were non-linearly related to A c . This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Why it matches plant phenotyping methods64台カメラのマルチビュー・ステレオ計測によるフェノミクスデータと、キャノピー光合成モデル・因子分析を統合した新規パイプラインの開発が中心であり、植物形態形質とキャノピー光合成の関係を定量化している。
titleDevelopment of a Novel 3D Canopy Modeling Pipeline
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' 3D canopy modeling pipeline source code and the FastTracer ray tracing software used for the canopy photosynthesis simulations, both on public GitHub repositories. No phenotype/image datasets are explicitly deposited.Code · publicThe source code used in this study is available for non-commercial use and the code can be downloaded from https://github.com/PlantSystemsBiology/3DCanopyModelOpen asset ↗PlantSystemsBiology/3DCanopyModellines:224-402Code · publicThe FastTracer software is available from https://github.com/PlantSystemsBiology/fastTracerPublicOpen asset ↗PlantSystemsBiology/fastTracerPubliclines:224-402Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In vivo microscopy of plants with high-frequency imaging allows observation and characterization of the dynamic responses of plants to stimuli. It provides access to responses that could not be observed by imaging at a given time point. Such methods are particularly suitable for the observation of fast cellular events such as membrane potential changes. Classical measurement of membrane potential by probe impaling gives quantitative and precise measurements. However, it is invasive, requires specialized equipment, and only allows measurement of one cell at a time. To circumvent some of these limitations, we developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3). In this protocol, we describe how to prepare experiments for agar media and microfluidics, and we detail the image analysis. We take an example of the rapid plasma membrane depolarization induced by the phytohormone auxin to illustrate the method. Relative membrane potential measurements using DISBAC 2 (3) fluorescence increase the spatio-temporal resolution of the measurements and are non-invasive and suitable for live imaging of growing roots. Studying membrane potential with a more flexible method allows to efficiently combine mature electrophysiology literature and new molecular knowledge to achieve a better understanding of plant behaviors. Key features Non-invasive method to relatively quantify membrane potential in plant roots. Method suitable for imaging seedlings root in agar or liquid medium. Straightforward quantification.
Why it matches plant phenotyping methods植物根の膜電位を蛍光画像から定量する非侵襲的手法の開発と画像解析プロトコルが中心であり、植物表現型計測法に該当する。
abstractwe developed a method to relatively quantify membrane potential variations in Arabidopsis thaliana roots using the fluorescence of the voltage reporter DISBAC 2 (3).
Reproduction assets foundThe protocol explicitly states that the raw imaging data re-analyzed in the paper are deposited on Zenodo and that all analysis scripts (R and Python) are available in a public SourceForge repository. Both are paper-specific, public, and actionable.Dataset · publicluorescence only in the root transition zone. Moreover, we focus on the interface between the cortex and the epidermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
QuantOpen asset ↗Zenodo · 4922659lines:168-214Code · publicermis, as the dead lateral root cap cells were strongly fluorescent (open membranes for the dye to react to). The data presented here are re-analyzed images from Serre et al. (2021). Raw data can be found on Zenodo ( https://zenodo.org/record/4922659 ). All the scripts used in this protocol can be found on the public repository https://sourceforge.net/projects/disbac2-3-data-analysis/ .
Here, we describe a method to:
Quantify DISBAC 2 (3) fluorescence in the transition zone at a given point (agar experiment) or over time (microfluidics) using the ImageJ/Fiji software.
Quantify root elongation either as an average growth (agar experiment) or over time (microfluidics).
Normalize the microfluidOpen asset ↗SourceForge · disbac2-3-data-analysislines:168-214Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.
Why it matches plant phenotyping methodsトマト果皮画像の色からリコペン含量を非破壊推定する低コストセンサーと予測モデルの開発が中心であり、植物器官の形質測定法に該当する。
abstractthis work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp.
Reproduction assets foundThe paper's Data Availability Statement links to a public Google Drive folder containing the data supporting the reported lycopene measurement results (tomato RGB/L*a*b* image-derived measurements and HPLC-calibrated model data). No separate code deposit is described; the models were built in MATLAB toolboxes without aDataset · publicl analysis, M.-G.B.-S.; investigation, J.-A.P.-M.; writing—original draft preparation, J.P.-O. and M.-J.V.-A.; writing—review and editing, A.-I.B.-G.; supervision, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data supporting reported results can be found at: https://drive.google.com/drive/folders/1d1Q_RtEWmo2lbpipMCNG4x53s09-pB-C?usp=sharing .
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Listed below are the 18 inference rules and weights for each of the two fuzzy systems red, green, and blue:
If (L is Low_L) and (a is Low_a) and (b is Low_b) then (Lycopene is Lycopenemf1)
If (L is Low_L) Open asset ↗lines:75-128Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致を検証しているため、植物フェノタイピング手法が中心である。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Crop yield is determined by potential harvest organ size, source organ photosynthesis and carbohydrate partitioning. Filling the harvest organ efficiently remains a challenge. Here, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics. The model reproduces the rice yield formation process under different environmental and genetic perturbations. In silico screening identified a range of post-anthesis targets—both established and novel—that can be manipulated to enhance rice yield. Remarkably, we pinpointed the stability of grain-filling rate from flowering to harvest as a critical factor for maximizing grain yield. This finding was further validated in two independent super-high-yielding rice cultivars, each yielding approximately 21 t ha−1 of rough rice at 14% moisture content. Furthermore, we revealed that stabilizing the grain-filling rate could lead to a potential yield increase of 30–40% in an elite rice cultivar. Notably, the instantaneous grain-filling rates around 15- and 38-day post-flowering significantly influence grain yield; and we introduced an innovative in situ approach using ear respiratory rates for precise quantification of these rates. We finally derived an equation to predict the maximum dried brown rice yield (Y, t ha−1) of a cultivar based on its potential gross photosynthetic accumulation from flowering to harvest (Apc, t CO2 ha−1): Y = 0.74 × Apc + 1.9. Overall, this work establishes a framework for quantitatively dissecting crop physiology and designing high-yielding ideotypes.
Why it matches plant phenotyping methods全植物の炭素・窒素動態と収量形成を推定する速度論モデルを開発し、耳の呼吸速度による粒充填速度の定量化手法も導入しているため、表現型取得・推定が中心的です。
abstractHere, we developed a kinetic model of rice grain filling, which scales from the primary biochemical and biophysical processes of photosynthesis to whole-plant carbon and nitrogen dynamics.
Reproduction assets foundThe paper's authors publicly released the WACNI model source code (the computational framework used for all simulations and analysis) on GitHub, with explicit availability language in the MODEL AND DATA AVAILABILITY section. Supplementary Data 2 contains literature-extracted experimental data but no separate public URLCode · publicip
help improve model parameterization.
cr
MODEL AND DATA AVAILABILITY
us
an
Experimental data extracted from literature, used in model-data comparison, are tabulated in
Supplementary Data 2.
M
The source code used for this study, along with the operational commands and user guide, is
freely available for non-commercial use at https://github.com/rootchang/WACNI-rice.git.
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28Open asset ↗rootchang/WACNI-ricepdf-layout-page:28 lines:1-44Code / 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 confirmedEurope PMC · checked 7 Sept 2026
NADP(H) is a central metabolic hub providing reducing equivalents to multiple biosynthetic, regulatory and antioxidative pathways in all living organisms. While biosensors are available to determine NADP + or NADPH levels in vivo, no probe exists to estimate the NADP(H) redox status, a determinant of the cell energy availability. We describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) . NERNST consists of a redox-sensitive green fluorescent protein (roGFP2) fused to an NADPH-thioredoxin reductase C module which selectively monitors NADP(H) redox states via oxido-reduction of the roGFP2 moiety. NERNST is functional in bacterial, plant and animal cells, and organelles such as chloroplasts and mitochondria. Using NERNST, we monitor NADP(H) dynamics during bacterial growth, environmental stresses in plants, metabolic challenges to mammalian cells, and wounding in zebrafish. NERNST estimates the NADP(H) redox poise in living organisms, with various potential applications in biochemical, biotechnological and biomedical research.
Why it matches plant phenotyping methods植物を含む生体内でNADP(H)酸化還元状態を推定する遺伝子 encoded センサーを設計・特性評価しており、植物の生理状態を測定する方法開発が中心である。
abstractWe describe herein the design and characterization of a genetically-encoded ratiometric biosensor, termed NERNST, able to interact with NADP(H) and estimate E NADP(H) .
Reproduction assets foundThe paper's authors publicly deposited their image-analysis pipelines (ImageJ macros, CellProfiler/KNIME workflows, and R scripts) used to quantify NERNST biosensor fluorescence measurements, in a GitHub repository, and also cite a Zenodo archive of the same materials.Code · publicAnalysis pipelines for both software packages and R scripts are available in the GitHub repository found at https://github.com/PameeMolinari/NCOMMS-22-05375-T 94 .Open asset ↗PameeMolinari/NCOMMS-22-05375-Tlines:206-213Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.
Why it matches plant phenotyping methods紫キャベツのアントシアニン含量という植物形質を、NIR分光とケモメトリクスで迅速推定する方法を開発・検証しており、表現型取得法が研究の中心である。
abstracta fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR).
Reproduction assets foundThe paper's supplementary material (Table S1) contains the paper-specific HPLC-measured anthocyanidin contents for the 106 purple Chinese cabbage samples used to build the NIR prediction models, and is publicly downloadable from MDPI. No author analysis code, spectral files, or trained model files are explicitly sharedSupplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods12091922/s1 , Table S1: Contents of anthocyanidins in purple Chinese cabbage analyzed by high-performance liquid chromatography (mg/kg).
Click here for additional data file.
Author Contributions
Conceptualization, D.-S.Z. and H.-J.H.; software, G.-M.L.; validation, Y.-Q.W. and L.-P.H.; formal analysis, G.-M.L. and X.-Z.Z.; invOpen asset ↗lines:56-122Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Many studies proposed the use of stable carbon isotope ratio (δ 13 C) as a predictor of abiotic stresses in plants, considering only drought and nitrogen deficiency without further investigating the impact of other nutrient deficiencies, that is, phosphorus (P) and/or iron (Fe) deficiencies. To fill this knowledge gap, we assessed the δ 13 C of barley ( Hordeum vulgare L.), cucumber ( Cucumis sativus L.), maize ( Zea mays L.), and tomato ( Solanum lycopersicon L.) plants suffering from P, Fe, and combined P/Fe deficiencies during a two-week period using an isotope-ratio mass spectrometer. Simultaneously, plant physiological status was monitored with an infra-red gas analyzer. Results show clear contrasting time-, treatment-, species-, and tissue-specific variations. Furthermore, physiological parameters showed limited correlation with δ 13 C shifts, highlighting that the plants' δ 13 C, does not depend solely on photosynthetic carbon isotope fractionation/discrimination (Δ). Hence, the use of δ 13 C as a predictor is highly discouraged due to its inability to detect and discern different nutrient stresses, especially when combined stresses are present.
Why it matches plant phenotyping methodsδ13Cを用いた栄養ストレス予測法の有効性を複数作物で評価・検証しており、植物状態の推定手法の技術的妥当性が中心である。
titleδ 13 C as a tool for iron and phosphorus deficiency prediction in crops.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the raw δ13C/physiology data and the analysis scripts used to generate figures, which is a paper-specific, publicly actionable asset.Code · publick Dr. Christian Ceccon for providing support for the isotope analysis.
DATA AVAILABILITY STATEMENT
The following information was supplied regarding data and code availability: the raw data, the version of the individual packages and scripts used to analyze the data and generate the figures of this study are available at GitHub: https://github.com/Fabio-Trevisan/13C-Experiment.git .
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Increases in phosphoenolpyruvate carboxylase activity in iron‐deficient sugar beet roots: Analysis of spatial localization and post‐translational modification
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10.1023/A:1Open asset ↗Fabio-Trevisan/13C-Experiment · 13C-Experimentlines:309-505Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R 2 , 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.
Why it matches plant phenotyping methodsコムギ葉のハイパースペクトル画像と画像特徴からクロロフィル量を推定する手法を開発・評価しており、植物表現型の取得・推定が中心である。
abstractCompared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique.
Reproduction assets foundThe paper's phenotype data (335 wheat varieties, SPAD values, hyperspectral-derived traits) are stated to be contained in the article and its supplementary files (Table S1 variety list, Table S2 SPAD values), publicly downloadable from the MDPI supplementary link. No author analysis code, models, or raw hyperspectral/3Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24065825/s1 .
Click here for additional data file.
Author Contributions
C.Z. and Y.Y. conceived and designed the study; Y.Y., R.N., T.M., Y.S. and F.S. (Fanghui Shi) collected the wheat samples; Y.Y., Y.W. and C.Z. analyzed the data; Y.Y. and X.L. wrote the manuscript; F.S. (Fengli Sun), Y.X. and C.Z. revised the manuscript. AlOpen asset ↗lines:70-112Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R 2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
Why it matches plant phenotyping methods落花生種子の活力という植物形質をハイパースペクトル画像と機械学習で非破壊推定する手法が研究の中心であり、分類・回帰性能も評価している。
abstractIn this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' original contributions (hyperspectral seed vigor data and analysis). The repository URL in the text (https://github.com/cjkka/cjkka/tree/main) is under the allowed base URL https://github.com/cjkka/. No separate code orDataset · publicavailable. This data can be found here: https://github.com/cjkka/ absence of any commercial or financial relationships that could beOpen asset ↗cjkkapdf-page:12 lines:1-54Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Viruses are key players in marine ecosystems where they infect abundant marine microbes. RNA viruses are emerging as key members of the marine virosphere. They have recently been identified as a potential source of mortality in diatoms, a group of microalgae that accounts for roughly 40% of the primary production in the ocean. Despite their likely importance, their impacts on host populations and ecosystems remain difficult to assess. In this study, we introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements to expand our understanding of viral infection. We followed different stages of infection of the bloom-forming diatom Guinardia delicatula by the RNA virus GdelRNAV-04 until the complete lysis of the host. From 20h after infection, we observed quantifiable changes in subcellular host morphology and biomass. Our microscopy monitoring also showed that viral infection of G. delicatula induced the formation of auxospores as a probable defense strategy against viruses. Our method enables the detection of discriminative morphological features on the subcellular scale and at high throughput for comparing populations, making it a promising approach for the quantification of viral infections in the field in the future.
Why it matches plant phenotyping methods自動3D共焦点顕微鏡と定量画像解析を組み合わせ、感染に伴う珪藻の細胞形態・バイオマスを高スループットに定量する手法が研究の中心である。
abstractwe introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements
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 full annotated script can be found on https://github.com/mariescopy/Guinardia_ViralInfectionOpen asset ↗mariescopy/Guinardia_ViralInfectionlines:286-318Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with ≥ 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R 2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHT Data from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice.
Why it matches plant phenotyping methods自動ハイパースペクトル画像を用いてイネの生理形質を非破壊推定し、予測精度を評価しているため、フェノタイピング手法が中心的です。
abstractHere, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system.
Reproduction assets foundThe paper's collected/analyzed datasets (hyperspectral imaging and physiological trait data) are publicly deposited in the Purdue University Research Repository, and the authors' analysis code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicand/or edits.
657 CONFLICT OF INTEREST
658 The authors declare no conflict of interest.
659 FUNDING
660 This work was partially funded by a grant from USDA NIFA to DRW (#2022-67013-36205).
661 DATA AVAILABILITY
662 The datasets collected and analyzed for this study can be found in the Purdue University Research
663 Repository [https://purr.purdue.edu/publications/4079/1].
664
665 REFERENCES
666 Al Makdessi, N., Ecarnot, M., Roumet, P., and Rabatel, G. (2019). A spectral correction method for
667 multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application
668 to nitrogen content assessment in wheat. Precision Agric 20, 237–259. doi: 10.1007/s11119-
669 018-Open asset ↗pdf-layout-page:30 lines:1-64Code · public249
250 Data analysis
251 Data were formatted and analyzed in R 4.1.1 (R Core Team, 2021) with packages dplyr
252 (Wickham et al., 2021) and reshape2 (Wickham, 2007). Plots were made with package ggplot2
253 (Wickham, 2016) or in base R environment. The code for each physiological trait model can be
254 accessed through GitHub (https://github.com/To-Chia/rice_imaging_ms).
255 Physiological trait collection: From the physiological trait measurements, we derived specific
256 leaf area (SLA, cm2g-1), CN ratio (C:N), specific leaf area with respect to carbon (SLA_C (cm2
257 mg-1 (C)) and specific leaf nitrogen (SLN, mg (N) cm-2). The summary statistics are in Table S3.
258 Histograms and normal Open asset ↗GitHub · To-Chia/rice_imaging_mspdf-layout-page:12 lines:1-64Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Drought tolerance and quality stability are important indicators to evaluate the stress tolerance of tea germplasm resources. The traditional screening method of drought resistant germplasm is mainly to evaluate by detecting physiological and biochemical indicators of tea plants under drought stresses. However, the methods are not only time consuming but also destructive. In this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators. The results showed that: (1) the information contents of malondialdehyde, soluble sugar and total polyphenol were 0.21, 0.209 and 0.227 respectively, and the drought tolerance coefficient (DTC) index of each tea variety was between 0.069 and 0.81; (2) the comprehensive drought tolerance of different varieties were (from strong to weak): QN36, SCZ, ZC108, JX, JGY, XY10, QN1, MS9, QN38 , and QN21 ; (3) by using SVM, RF and PLSR to model DTC (drought tolerance coefficient) data, the best prediction model was selected as MSC-2D-UVE-SVM (R 2 = 0.77, RMSE = 0.073, MAPE = 0.16) for drought tolerance of tea germplasm resources, named Tea-DTC model. Therefore, the Tea-DTC model based on hyperspectral machine-learning technology can be used as a new screening method for evaluating tea germplasm resources with drought tolerance.
Why it matches plant phenotyping methods茶樹の乾燥耐性という植物状態をハイパースペクトル画像と機械学習で推定するモデルを開発し、従来の生理・生化学指標に代わるスクリーニング手法として性能評価しているため、フェノタイピング手法が中心である。
abstractIn this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators.
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 content data of physiological and biochemical components of tea leaves measured with the kit are shown in supplementary Table 1Open asset ↗lines:322-334Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Hyperspectral imaging is a promising method to predict traits in a high-throughput manner with the potential to unlock quantitative genetic studies. Researchers have successfully modeled physiological traits such as vegetative Nitrogen content, but scope of methodology and lack of truly novel testing data hinder large scale trust in the process. Here, I explore the ability to model leaf Nitrogen content from hyperspectral reflectance data collected with a LeafSpec imaging device on 22 maize hybrids. Three broad strategies based on different input feature sets are undertaken. Strategy one mines data for the most informative hyperspectral channels and then constructs a normalized index similar to NDVI as input features. Strategy two considers all 364 channels of hyperspectral data and makes predictions using various machine learning techniques; partial least squares regression(PLSR), random forest regression, and a feed-forward neural net regression. Strategy three aims to take advantage of the spatial distribution of hyperspectral data on the leaf surface by training a convolutional neural net(CNN). A normalized visual index constructed from bands most correlated with nutrient content out-performed established NDVI. PLSR was the most accurate algorithm, followed by feed-forward neural net and then CNN, based on coefficient of determination score. PLSR is well established as a robust method for hyperspectral prediction which is further evidenced by this study. This is one of the first applications of CNN for hyperspectral data. Despite not being the most accurate algorithm there remains room for hyper-parameter optimization.
Why it matches plant phenotyping methodsトウモロコシ葉の窒素含量をハイパースペクトル画像から推定する特徴量設計・機械学習手法を比較評価しており、植物フェノタイピング手法が中心である。
abstractHyperspectral imaging is a promising method to predict traits in a high-throughput manner
Reproduction assets foundThe paper's data availability statement explicitly points to a public GitHub repository containing CorNDVI tensors (phenotyping-derived image data) and analysis scripts used in this study.Code · publicard neural network. CNN – Convolutional neural network.
Strategy Prediction goodness Prediction mean square
of fit - R2 error (% Total Nitrogen)
NDVI_Regression 0.21 0.21
CorNDVI_Regression 0.41 0.16
PLSR 0.57 0.12
RF 0.39 0.16
FFNN 0.54 0.12
CNN 0.49 0.14
DATA AVAILABILITY STATEMENT
CorNDVI tensors and scripts are available at https://github.com/B-Webster-Bio/NuteNet
ACKNOWLEDGMENTS
This work was made possible thanks to AgSpectrum company and NRT-IMPACTs fellowship.
4Open asset ↗B-Webster-Bio/NuteNet · NuteNetpdf-layout-page:5 lines:1-29Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
Why it matches plant phenotyping methods複数の表現型予測モデルを植物種の実データとシミュレーションで系統比較・評価しており、計算による植物形質推定が研究の中心である。
abstractour aim is to systematically compare 12 different phenotype prediction models
Reproduction assets foundThe paper's authors publicly release their analysis code (easyPheno framework and the phenotype_prediction repository containing simulated phenotypes, hyperparameter optimization results, GWAS results, and figure-generation code), plus the Arabidopsis SNP matrix (figshare) and four AraPheno phenotype datasets used in aCode · publicAll simulated phenotypes, detailed results of the whole hyperparameter optimization, precomputed permutation-based GWAS results, and the code for conducting the simulations and generating all figures can be freely downloaded from our GitHub repository: https://github.com/grimmlab/phenotype_prediction .Open asset ↗grimmlab/phenotype_predictionlines:619-631Dataset · publicThe fully imputed SNP matrix data for Arabidopsis thaliana is publicly available and can be downloaded from https://doi.org/10.6084/m9.figshare.11346893.v1 .Open asset ↗10.6084/m9.figshare.11346893.v1lines:619-631Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / 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 · checked 8 Sept 2026
Leaf pigments, including chlorophylls and carotenoids, are important biochemical indicators of plant photosynthesis and photoprotection. In this study, we developed, optimized, and validated a sequential extraction and liquid chromatography-diode array detection method allowing for the simultaneous quantification of the main photosynthetic pigments, including chlorophyll a, chlorophyll b, β-carotene, lutein, neoxanthin, and the xanthophyll cycle (VAZ), as well as the characterization of plant pigment derivatives. Chromatographic separation was accomplished with the newest generation of core-shell columns revealing numerous pigment derivatives. The sequential extraction allowed for a better recovery of the main pigments (+25 % chlorophyll a, +30 % chlorophyll b, +42 % β-carotene, and 61% xanthophylls), and the characterization of ca. 5.3 times more pigment derivatives (i.e., up to 62 chlorophyll and carotenoid derivatives including isomers) than with a single-step extraction. A broad working range of concentrations (300-2,000 ng.mL -1 ) was achieved for most pigments and their derivatives and the limit of detection was as low as a few nanograms per milliliter. The method also showed adequate trueness (RSD Fagus sylvatica L . leaves, pigment derivatives revealed a high within-individual tree variability throughout the growing season that could not be detected using the main photosynthetic pigments alone, eventually showing that the method allowed for the monitoring of pigment dynamics at unprecedented detail.
Why it matches plant phenotyping methods葉の光合成色素を植物の生理状態・動態として定量する分析法を開発、最適化、検証しており、表現型取得法が研究の中心である。
abstractIn this study, we developed, optimized, and validated a sequential extraction and liquid chromatography-diode array detection method allowing for the simultaneous quantification of the main photosynthetic pigments
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 ) and calibrate optical measurements performed with a field spectroradiometer or airborne spectral sensors (Croft and Chen, 2018 ).Open asset ↗lines:285-286Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in many plant species including tree crops leading to aberrant growth, flowering, and fruiting. Currently, research in this field is impeded by the lack of affordable non-invasive methods for on-line monitoring of dormancy. We report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants. The proposed method is based on continuous near-field sensing of the photosynthetic activity of shoots via pulse-amplitude modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli are subjected to frequency-domain analysis. The proposed approach allows to overcome the variance coming from diurnal changes of insolation and to derive estimations on the depth of dormancy. Our approach was validated over three seasons in an experimental apple (Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the tress at different phases of dormancy) and the output of commonly used chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of freeze damages along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠深度を非侵襲的に推定する蛍光センサーと時系列解析のフレームワークを開発し、従来法およびモデルと照合検証しており、表現型取得法が研究の中心である。
abstractWe report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub. This is a paper-specific computational analysis asset for the chlorophyll fluorescence/JIP-test time-frequency analysis. The full raw data and derived parameters are only available on request, so they are notCode · publicic projects in priority areas of scientific and technological
development (grant number 075-15-2020-774).
Data Availability Statement: The raw data and derived parameters are available from the corre-
sponding author on reasonable request.
Code used in the analysis, accompanied with a subset of the data, is available on GitHub
(https://github.com/Lodinn/PAM-timeseries).
Acknowledgments: The indoors chlorophyll fluorescence measurements were carried out at the
Phototrophic Organisms Phenotyping user facilities of Lomonosov Moscow State University. TheOpen asset ↗Lodinn/PAM-timeseriespdf-layout-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Precise and site-specific nitrogen (N) fertilizer management of vegetables is essential to improve the N use efficiency considering temporal and spatial fertility variations among fields, while the current N fertilizer recommendation methods are proved to be time- and labor-consuming. To establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020. Two planting densities, viz, high (123,000 plants ha -1 ) in Year I and low (57,000 plants ha -1 ) in Year II, whereas, combined densities in Year III were used to evaluate the effect of five N application rates (0, 45, 109, 157, and 205 kg N ha -1 ). A robust relationship was observed between the sensor-based normalized difference vegetation index (NDVI), the ratio vegetation index (RVI), and the yield potential without topdressing (YP 0 ) at the rosette stage, and 81-84% of the variability at high density and 76-79% of that at low density could be explained. By combining the densities and years, the R 2 value increased to 0.90. Additionally, the rosette stage was identified as the earliest stage for reliably predicting the response index at harvest (RI Harvest ), based on the response index derived from NDVI (RI NDVI ) and RVI (RI RVI ), with R 2 values of 0.59-0.67 at high density and 0.53-0.65 at low density. When using the combined results, the RI RVI performed 6.12% better than the RI NDVI , and 52% of the variability could be explained. This study demonstrates the good potential of establishing a sensor-based N topdressing algorithm for bok choy, which could contribute to the sustainable development of vegetable production.
Why it matches plant phenotyping methods携帯型キャノピーセンサーのNDVI/RVIから収量ポテンシャルと施肥応答を推定するアルゴリズムを開発・検証しており、植物形質推定手法が研究の中心です。
abstractTo establish a site-specific N topdressing algorithm for bok choy ( Brassica rapa subsp. chinensis ), using a hand-held GreenSeeker canopy sensor, we conducted field experiments in the years 2014, 2017, and 2020.
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 empirical exponential model was used to determine the relationship between YP 0 and the sensor-based vegetation indices (NDVI and RVI) for bok choy across growth stages ( Table 3 ).Open asset ↗lines:391-483Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Why it matches plant phenotyping methodsクロロフィル蛍光データを用いたフェノミック予測モデルを構築・比較し、成長形質の予測性能と条件間・集団間の転移性を評価しているため、植物フェノタイピング手法が中心である。
abstractThe models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors.
Reproduction assets foundThe paper's Data availability statement explicitly states that all code and datasets (including the ChlF phenomic data and growth-trait phenotypes used for GP/PP modeling) are freely available at the authors' public GitHub repository https://github.com/alainmbebi/GP-PP, which matches an allowed URL. Other URLs (BGLR CRCode · publicr and is an excellent proxy for photosynthesis in coffee, making it a tool of choice for assessing the vigor of a genotype, which the present study tends to prove.
Data availability
We implemented all statistical models using R programming language; the codes and all data sets used in the current study are freely available from https://github.com/alainmbebi/GP-PP .
Supplemental material is available at G3 online.
Supplementary Material
jkac170_Supplementary_Data_File_S1
Click here for additional data file.
jkac170_Supplementary_Data_File_S2
Click here for additional data file.
Acknowledgments
We would like to thank the 2 anonymous reviewers for their suggestions and comments.
Funding
ThOpen asset ↗alainmbebi/GP-PPlines:876-910Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.
Why it matches plant phenotyping methodsRGB-D画像とCNNを用いてレタスの複数の生育形質を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera.
Reproduction assets foundThe paper's phenotyping inputs (388 RGB-D lettuce image pairs with destructive growth-index measurements from the Third Autonomous Greenhouse Challenge) are a third-party public dataset explicitly stated to be publicly available at 4TU.ResearchData, with the DOI 10.4121/15023088.v1 cited in the text and figure captionsDataset · publicThe dataset is available in online: https://doi.org/10.4121/15023088.v1 [ 30 ].Open asset ↗10.4121/15023088.v1lines:518-697Code / 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 confirmedEurope PMC · checked 8 Sept 2026
Canopy photosynthesis is the sum of photosynthesis of all above-ground photosynthetic tissues. Quantitative roles of nonfoliar tissues in canopy photosynthesis remain elusive due to methodology limitations. Here, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities. The new model precisely predicts wheat canopy gas exchange rates at different growth stages, weather conditions, and canopy architectural perturbations. Using the model, we systematically study (1) the contribution of both foliar and nonfoliar tissues to wheat canopy photosynthesis and (2) the responses of wheat canopy photosynthesis to plant physiological and architectural changes. We found that (1) at tillering, heading, and milking stages, nonfoliar tissues can contribute ~4, ~32, and ~50% of daily gross canopy photosynthesis ( A cgross ; ~2, ~15, and ~-13% of daily net canopy photosynthesis, A cnet ) and absorb ~6, ~42, and ~60% of total light, respectively; (2) under favorable condition, increasing spike photosynthetic activity, rather than enlarging spike size or awn size, can enhance canopy photosynthesis; (3) covariation in tissue respiratory rate and photosynthetic rate may be a major factor responsible for less than expected increase in daily A cnet ; and (4) in general, erect leaves, lower spike position, shorter plant height, and proper plant densities can benefit daily A cnet . Overall, the model, together with the facilities for quantifying plant architecture and tissue gas exchange, provides an integrated platform to study canopy photosynthesis and support rational design of photosynthetically efficient wheat crops.
Why it matches plant phenotyping methods小麦の葉・非葉器官・群落の光合成と植物体構造を定量する統合モデルを開発し、ガス交換測定施設で検証しているため、植物フェノタイピング手法が研究の中心である。
abstractHere, we develop the first complete canopy photosynthesis model incorporating all above-ground photosynthetic tissues and validate this model on wheat with state-of-the-art gas exchange measurement facilities.
Reproduction assets foundThe paper explicitly states that the source code and user manual for the 3dCAP-wheat framework (used for plant architecture extraction, 3D reconstruction, ray tracing, and canopy photosynthesis computation) are freely available on GitHub at the authors' public URL.Code · publicSource code used for this study, together with the user manual, are freely available for noncommercial use at https://github.com/rootchang/3dCAP-wheat .Open asset ↗rootchang/3dCAP-wheatlines:162-298Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と複数の回帰モデルを用いて植物群落形質を推定し、手法性能を比較評価しているため、形質取得・推定法が研究の中心である。
abstractwe compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging.
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 · publicFigure S2: The UAV hyperspectral image used
for mapping plant community traits. The upper one is the raw image and the lower one is the
corrected image shown in true colour composites.Open asset ↗pdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.
Why it matches plant phenotyping methods塩ストレス下のイネに対するHSI画像処理パイプラインとPLSR予測モデルを開発・評価し、Na+・K+という植物生理状態の非破壊フェノタイピングへの適用性と予測性能を検証しているため、方法が中心的である。
abstractAnalysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis.
Reproduction assets foundThe authors deposited the paper's hyperspectral image dataset (rice plants under salinity stress, used for Na+/K+ prediction modeling) in the Dryad Digital Repository, with an explicit availability statement and public DOI.Dataset · publicData Availability: The hyperspectral image dataset used in this study is available through the DRYAD Digital Repository: https://doi.org/10.5061/dryad.2jm63xsrm .Open asset ↗Dryad Digital Repository · 10.5061/dryad.2jm63xsrmlines:140-151Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Why it matches plant phenotyping methods葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。
abstractReflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits
Reproduction assets foundThe paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.Dataset · publicected and curated the spectral and trait data. SK analyzed the data,
597 interpreted the results, and wrote the first draft with substantial contributions from EL. All authors
598 contributed to further revisions of the paper.
599
600 Data availability
601 All fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).
602 Upon publication, we will also upload all spectral data, as well as metadata and trait data, to theOpen asset ↗pdf-layout-page:38 lines:1-58Code · publicnder a CC-BY 4.0 International license.
603 Ecological Spectral Information System (EcoSIS, https://ecosis.org/), and upload models to the
604 Ecological Spectral Model Library (EcoSML, https://ecosml.org/). At that stage, we will update this
605 section accordingly. Analysis scripts are available as a repository on GitHub
606 (https://github.com/ShanKothari/CABO-trait-models).Open asset ↗ShanKothari/CABO-trait-modelspdf-layout-page:39 lines:1-14Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The existing methods of callose quantification include epifluorescence microscopy and fluorescence spectrophotometry of aniline blue-stained callose particles, immuno-fluorescence microscopy and indirect assessment of both callose synthase and β-(1,3)-glucanase enzyme activities. Some of these methods are laborious, time consuming, not callose-specific, biased and require high technical skills. Here, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA). Tissue culture-derived banana plantlets were inoculated with Xanthomonas campestris pv. musacearum ( Xcm ) bacteria as a biotic stress factor inducing callose production. Banana leaf, pseudostem and corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification. Callose levels were significantly different in banana tissues of Xcm -inoculated and control groups except in the pseudostems of both banana genotypes. The method described here could be applied for the quantification of callose in different plant species with satisfactory level of specificity to callose, and reproducibility. Additionally, the use of 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases. We provide step-by-step detailed descriptions of the method.
Why it matches plant phenotyping methods植物組織中のカロース量を定量するELISA法を開発・再現性評価し、高スループット測定への適用性を示した研究であり、植物状態の取得方法が中心です。
abstractHere, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA).
Reproduction assets foundThe paper's supplementary material (Table S1) publicly hosts the callose quantification measurements (concentrations in leaves, pseudostems, corms of Xcm-inoculated vs. control banana plantlets) underlying this study's analysis. No author analysis code, images, or trained models are deposited; the R statistical packageDataset · publicor up to 12 months).
Dissolve para-nitrophenyl phosphate (pNPP) in substrate buffer to a working concentration of 1 mg/mL.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/mps5040054/s1 , Table S1: Analysis of callose concentration in the leaves, pseudostems and corms of banana plants inoculated and non-inoculated (control) with Xcm (Independent sample t-test, α ≤ 0.05).
Click here for additional data file.
Author Contributions
Conceptualization, A.K.T.; methodology, A.S.M., A.K.T. and P.S.; validatiOpen asset ↗lines:167-297Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Ultraviolet-B (UV-B, 280-315 nm) radiation has been known as an elicitor to enhance bioactive compound contents in plants. However, unpredictable yield is an obstacle to the application of UV-B radiation to controlled environments such as plant factories. A typical three-dimensional (3D) plant structure causes uneven UV-B exposure with leaf position and age-dependent sensitivity to UV-B radiation. The purpose of this study was to develop a model for predicting phenolic accumulation in kale ( Brassica oleracea L. var. acephala ) according to UV-B radiation interception and growth stage. The plants grown under a plant factory module were exposed to UV-B radiation from UV-B light-emitting diodes with a peak at 310 nm for 6 or 12 h at 23, 30, and 38 days after transplanting. The spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model. Total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), UV-B absorbing pigment content (UAPC), and the antioxidant capacity were significantly higher in UV-B-exposed leaves. Daily UV-B energy absorbed by leaves and developmental age was used to develop stepwise multiple linear regression models for the TPC, TFC, TAC, and UAPC at each growth stage. The newly developed models accurately predicted the TPC, TFC, TAC, and UAPC in individual leaves with R 2 > 0.78 and normalized root mean squared errors of approximately 30% in test data, across the three growth stages. The UV-B energy yields for TPC, TFC, and TAC were the highest in the intermediate leaves, while those for UAPC were the highest in young leaves at the last stage. To the best of our knowledge, this study proposed the first statistical models for estimating UV-B-induced phenolic contents in plant structure. These results provided the fundamental data and models required for the optimization process. This approach can save the experimental time and cost required to optimize the control of UV-B radiation.
Why it matches plant phenotyping methods3DスキャンとレイトレーシングによるUV-B吸収量の推定、およびフェノール含量予測モデルの開発が研究の中心であり、植物形質の計測・推定手法に該当する。
abstractThe spatial distribution of UV-B radiation interception in the plants was quantified using ray-tracing simulation with a 3D-scanned plant model.
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
Relative growth rate and expansion rate of leaf groups, and the assigned leaf order in kale plants at 23, 30, and 38 DAT.Open asset ↗lines:612-691Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Common beanWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / development / phenology
Background Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.
Why it matches plant phenotyping methods遺伝子型・環境データから開花時期という植物形質を予測する動的モデルを構築・比較しており、形質推定手法が研究の中心である。
abstractWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.).
Reproduction assets foundThe authors publicly deposited the paper's MET phenotypic/meteorological observational data, synthetic data, and the R/FORTRAN analysis code (dynamic mixed-effects flowering model) on figshare (DOI 10.6084/m9.figshare.19692628), and separately deposited the RI family genotype data at a figshare link given in Methods.Dataset · publicComputer codes are available in the Supplementary Materials file, and observational and synthetic data in Additional file 1 , which have been uploaded to the figshare database repository ( https://doi.org/10.6084/m9.figshare.19692628 ).Open asset ↗figshare · 10.6084/m9.figshare.19692628lines:167-260Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (Rcv2=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (Rcv2=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step.
Why it matches plant phenotyping methodsUASマルチスペクトルセンサーと機械学習を用いて、草地の乾物バイオマスおよび植物窒素濃度という植物形質を推定し、センサー・アルゴリズム・予測変数を比較検証しているため、フェノタイピング手法が中心である。
abstractOur study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration.
Reproduction assets foundThe paper's field dataset (in situ DM, plant N concentration, canopy height, and corresponding UAS multispectral measurements from 10 grassland sites) is publicly deposited in PANGAEA. The authors' analysis code is only available upon request, so it does not qualify as a public asset.Dataset · publicThe field data set used in this study is
available in the PANGAEA repository at https://doi.org/10.1594/PANGAEA.920600 (Schucknecht et al., 2020b).Open asset ↗PANGAEA · 10.1594/PANGAEA.920600lines:1344-1401Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm 3 , and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized.
Why it matches plant phenotyping methodsトウモロコシ穀粒の組成・物理形質を高スループットに取得・推定するハイパースペクトル画像プラットフォームと解析手法の開発が研究の中心である。
abstractwe developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels
Reproduction assets foundThe paper's authors explicitly state that all analysis code for the hyperspectral phenotyping pipeline (PLSR trait prediction, PLS-DA kernel-side classification, image analysis) is publicly available in their GitHub repository.Code · publicgenerate a confusion
matrix, along with specificity and sensitivity rates (Supplemental
Table 1).
2.7. Complete pipeline
The processes, measurements, and analyses described in Sections
2.3-2.6 were combined to produce a pipeline shown in Fig. 1B-E. All of
the code created to execute the analyses is available in this repository,
https://github.com/jivarelao/Hyperspectral_Scanner.3. Results and discussion
3.1. Variability of maize kernel traits in ground-truth sets
Directly measured traits ranged widely across the kernel samples
(Table 1). Kernel volume displayed the largest range (5.6-fold). Kernel
weight was second at 5-fold, followed by vitreousness (2.7-fold), protein
(2.4-fold) and densitOpen asset ↗https://github.com/jivarelao/Hyperspectral_Scanner.3pdf-raw-page:5 lines:1-77Code / 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.
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513 Data Availability
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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.
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522 Table 1: Rootstock parentage
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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
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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
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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 confirmedEurope PMC · checked 8 Sept 2026
We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.
Why it matches plant phenotyping methodsメタボロームスペクトルから大麦の麦芽品質形質を予測し、複数モデルと交差検証で予測精度を比較・検証しており、育種利用可能な形質推定法が中心です。
abstractWe investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes.
Reproduction assets foundThe article states that all data used (malting quality trait records and metabolomic features for 2667 plots of 564 spring barley lines) are deposited in a public Mendeley Data repository with a direct link, making the paper's phenotyping measurements publicly available.Dataset · publicAll the data used are available in a public accessible repository with the direct link as https://data.mendeley.com/datasets/s3s4ft92wj/1 .Open asset ↗s3s4ft92wjlines:160-241Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
Why it matches plant phenotyping methods葉面ハイパースペクトル反射から光合成・葉形質を推定するモデルを構築し、精度評価と集団への適用を行っており、表現型取得・推定手法が研究の中心である。
abstractwe used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits
Reproduction assets foundThe paper's leaf reflectance processing code (FieldSpec R Package, Zenodo DOI 10.5281/zenodo.6248237) and its paper-specific phenotype/reflectance data, PLSR model coefficients, and complete genetic mapping dataset are publicly available via the Genetics figshare supplemental repository (DOI 10.25386/genetics.19394693)Dataset · publicgenetic mapping and
804
analyses can be found in File S18. The majority of lines and accessions used in this manuscript
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are available via GRIN (https://www.ars-grin.gov/) or by request from Soybase.org for the NAM
806
lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
807
available at figshare: https://doi.org/10.25386/genetics.19394693
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Acknowledgements
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We thank Troy Cary, Chris Moller, and Noah Mitchell for help in setting up and maintaining the
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experimental plots, collecting data, and processing samples.
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Funding
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This work was supported by soybean checkoff funding from the United Soybean Board. ASS
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was supported by a post-doctoraOpen asset ↗figshare · 10.25386/genetics.19394693pdf-raw-page:39 lines:1-49Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Vitamin A deficiency (VAD) is a public health issue worldwide. Provitamin A (PVA) biofortified maize serves as an alternative to help combat VAD. Breeding efforts to develop maize varieties with high PVA carotenoid content combine molecular and phenotypic selection strategies. The phenotypic assessment of carotenoids is currently done using liquid chromatography, a precise but time- and resource-consuming methodology. Using near-infrared spectroscopy (NIRS) could increase the breeding efficiency. This study used ultra-performance liquid chromatography (UPLC) data from 1857 tropical maize genotypes as a training set and NIRS data to do an independent test of a set of 650 genotypes to predict PVA carotenoids using Bayesian and modified partial least square (MPLS) regression models. Both regression methods produced similar prediction accuracies for the total carotenoids (r2 = 0.75), lutein (r2 = 0.55), zeaxanthin (r2= 0.61), β-carotene (r2 = 0.22) and β-cryptoxanthin (BCX) (r2 = 0.57). These results demonstrate that Bayesian and MPLS regression of BCX on NIRS data can be used to predict BCX content, the current focus on PVA enhancement, and thus offers opportunities for high-throughput phenotyping at a low cost, especially in the early stages of PVA maize breeding pipeline when many genotypes must be screened.
Why it matches plant phenotyping methodsNIRSと回帰モデルを用いてトウモロコシ種子のプロビタミンAカロテノイド含量を高スループット推定する手法が研究の中心であり、育種用フェノタイピングへの応用を技術的に評価している。
abstractUsing near-infrared spectroscopy (NIRS) could increase the breeding efficiency.
Reproduction assets foundThe paper's maize carotenoid/NIRS datasets are publicly deposited in a CIMMYT repository with an explicit download link; the analysis R code is only in the article appendix, not a separate public deposit.Dataset · publicn [INV-003439 BMGF/FCDO Accelerating Genetic Gains in
Maize and Wheat for Improved Livelihoods (AG2MW)] as well as USAID projects [Amend. No. 9
MTO 069033, USAID-CIMMYT Wheat/AGGMW, AGG-Maize Supplementary Project, AGG (Stress
Tolerant Maize for Africa)].
Data Availability Statement: The datasets can be downloaded from the link https://hdl.handle.net/11529/10548607 (accessed on 24 April 2022).
Acknowledgments: The authors kindly thank Andrea Susana Cruz, research assistant at CIMMYT,
for her field work and for facilitating the maize germplasm used in the study. They also thank the
Maize Quality Laboratory “Evangelina Villegas” at CIMMYT for support in analyzing the samples).
Conflicts ofOpen asset ↗hdl.handle.net · 11529/10548607pdf-raw-page:10 lines:1-51Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
MaizeField / plotRootPhysiological trait estimationRoot system architectureStress response / tolerance
High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.
Why it matches plant phenotyping methods数百遺伝子型・数千区画を対象とする高スループットな圃場根系表現型測定を設計・適用し、根抜き力と根系構造を取得している。主目的は遺伝解析だが、表現型取得手法の大規模適用が実質的に記述されているため採録する。
abstractHigh-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity.
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 5 ); however, we saw no overlap in hits between our root traits and flowering, consistent with the lack of correlation in Figure 4 .Open asset ↗lines:330-340Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genetic variation is the basis of selection, evolution and breeding. Maize landraces represent a rich source of allelic diversity, but their efficient utilization in breeding and research has been hampered by their heterogeneous and heterozygous nature and insufficient information about most accessions. While molecular inventories of germplasm repositories are growing steadily, linking these data to meaningful phenotypes for quantitative traits is challenging. Here, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces. Due to their full homozygosity, the DH lines can be multiplied ad libitum and represent a powerful biological resource available to the community. The DH lines allow high-precision phenotyping in repeated experiments and reveal the full additive genetic variance of the population. The DH lines were evaluated for nine agronomically important, quantitative traits in multi-environment field trials comprising seven locations and two years. The DH populations revealed high genetic variance and high heritability for the analysed traits. The DH lines were genotyped with 600k SNP markers. After stringent quality filtering 500k markers remained for further analyses. This is the largest resource of landrace derived DH material in maize, unprecedented in its structure and dimension. The presented data are ideal for linking molecular variation to meaningful phenotypes. They can be used for genome-wide association studies, genomic prediction, and population genetic analyses as well as for developing and testing statistical methods. All plant material is available to the community for conducting additional experiments, extending the panel of traits and environments, and for testing the landrace-derived lines in combination with other genetic material.
Why it matches plant phenotyping methods大規模な植物表現型データセットを資源として提示し、再利用可能な高精度表現型解析基盤を提供しているため、表現型データセットとして中心的に該当する。
abstractHere, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces.
Reproduction assets foundThe paper is a Data in Brief article depositing its own phenotypic (raw plot-level data and BLUEs for nine traits across eleven environments) and genotypic datasets on figshare under DOI 10.6084/m9.figshare.12137142.v1, with a direct URL matching an allowed URL. This is a paper-specific, publicly accessible phenotype/Dataset · publicData accessibility
Repository name: figshare
Data identification number: 10.6084/m9.figshare.12137142.v1
Direct URL to data: https://doi.org/10.6084/m9.figshare.12137142.v1
Related research article
M. Mayer, A.C. Hölker, E. González-Segovia, E. Bauer, T. Presterl, M. Ouzunova, A.E. Melchinger, C.-C. Schön, Discovery of beneficial haplotypes for complex traits in maize landraces, Nat. Commun. 11 (2020) 4954. https://doi.org/10.1038/s41467-020-18683-3
Value of the Data
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This is the largest resourceOpen asset ↗figshare · 10.6084/m9.figshare.12137142.v1lines:52-74Code / 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 8 Sept 2026
Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.
Why it matches plant phenotyping methods植物の動的な生理状態を画像から定量化するFRET画像処理ツールの開発・ベンチマークが中心であり、花粉管内カルシウム分布の抽出に実質的に応用されている。
abstractFRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python.
Reproduction assets foundThe paper's authors publicly released the FRET-IBRA analysis toolkit (Python source code, test images, example configuration files, and tutorial) under a BSD license on GitHub. The test images include the FRET microscopy image stacks of growing Arabidopsis pollen tubes used in the paper's calcium-distribution phenotypiCode · publicThe source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.Open asset ↗github.com/gmunglani/fret-ibralines:113-128Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.
Why it matches plant phenotyping methodsハイパースペクトル葉反射から光合成能力と葉窒素含量を推定する統計モデルを開発・評価しており、植物形質取得手法が中心である。
abstractTherefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes
Reproduction assets foundThe authors deposited the paper's phenotype measurements (photosynthetic capacity parameters, leaf nitrogen, hyperspectral leaf reflectance of Populus taxa) in Mississippi State University's institutional repository, Scholars Junction, with an explicit public DOI. No author analysis code or trained models were shared.Dataset · publicData Availability: Our data can be accessed from Scholars Junction: Mississippi State University’s Institutional Repository at the following DOI: https://doi.org/10.54718/BACR5952 .Open asset ↗Scholars Junction · 10.54718/BACR5952lines:159-169Code / 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 confirmedEurope PMC · checked 15 Sept 2026
TomatoGreenhouseLeafPhysiological trait estimationGrowth / development / phenology
While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R 2 value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.
Why it matches plant phenotyping methodsトマトの開花率という植物形質をメタボロームデータから予測するLASSOモデルを構築し、別データセットで検証しており、形質推定手法が中心である。
abstractmetabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes.
Reproduction assets foundThe paper's tomato leaf metabolome dataset (DM0041) used for the LASSO anthesis-rate modeling is publicly deposited in DROP Met at PRIMe, with an explicit Data Availability Statement and URL. No author analysis code or trained model checkpoint is stated as publicly available; the supplementary material link is generic.Dataset · publicss spectrometer (LC-QqQ-MS) (UPLC coupled with Xevo TQ-S, Waters, Milford, MA, United States) ( Sawada et al., 2009 ; Sawada et al., 2019 ). The analytical conditions are described in detail in Supplementary Tables S1–S3 . The metabolome data were deposited in the DROP Met in PRIMe (the Platform for RIKEN Metabolomics) (DM0041, http://prime.psc.riken.jp/archives/data/DropMet/059/ ).
2.3.3 Measurement of Relative Metabolite Contents
For the Tsukuba data (TK01), the peak areas of 501 target metabolites (including two internal standards) were processed as follows. Values below the detection limit were set to zero. The peak area of each metabolite in a leaf sample was divided by the mean peak arOpen asset ↗DROP Met · DM0041lines:87-98Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.
Why it matches plant phenotyping methods穀粒という植物器官のタンパク質含量をNIRセンサーと機械学習で推定する校正モデルを開発・評価しており、形質取得・抽出法が研究の中心である。
abstractWe explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra.
Reproduction assets foundThe authors explicitly state that the custom CNN analysis code for this paper's NIR protein-prediction models is publicly available on GitHub at the authors' repository URL, which matches an allowed URL. Supplementary tables are only referenced via a placeholder (www.mdpi.com/xxx/s1) and are not actionable; the Video SCode · publicced by the quality
and size of the datasets used for training the model. To minimize the “over-fitting” error,
the large dataset was used and split carefully to include the different multi-cereal species
in both the calibration and validation dataset (as described in section 2.5.1). The code is
available on the GitHub platform (https://github.com/adamavip/nirs-protein-prediction)
and its particular parts can be now utilized to enhance and develop other pipelines and
products.
For our dataset, the algorithms built using ML-based methods (particularly, the
stacked ensemble model via Hone Create and custom-designed CNN; section 3.4)
achieved the better comparative metrics for both spectra typesOpen asset ↗adamavip/nirs-protein-predictionpdf-raw-page:14 lines:1-54Code / 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
In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature.
Why it matches plant phenotyping methods植物の遺伝子型から288形質を予測するエンドツーエンドのニューラルネットワーク手法を開発・評価しており、表現型推定手法が研究の中心である。
abstractOur model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data.
Reproduction assets foundThe paper's authors explicitly state that the Galiana model code is freely available from their public Bitbucket repository, which is a paper-specific computational analysis asset. The phenotype data come from third-party databases (1001 Genomes, AraPheno) and are not paper-specific deposits; supplementary tables are aCode · publicWe implemented the model using pytorch ( 26 ). The code is freely available from our git repository https://bitbucket.org/eddiewrc/galiana/src/master/ .Open asset ↗bitbucket.org/eddiewrc/galiana · eddiewrc/galianalines:44-55Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Published1 Feb 2022Proceedings of the National Academy of Sciences of the United States of AmericaCited by 220 · OpenAlex ↗
Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.
Why it matches plant phenotyping methods果実の風味という植物器官形質を、メタボロームから予測する統計・機械学習モデルを開発し、交差検証で性能評価している。育種に利用可能な風味表現型推定法が中心であり、単なる代謝測定ではない。
abstractwe leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor.
Reproduction assets foundThe paper provides public author analysis scripts on GitHub and paper-specific phenotype data (sensory panel ratings, metabolite concentrations, model accuracies) in Datasets S1–S7 within the PNAS supporting information. The caret R package is a generic library and excluded.Code · publicRelevant scripts are provided in the GitHub repository at https://github.com/Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavor .Open asset ↗Resende-Lab/metabolomic_selection_for_enhanced_fruit_flavorlines:126-357Dataset · publicSensory panel ratings and metabolite concentrations are provided in Datasets S1 and S2 . Underlying data for Fig. 3 are provided in Dataset S3 . Model accuracies in Fig. 4 are provided in Datasets S4–S7 .Open asset ↗lines:126-357Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Background Most plants encounter water stress at one or more different stages of their life cycle. The maintenance of genetic stability is the integral component of desiccation tolerance that defines the storage ability and long-term survival of seeds. Embryonic axes of desiccation-sensitive recalcitrant seeds of Acer pseudoplatnus L. were used to investigate the genotoxic effect of desiccation. Alkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks and 8-oxo-7,8-dihydroguanine (8-oxoG) formation during progressive steps of desiccation and rehydration. Results The loss of DNA integrity and impairment of damage repair were significant predictors of the viability of embryonic axes. In contrast to the comet assay, automated electrophoresis failed to detect changes in DNA integrity resulting from desiccation. Notably, no significant correlation was observed between hydroxyl radical ( ٠ OH) production and 8-oxoG formation, although the former is regarded to play a major role in guanine oxidation. Conclusions The high-throughput comet assay represents a sensitive tool for monitoring discrete changes in DNA integrity and assessing the viability status in plant germplasm processed for long-term storage.
Why it matches plant phenotyping methods植物胚軸のDNA完全性と生存性を評価する高スループットコメットアッセイを最適化・検証しており、表現型状態の取得法が研究の中心である。
abstractAlkaline single-cell gel electrophoresis (comet assay) methodology was optimized and used to provide unique insights into the onset and repair of DNA strand breaks
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 · publicAdditional file 3: Fig. S3. (download PDF )
The representative comet measurements and images captured by Comet Assay IV analysis software.Open asset ↗lines:516-614Code / 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 confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
The root system of a plant provides vital functions including resource uptake, storage, and anchorage in soil. The uptake of macro-nutrients like nitrogen (N), phosphorus (P), potassium (K), and sulphur (S) from the soil is critical for plant growth and development. Small signaling peptide (SSP) hormones are best known as potent regulators of plant growth and development with a few also known to have specialized roles in macronutrient utilization. Here we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients. The SSP, CEP1 (C-TERMINALLY ENCODED PEPTIDE) enhanced nitrate uptake rate per unit root length in Medicago truncatula plants deprived of N in the high-affinity transport range. Single structural variants of M. truncatula and Arabidopsis thaliana specific CEP1 peptides, MtCEP1D1:hyp4,11 and AtCEP1:hyp4,11, enhanced uptake not only of nitrate, but also phosphate and sulfate in both model plant species. Transcriptome analysis of Medicago roots treated with different MtCEP1 encoded peptide domains revealed that hundreds of genes respond to these peptides, including several nitrate transporters and a sulfate transporter that may mediate the uptake of these macronutrients downstream of CEP1 signaling. Likewise, several putative signaling pathway genes including LEUCINE-RICH REPEAT RECPTOR-LIKE KINASES and Myb domain containing transcription factors, were induced in roots by CEP1 treatment. Thus, a scalable method has been developed for screening synthetic peptides of potential use in agriculture, with CEP1 shown to be one such peptide.
Why it matches plant phenotyping methods植物の栄養吸収率を測定する高スループット表現型解析プラットフォームを開発し、合成ペプチドのスクリーニングに適用しているため、測定手法が研究の中心である。
abstractHere we describe a high throughput phenotyping platform for testing SSP effects on root uptake of multiple nutrients.
Reproduction assets foundThe paper's nutrient uptake rate calculations were performed with R code publicly available on Zenodo (Griffiths et al., 2021), which the authors state they used with minor modifications for this paper's phenotyping analysis. The NCBI BioProject (PRJNA764762) is an RNA-seq omics deposit and is excluded per criteria; TrCode · publicdata processing to determine specific nutrient uptake rates was conducted using R version 3.6.0 (Team, 2020)( R Core Team, 2020 ) with minor modification to the R code available at https://doi.org/10.5281/zenodo.3893945 ( Griffiths et al., 2021 )Open asset ↗zenodo · 10.5281/zenodo.3893945lines:318-326Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modelling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang–Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose–responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose–response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度温度データと反復草丈データから小麦の成長・温度応答形質を抽出するモデルを開発・評価しており、表現型抽出手法が研究の中心である。
titlePhenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the source code supporting its phenotyping analysis (dose–response extraction from wheat height and temperature data) in a public ETH GitLab repository, archived in the ETH Research Collection with a DOI. Both URLs are allowed and the repository/identifier verCode · publicting—original draft. H.-P.P.: Conceptualization,
methodology, writing—review & editing. A.H.: Conceptualization,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 20Open asset ↗gitlab.ethz.ch/crop_phenotyping/htfp_data_processingpdf-raw-page:13 lines:1-88Code · publication,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 2000. Bases and limits to using ‘degree.day’
units. European Journal of Agronomy 13:1–10. doi:10.1016/
SOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:13 lines:1-88Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum reflects the biochemical composition within a tissue, under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been successfully applied in several cereal species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. We found that the co-inertia between spectra and genomic data was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, there was a correlation across traits between predictive ability of genomic and phenomic prediction, with a slope around 1 and an intercept of −0.2, thus suggesting that phenomic prediction could be applied for any trait.
Why it matches plant phenotyping methodsスペクトルに基づくフェノミック予測をブドウで適用し、複数組織・年・集団・形質で遺伝予測との性能比較を行っており、表現型取得・予測手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits.
Reproduction assets foundThe paper's Data availability statement deposits spectra, R scripts, and result tables in the INRAE data portal (DOI 10.15454/BICRFX), and genotypic values/genotypic data at DOI 10.15454/PNQQUQ. Both are paper-specific, public, and actionable.Dataset · publicyear of phenotyping and spectra measurement
are the same. Still, PP has shown its interest for
breeding over a wide range of traits.
Data availability
All analyses were conducted using free and open-
source software, mostly R. Genotypic values and
genotypic data for half-diallel and diversity panel
populations are available at https://doi.org/10.15454/PNQQUQ. Spectra, R scripts and result
tables have been deposited in the INRAE data
15
.
CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this versionOpen asset ↗10.15454/PNQQUQpdf-raw-page:15 lines:1-97Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider ( Petauroides volans ), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50-1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、樹冠レベル・林分スケールの葉面窒素を推定・検証する手法が研究の中心であり、植物形質の取得方法に該当する。
abstractWe tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale
Reproduction assets foundThe paper's data availability statement deposits all datasets and analysis scripts on Dryad (public DOI), while UAV imagery, point clouds and raster data are only available upon request. The Victorian Biodiversity Atlas is a third-party public database of animal observations, not a paper-specific phenotyping asset.Dataset · publicAll other datasets and scripts are available on Dryad via https://doi.org/10.5061/dryad.k0p2ngf9d .Open asset ↗Dryad · 10.5061/dryad.k0p2ngf9dlines:600-681Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC ( r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination ( R 2 ) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるイネの窒素状態・高NUE表現型の非破壊かつ高スループットな推定手法を開発・検証しており、表現型取得法が研究の中心である。
abstractfew easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD)
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 ), two main aspects were considered: (1) Many varieties from the 3,000 rice genome project were germplasm collections and not used in field practices because of their lower-yielding, varied GD, and/or weak agricultural traits.Open asset ↗lines:380-387Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.
Why it matches plant phenotyping methods衛星データから作物形質を推定し、GPRによる補間・時系列化とGEE上の再利用可能な処理ワークフローを構築・検証しており、フェノタイピング手法が中心である。
abstractthe building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning
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 following link contains a repository with demo codes of the different procedures used in this paper https://github.com/msalinero/GEEGPRPhenoDemos.git .Open asset ↗GEEGPRPhenoDemoslines:353-362Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.
Why it matches plant phenotyping methods葉・キャノピーの蛍光、反射、植生指数を用いて植物の光合成機能を測定・統合した再利用可能なデータセットであり、センサー計測系とデータ処理が研究の中心である。
abstractWith this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP).
Reproduction assets foundThis Data in Brief article explicitly deposits its maize leaf/canopy fluorescence, reflectance, VI and GEP time-series dataset (raw and collated files) in Mendeley Data under DOI 10.17632/b84jk376c3.1, and the FloX reflectance/SIF processing was performed with two author-maintained open-source R packages on GitHub (tomDataset · publicral Research Center (BARC)
City/Town/Region: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3Open asset ↗Mendeley Data · 10.17632/b84jk376c3.1lines:36-69Dataset · publicion: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3390/rs11050488 ]
Yang, P., C. van der TolOpen asset ↗Mendeley Data · b84jk376c3lines:36-69Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated ( R 2 > 0.7 in ∼34% of the metabolites) and predicted ( Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から代謝物プロファイルを非破壊推定する手法を統計モデルで評価しており、植物の生理状態の推定が中心的な方法的貢献として記述されている。
abstractLeaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions.
Reproduction assets foundThe article's Supplementary Data 1 publicly provides best linear unbiased estimators for all fiber, metabolite, hyperspectral, and vegetation index measurements of this study, accessible via the Frontiers supplementary-material page. No author analysis code or trained model deposit is mentioned.Dataset · publicSupplementary Data 1
Best linear unbiased estimators of single accessions in the 2 years of the field experiment for all the fiber yield/quality data, metabolites, hyperspectral data, and vegetation indices.Open asset ↗lines:577-642Supplement · publicSupplementary Table 2
Repeatability values and significance of fixed effects from the linear mixed models for the fiber traits of the 22 cotton accessions in 2018.Open asset ↗lines:577-642Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Why it matches plant phenotyping methods小麦のハイパースペクトル反射から生理・光合成形質を推定する深層学習モデルを開発・比較し、精度を検証した研究であり、表現型取得・推定法が中心である。
abstractHere we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.
Reproduction assets foundThe paper publicly releases its authors' model code (GitHub) and hosts the training data and pre-trained models via the Wheat Physiology Predictor web server. The SAMS repository is a generic third-party tool and is excluded.Code · publicThe full code of these models is located at https://github.com/ashwhall/hyperspec-trait-prediction .Open asset ↗ashwhall/hyperspec-trait-predictionlines:132-148Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was constructed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, respectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
Why it matches plant phenotyping methods植物機能形質LMAを可視・近赤外観測から推定する3D植生指数行列を開発し、合成データおよび大規模実験データで検証しており、表現型取得手法が中心である。
abstractIn this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation.
Reproduction assets foundThe paper's two experimental phenotyping datasets (LOPEX leaf spectra/LMA and the Madison, WI leaf spectra dataset) are explicitly stated to be publicly available on EcoSIS with direct URLs in the Data Availability Statement. No author analysis code or trained model is shared.Dataset · publicResearch Funds for the Central Universities, China Uni-
versity of Geosciences, Wuhan (grant number 111-G1323520290). T.T. was funded by SNSA (Dnr
96/16) and the EU-Aid-funded CASSECS project.
Data Availability Statement: All data used in this manuscript are publicly available through EcoSIS
spectral database, including LOPEX (https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-) and MA (https://ecosis.org/package/7433af7d-fbbd-4617-8df4-4d892f0d4357).Acknowledgments: We thank the open access to the LOPEX and MA datasets, as well as the PRO-
SPECT-D model.
Conflicts of Interest: The authors declare no conflict of interest. Authors are aware of and comply
with bestOpen asset ↗EcoSIS · leaf-optical-properties-experiment-database--lopex93-pdf-raw-page:13 lines:1-51Dataset · publicant number 111-G1323520290). T.T. was funded by SNSA (Dnr
96/16) and the EU-Aid-funded CASSECS project.
Data Availability Statement: All data used in this manuscript are publicly available through EcoSIS
spectral database, including LOPEX (https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-) and MA (https://ecosis.org/package/7433af7d-fbbd-4617-8df4-4d892f0d4357).Acknowledgments: We thank the open access to the LOPEX and MA datasets, as well as the PRO-
SPECT-D model.
Conflicts of Interest: The authors declare no conflict of interest. Authors are aware of and comply
with best practices in publication ethics specifically about authorship (avoidance of guest authorOpen asset ↗EcoSIS · 7433af7d-fbbd-4617-8df4-4d892f0d4357pdf-raw-page:13 lines:1-51Code / dataset availability confirmedCrossref · checked 9 Sept 2026
This research reports the findings of a Landsat Next expert review panel that evaluated the use of narrow shortwave infrared (SWIR) reflectance bands to measure ligno-cellulose absorption features centered near 2100 and 2300 nm, with the objective of measuring and mapping non-photosynthetic vegetation (NPV), crop residue cover, and the adoption of conservation tillage practices within agricultural landscapes. Results could also apply to detection of NPV in pasture, grazing lands, and non-agricultural settings. Currently, there are no satellite data sources that provide narrowband or hyperspectral SWIR imagery at sufficient volume to map NPV at a regional scale. The Landsat Next mission, currently under design and expected to launch in the late 2020’s, provides the opportunity for achieving increased SWIR sampling and spectral resolution with the adoption of new sensor technology. This study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents. These spectra were processed to generate narrow bands with centers at 2040, 2100, 2210, 2260, and 2230 nm, at various bandwidths, that were subsequently used to derive 13 NPV spectral indices from each spectrum. For crop residues with minimal green vegetation cover, two-band indices derived from 2210 and 2260 nm bands were top performers for measuring NPV (R2 = 0.81, RMSE = 0.13) using bandwidths of 30 to 50 nm, and the addition of a third band at 2100 nm increased resistance to atmospheric correction residuals and improved mission continuity with Landsat 8 Operational Land Imager Band 7. For prediction of NPV over a full range of green vegetation cover, the Cellulose Absorption Index, derived from 2040, 2100, and 2210 nm bands, was top performer (R2 = 0.77, RMSE = 0.17), but required a narrow (≤20 nm) bandwidth at 2040 nm to avoid interference from atmospheric carbon dioxide absorption. In comparison, broadband NPV indices utilizing Landsat 8 bands centered at 1610 and 2200 nm performed poorly in measuring fractional NPV (R2 = 0.44), with significantly increased interference from green vegetation.
Why it matches plant phenotyping methodsSWIRバンドとスペクトル指数を用いて非光合植生・作物残渣被覆を測定する手法を開発・比較評価しており、植物状態の取得方法が研究の中心である。
abstractThis study employed hyperspectral data collected from 916 agricultural field locations with varying fractional NPV, fractional green vegetation, and surface moisture contents.
Reproduction assets foundThe paper's core phenotyping input — the 916 agricultural field surface reflectance spectra used to derive NPV indices — is published as a USGS data release (reference 44) with a public DOI. No author analysis code or trained models are stated as available. Other URLs (Earth Explorer WV3 imagery, CTIC, NGAC, Auscope) pDataset · publicHively, W.D.; Lamb, B.T.; Daughtry, C.S.T.; Serbin, G.; Dennison, P. Reflectance Spectra of Agricultural Field Conditions
Supporting Remote Sensing Evaluation of Non-Photosynthetic Vegetative Cover. 2021. (U.S. Geological Survey Data Release.
Available online: https://doi.org/10.5066/P9XK3867 (accessed on 14 September 2021).Open asset ↗U.S. Geological Survey Data Release · 10.5066/P9XK3867pdf-page:31 lines:1-53Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から植物の干ばつストレスおよび関連形質を推定する手法を開発・検証しており、独立圃場試験での検証も含むため、表現型取得法が中心である。
abstractLeaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response.
Reproduction assets foundThe authors deposited the full raw hyperspectral/phenotype dataset on EcoSIS (DOI 10.21232/UTK8zaW4.669) and the supplementary dataset containing raw gas exchange and leaf metabolic trait data (DOI 10.21232/UTK8zaW4.665). Both are public, paper-specific phenotype/spectral datasets directly reproducing the paper's PLSR/Dataset · public.
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The full raw dataset accompanying this manuscript is available online at EcoSIS (ecosis.org) at
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This work was supported by the United States Department of Energy contract No. DE-
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SC0012704 to Brookhaven National Laboratory. We thank A. Brinton, M. J. B. Burnett, E.
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O’Connor, G. Hilles, K. Scanlon and D. Yang for assisting with data collection in the glasshouse;
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Supplementary Figure 1. Example workflow for visual identification of drought.
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SC0012704 to Brookhaven National Laboratory. We thankOpen asset ↗EcoSIS · 10.21232/UTK8zaW4.665pdf-raw-page:36 lines:1-44Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.
Why it matches plant phenotyping methods葉画像から窒素状態を推定するコンピュータビジョン手法の開発・評価が中心であり、植物生理状態の表現型取得法に該当する。
abstractIn this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops.
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 · publicA software release of the proposed
vision-based framework for N-nutrient estimation in crops is made publicly available on
the project website: http://faculty.pucit.edu.pk/~farid/Research/GCV.html, accessed on
8 June 2021.Open asset ↗pdf-page:16 lines:1-55Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation. Results Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H 2 ) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach. Conclusions A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H 2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.
Why it matches plant phenotyping methodsµXRF画像の植物組織分割と、金属蓄積空間パターンを定量化する指標を開発・検証しており、植物表現型の取得・抽出手法が中心である。
abstractWe developed an image-processing method to segment different plant substructures in the µXRF images.
Reproduction assets foundThe authors explicitly state that the analysis code created for this study (µXRF image segmentation and CQ/heritability analysis) is publicly available in their GitHub repository. The phenotype datasets themselves are only available on request from the corresponding author, so they are noted as a request-only asset. NoCode · publicCode is available at https://github.com/LucasYEAST/noccaea .Open asset ↗LucasYEAST/noccaealines:211-272Code / 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 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 confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Chlorophyll fluorescence (ChlF) is a powerful non‐invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high‐throughput field phenotyping capabilities. The light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( ) and the kinetics of electron transport measured by reoxidation rates ( and ). Short‐ and long‐term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, and were little affected, while was consistently accelerated in water‐limited compared to well‐watered plants, increasingly so with rising vapour pressure deficit. This high‐throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.
Why it matches plant phenotyping methodsLIFTセンサーを用いた高スループットな圃場キャノピー蛍光計測が研究の中心であり、光合成形質を定量するフェノタイピング手法を実質的に適用している。
abstractThe light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance.
Reproduction assets foundThe paper's raw and processed/cleaned LIFT chlorophyll fluorescence and spectral phenotyping datasets for both growing seasons are openly deposited on Zenodo (DOI 10.5281/zenodo.4305673), as stated in the methods and data availability statement. TERRA-REF is only cited as the meteorological data provider (infraction: aDataset · public) and 77,946 (97%) ChlF
transients in Y1 and Y2, respectively, were averaged, resulting in one
value per trait per plot per time of measurement (N = 5,544 data
points per trait in Y1; and N = 4,032 data points per trait in Y2). The
raw data and the processed and cleaned datasets for both growing
seasons are publicly accessible (https://doi.org/10.5281/zenodo.4305673).2.9 | Statistical analysis
A linear mixed model (LMM) approach was used to analyse the resolv-
able row-column designs with repeated measures for both Y1 and Y2.
Single-stage analysis models were applied to partition variance com-
ponents and to estimate genotypic effects for all traits based on “Best
Linear Unbiased PredictioOpen asset ↗Zenodo · 10.5281/zenodo.4305673pdf-raw-page:6 lines:1-96Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
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fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
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montage image was then created to quickly estimate segmentation quality. The imageJ macro
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and sample images are available in the Zenodo repository,
572
http://doi.org/10.5281/zenodo.4809980.573
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AI Segmentation
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The Machine learning approach is based on the “Waikato Environment for Knowledge
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Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
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classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
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middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.
Why it matches plant phenotyping methods圃場分光データとSentinel-2模擬データを用いて、植物体N関連形質を推定する手法を比較・評価しており、形質取得・推定手法が研究の中心である。
abstractTherefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA.
Reproduction assets foundThe authors state the field-spectrometer spectral library and plant trait measurements (N conc, Chl AB, LAI, LAI-scaled traits) used in this study are openly available via an ETH research collection DOI, which is an allowed URL. This is a paper-specific, public, actionable phenotype/spectral dataset.Dataset · publicThe data presented in this study are openly available in: https://doi.org/10.3929/ethz-b-000488405 . Please also see the ‘ supplementary materials – dataset ’ for more information on the dataset.Open asset ↗ethz-b-000488405 · 10.3929/ethz-b-000488405lines:301-314Code / 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 confirmedEurope PMC · checked 9 Sept 2026
Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study.
Why it matches plant phenotyping methodsトウモロコシの光遮断係数kとfPARiを推定する統計手法を比較・評価し、植物キャノピー形質の再現可能な推定ワークフローとして推奨手法を提示しているため、方法論が中心である。
abstractThe parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution.
Reproduction assets foundThe paper's authors explicitly state that the R code implementing the k-estimation analysis (LSE, LogTLM, MLE, Bayesian estimation) is freely available in a public GitHub repository. The underlying phenotype datasets (fPARi/LAI measurements for seven maize genotypes) are only available from the corresponding author on,Code · publicR code is freely available at https://github.com/jlacasa/k-estimation/blob/main/k_estimation_02182021.Rmd .Open asset ↗https://github.com/jlacasa/k-estimation · k_estimation_02182021.Rmdlines:172-210Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
Why it matches plant phenotyping methods植物形質をハイパースペクトル反射データから推定する手法をレビューし、トウモロコシの新規サンプルで既存モデルを評価しており、表現型取得・推定法が中心である。
abstractHere, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' spectral reflectance data and ground truth phenotyping measurements in a public repository (Zenodo-style DOI 10.21232/y5TTxY3N), which is an allowed URL. This is a paper-specific, publicly actionable hyperspectral phenotyping dataset.Dataset · publicd the potential for reusable genotypic datasets, that makes the potential of hyperspectral reflectance phenotyping to both expand our current genetic knowledge and address the challenges of breeding for the 21st century so exciting.
Data availability
Spectral reflectance data and ground truth measurements have been deposited in https://doi.org/10.21232/y5TTxY3N .
Funding
This research was supported by the Office of Science (BER), 10.13039/100000015 U.S. Department of Energy , grant no. DE-SC0020355 to J.C.S. and Y.G., the 10.13039/100000001 National Science Foundation under grant OIA-1557417 to Y.G. and J.C.S. and OIA-1826781 to J.C.S. This project was completed utilizing the HollandOpen asset ↗10.21232/y5TTxY3Nlines:311-337Code / 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 confirmedOpenAlex · Crossref · checked 14 Sept 2026
Digital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments. A fast and non-destructive approach can allow rapid decision making to breed and select relevant rice varieties. Hence, this study compares the predictive potential of near-infrared (NIR) spectroscopy for three physical forms of rice i.e., rice kernel (with glume), whole grain brown rice and powdered rice. The aim is to identify the best physical form to be adapted in future use for high-throughput protein content prediction in rice samples. The models were optimized by selecting key wavelengths most correlated to the protein content in rice. For variable selection, a total of 8 recently developed chemometric variable selection techniques were used. As a baseline comparison to variable selection techniques, partial-least square (PLS) regression analysis was used. The results showed that for all forms of rice samples, variable selection improved the predictive performance compared to the PLS regression modelling. The best accuracies were obtained for the brown rice samples with a prediction error of 0.349%. Further, this was achieved with only 12 wavelengths compared to the 304 wavelengths available in the original data set. Based on the results, this study indicates that there is no need to grind the rice samples into powder for using NIR spectroscopy. Hence, NIR spectroscopy can directly be used on brown rice samples and can support the rapid assessment of protein content in rice to support digital phenotyping.
Why it matches plant phenotyping methodsNIR分光と波長選択・回帰モデルを用いて、玄米のタンパク質含量を非破壊かつ高スループットに推定する方法を比較・最適化しており、植物表現型取得が研究の中心である。
abstractDigital rice phenotyping requires rapid assessment of protein content of rice to kernels to support high-throughput crop phenotyping experiments.
Reproduction assets foundThe paper's NIR spectra and reference protein content dataset (201 rice samples in three physical forms) is explicitly stated to be publicly accessible on Mendeley Data. No author analysis code or trained models are reported as available.Dataset · publicThe original data set used in this study is/are accessible at: https://
data.mendeley.com/datasets/zvgy65m2rc/1.data.mendeley.com · zvgy65m2rc/1pdf-raw-page:3 lines:1-78Code / 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 9 Sept 2026
Abstract Background Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc, and nickel with substantial genetic variation at the population-level. X-ray Fluorescence microscopy (µXRF) is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation. Results Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad sense heritabilities (H 2 ) ranging from 76–92% correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach. Conclusions A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H 2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.
Why it matches plant phenotyping methodsµXRF画像から植物器官を自動分割し、金属蓄積パターンを定量する画像処理法と指標を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed an image-processing method to segment different plant substructures in the µXRF images.
Reproduction assets foundThe authors' image-processing/heritability analysis code for the µXRF plant phenotyping is explicitly deposited in a public GitHub repository. The phenotype datasets themselves are only available on request, and GeoPIXE is a generic third-party tool, not a paper-specific asset.Code · publicCode is
available at https://github.com/LucasYEAST/noccaea.Open asset ↗LucasYEAST/noccaeapdf-page:29 lines:1-48Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals’ fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote‐sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote‐sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large‐scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: (1) image segmentation, to identify individual trees and estimate structural traits; (2) an ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and (3) predictions for segmented crowns for the full remote‐sensing footprint at the NEON sites. The R 2 values on held‐out test data ranged from 0.41 to 0.75 on held‐out test data. The ensemble approach performed better than single partial least‐squares models. Carbon performed poorly compared to other traits ( R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over‐segmentation. The pipeline produced good estimates of DBH ( R 2 of 0.62 on held‐out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between 0.07 and 0.26. We used the pipeline to produce individual‐level trait data for ~5 million individual crowns, covering a total extent of ~360 km 2 . This large data set allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.
Why it matches plant phenotyping methods個体樹木の画像分割、ハイパースペクトル推定、アロメトリーを統合し、構造形質・葉形質を大規模に推定する手法を開発・適用しており、植物フェノタイピング手法が中心である。
abstractWe used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites.
Reproduction assets foundThe paper's Data Availability section deposits three paper-specific public assets on Zenodo: the authors' analysis code, the derived crown-level trait dataset for ~5 million trees, and the trait/input data with metadata. All are directly tied to this paper's phenotyping measurements and analysis.Code · publicgle tree extraction by exploiting airborne full-
waveform LiDAR data. Remote Sensing of Environment
123:368–380.
SUPPORTING INFORMATION
Additional supporting information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as describedOpen asset ↗Zenodo · 10.5281/zenodo.3991797pdf-raw-page:15 lines:1-105Dataset · publicrmation may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
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https://esajournals.onlinelibrary.wiley.Open asset ↗Zenodo · 10.5281/zenodo.3991815pdf-raw-page:15 lines:1-105Dataset · publicanalyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
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(https://onlinelibrary.wileOpen asset ↗Zenodo · 10.5281/zenodo.4434481pdf-raw-page:15 lines:1-105Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Summary The root economics space is a useful framework for plant ecology but is rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory, utilizing genetic variation, high‐throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images. We uncovered substantial variation in specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and structural costs. Multiple linear regression analysis indicated that lateral root tips had the greatest SRR, and the residuals from this model were used as a new trait. Specific root respiration was negatively correlated with plant mass. Network analysis, using a Gaussian graphical model, identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with SRR, SRL, and root branching frequency, and proposed gene candidates. Combining functional phenomics and root economics is a promising approach to improving our understanding of crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と構造を対象に、CO2フラックスの新規ハイスループット法と画像解析ソフトウェアを用いた機能的フェノミクスを中心的に実施しており、植物形質取得法が研究の主要部分である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high‐throughput method for CO 2 flux and the open‐source software RhizoVision Explorer to analyze scanned images.
Reproduction assets foundThe paper explicitly deposits its trait data, GEMMA GWAS output, and R analysis scripts at Zenodo (10.5281/zenodo.4247894), and separately deposits the root respiration measurement protocol and flux-calculation R scripts at Zenodo (10.5281/zenodo.4247873). Both are paper-specific, public, and actionable. The Triticeae-Dataset · publicAll trait data, gemma output, and R analysis scripts necessary for the statistical analysis and plotting are publicly available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b ).Open asset ↗Zenodo · 10.5281/zenodo.4247894lines:608-654Code · publicThe protocol for the root respiration measurements and the R script for calculating total flux from a directory of text files are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al.,
2020a ).Open asset ↗Zenodo · 10.5281/zenodo.4247873lines:85-97Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Brassica oleracea is an important crop species that at early growth stages may exhibit failure of the apical growing point, an abnormality called “blindness”. The occurrence of blindness is promoted by exposure to low temperatures during imbibition and germination, but the causes of sensitivity to such conditions are unknown. We combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness. For image analysis, we used the VideometerLab instrument, which can scan 19 wavelengths from ultraviolet to infrared and utilize that information in any combination to potentially identify unique criteria related to seed quality. The iXeed CF Analyzer was utilized to obtain chlorophyll fluorescence values for individual seeds. Chlorophyll contents of many seeds can be used as an indicator of seed maturity, a major contributor to seed quality. Finally, oxygen consumption measurements of individual seeds as obtained with the Q2 instrument are highly correlated with their performance under a wide variety of conditions. Six Brassica seed lots differed in their susceptibility to induction of blindness or loss of viability due to 48 h hydrated incubation at 1.5 ∘C. Analysis of physical and respiratory parameters identified some measurements that were highly correlated with the occurrence of blindness. Higher chlorophyll content, as detected by the CF-Mobile and certain wavelengths in the Videometer, was associated with greater occurrence of blindness or death following the induction treatment, suggesting that more immature seeds may be susceptible to blindness. Further research is required, but methods to detect and sort such seeds based on physical characteristics appear to be feasible.
Why it matches plant phenotyping methods種子の画像・蛍光・呼吸測定を組み合わせ、物理特性から発芽品質やblindness感受性を評価・選別する方法が研究の中心であり、単なる生物学的結果測定ではない。
abstractWe combined three analytical seed technology instruments to explore seed physical properties that are highly correlated with quality parameters and might be used directly for grading or sorting seed lots into subpopulations varying in potential susceptibility to blindness.
Reproduction assets foundThe paper's individual-seed phenotyping measurements (chlorophyll fluorescence, multispectral imaging, Q2 respiration, plant blindness scores) are consolidated in Supplemental Table S1 (Seed parameters database) and related supplements, publicly hosted on the MDPI article site. No author analysis code was deposited; CRDataset · publicSupplementary Materials: The following are available at https://www.mdpi.com/2077-0472/11/3
/220/s1, Table S1: Seed parameters database, Table S2: Q2 parameters, Table S3: MFA EigenvaluesOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.
Why it matches plant phenotyping methodsNIR分光によるサトウキビの繊維・ショ糖含量という植物品質形質の推定性能を、ゲノム予測および統合モデルと比較検証しており、形質取得・推定手法が研究の中心である。
abstractThe main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data
Reproduction assets foundThe paper's underlying phenotype (fiber and sucrose content BLUPs), NIR spectra, and SNP marker data for the 385 sugarcane clones are publicly deposited on figshare, as stated in the Data Availability statement. No author analysis code repository is mentioned. The figshare DOI appears in the text but its URL is not in;Dataset · publicData Availability: The data underlying the results presented in the study are available from 10.6084/m9.figshare.12635717 .Open asset ↗figshare · 10.6084/m9.figshare.12635717lines:155-167Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal. Better predictive performance of the multivariate analysis method can be obtained when the information from differently scatter corrected data is jointly used. To demonstrate it, an open-source NIR spectroscopy data set related to protein prediction in wheat kernels was used. Two different pre-processing fusion approaches i.e., sequential and parallel fusion, were used for fusing the complementary information from four different scatter correction techniques, namely standard normal variate (SNV), variable sorting for normalisation (VSN), 2nd derivative, and multiplicative scatter correction (MSC). As a comparison, partial least-squares regression (PLSR) was performed on the SNV pre-processed data. The results showed that fusion of scatter correction can improve the predictive performance of NIR spectroscopic models. The results revealed that both sequential and parallel fusion approaches improved the predictive performance compared to the PLSR performed using a single scatter correction technique. The R²ₚ was improved by up to 3% and the RMSEP was reduced by up to 13% compared to the results obtained with conventional PLSR model developed with a single scatter correction technique.
Why it matches plant phenotyping methods小麦粒のタンパク質含量という植物形質を対象に、NIRスペクトルの散乱補正融合と予測性能を検証しており、形質取得・推定手法が研究の中心である。
abstractThe study aims to test the hypothesis that modelling of near-infrared (NIR) spectroscopic data based on a single scatter correction technique is sub-optimal.
Reproduction assets foundThe paper's analysis is built entirely on an open NIR spectroscopy dataset of 523 wheat kernels with reference protein content, publicly deposited on Figshare and explicitly linked by the authors. No author analysis code is stated as publicly available.Dataset · publicntional (single) scatter
correction technique, partial least-squares regression (PLSR)
was performed individually pre-processed data.
2. Materials and methods
2.1. Data set
The wheat kernel data set used in this study was obtained
from the Mendeley repository of open data sets (Wenya, 2016).
The data set can also be accessed at https://figshare.com/articles/wheat_kernel_dataset/4252217/1. The data set con-
tains NIR spectra and reference protein concentration of 523
wheat kernels. The spectra were measured in the spectral
range of 850e1050 nm with a total of 100 wavelengths (nm). In
this analysis, the data set was divided into calibration (60%)
and test set (40%) using the Kennard-Stone (KS)Open asset ↗Figshare · 4252217pdf-raw-page:2 lines:1-87Code / 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 confirmedCrossref · Europe PMC · checked 9 Sept 2026
Basil is one of the most widespread aromatic and medicinal plants, which is often grown in drought- and salinity-prone regions. Often co-occurrence of drought and salinity stresses in agroecosystems and similarities of symptoms which they cause on plants complicates the differentiation among them. Development of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis. In this study, we have used different phenotyping techniques including chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning, aiming to quantify changes in basil phenotypic traits under early and prolonged drought and salinity stress and to determine traits which could differentiate among drought and salinity stressed basil plants. Ocimum basilicum “Genovese” was grown in a growth chamber under well-watered control [45–50% volumetric water content (VWC)], moderate salinity stress (100 mM NaCl), severe salinity stress (200 mM NaCl), moderate drought stress (25–30% VWC), and severe drought stress (15–20% VWC). Phenotypic traits were measured for 3 weeks in 7-day intervals. Automated phenotyping techniques were able to detect basil responses to early and prolonged salinity and drought stress. In addition, several phenotypic traits were able to differentiate among salinity and drought. At early stages, low anthocyanin index (ARI), chlorophyll index (CHI), and hue (HUE 2 D ), and higher reflectance in red (R Red ), reflectance in green (R Green ), and leaf inclination (LINC) indicated drought stress. At later stress stages, maximum fluorescence (F m ), HUE 2 D , normalized difference vegetation index (NDVI), and LINC contribute the most to the differentiation among drought and non-stressed as well as among drought and salinity stressed plants. ARI and electron transport rate (ETR) were best for differentiation of salinity stressed plants from non-stressed plants both at early and prolonged stress.
Why it matches plant phenotyping methods複数の自動フェノタイピング技術を用いて、形態・生理形質を統合的に定量し、乾燥・塩ストレスの早期検出と識別を評価しており、フェノタイピング手法の応用が研究の中心である。
abstractDevelopment of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis.
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
Analysis of variance (ANOVA) for measured phenotypic traits of basil grown in different treatments: control (C), moderate salinity stress (S1), severe salinity stress (S2), moderate drought (D1), and severe drought (D2).Open asset ↗lines:494-517Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Plants have evolved several adaptive mechanisms to cope with water-limited conditions. While most of them are through constitutive traits, certain "acquired tolerance" traits also provide significant improvement in drought adaptation. Most abiotic stresses, especially drought, show a gradual progression of stress and hence provide an opportunity to upregulate specific protective mechanisms collectively referred to as "acquired tolerance" traits. Here, we demonstrate a significant genetic variability in acquired tolerance traits among rice germplasm accessions after standardizing a novel gradual stress progress protocol. Two contrasting genotypes, BPT 5204 (drought susceptible) and AC 39000 (tolerant), were used to standardize methodology for capturing acquired tolerance traits at seedling phase. Seedlings exposed to gradual progression of stress showed higher recovery with low free radical accumulation in both the genotypes compared to rapid stress. Further, the gradual stress progression protocol was used to examine the role of acquired tolerance at flowering phase using a set of 17 diverse rice genotypes. Significant diversity in free radical production and scavenging was observed among these genotypes. Association of these parameters with yield attributes showed that genotypes that managed free radical levels in cells were able to maintain high spikelet fertility and hence yield under stress. This study, besides emphasizing the importance of acquired tolerance, explains a high throughput phenotyping approach that significantly overcomes methodological constraints in assessing genetic variability in this important drought adaptive mechanism.
Why it matches plant phenotyping methodsイネの乾燥適応形質を評価するための段階的ストレス付与プロトコルを標準化し、高スループット表現型解析として方法論的制約を克服する手法を提示しているため、表現型取得法が中心的です。
abstractafter standardizing a novel gradual stress progress protocol
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 ) during the Kharif season of 2019 to confirm the trait diversity, particularly for acquired tolerance traits.Open asset ↗lines:351-362Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Arbuscular mycorrhiza fungi (AMF) are beneficial soil fungi that can promote the growth of their host plants. Accurate quantification of AMF in plant roots is important because the level of colonization is often indicative of the activity of these fungi. Root colonization is traditionally measured with microscopy methods which visualize fungal structures inside roots. Microscopy methods are labor-intensive, and results depend on the observer. In this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene. First, we validated the primer pair AMG1F and AM1 in silico, and we show that these primers cover most AMF species present in plant roots without amplifying host DNA. Next, we compared the relative qPCR method with traditional microscopy based on a greenhouse experiment with Petunia plants that ranged from very high to very low levels of AMF root colonization. Finally, by sequencing the qPCR amplicons with MiSeq, we experimentally confirmed that the primer pair excludes plant DNA while amplifying mostly AMF. Most importantly, our relative qPCR approach was capable of discriminating quantitative differences in AMF root colonization and it strongly correlated (Spearman Rho = 0.875) with quantifications by traditional microscopy. Finally, we provide a balanced discussion about the strengths and weaknesses of microscopy and qPCR methods. In conclusion, the tested approach of relative qPCR presents a reliable alternative method to quantify AMF root colonization that is less operator-dependent than traditional microscopy and offers scalability to high-throughput analyses.
Why it matches plant phenotyping methods植物根のAMF菌根 colonization を定量する相対qPCR法を開発・検証し、顕微鏡法との比較で性能を評価しているため、植物状態の取得手法が中心である。
abstractIn this study, we present a relative qPCR method to quantify AMF in which we normalized the AMF qPCR signal relative to a plant gene.
Reproduction assets foundThe paper provides two paper-specific public assets: the authors' analysis code repository on GitHub (R/DADA2/qPCR analysis workflow) and raw amplicon sequencing data deposited in the European Nucleotide Archive under study accession PRJEB20127 (sample SAMEA103939171), which contains the qPCR amplicon sequences used toCode · publicAll code is available under https://github.com/PMI-Basel/Bodenhausen_et_al_AMF_qPCR .Open asset ↗PMI-Basel/Bodenhausen_et_al_AMF_qPCRlines:90-98Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Crop improvement for Nitrogen Use Efficiency (NUE) requires a well-defined phenotype and genotype, especially for different N-forms. As N-supply enhances growth, we comprehensively evaluated 25 commonly measured phenotypic parameters for N response using 4 N treatments in six indica rice genotypes. For this, 32 replicate potted plants were grown in the green-house on nutrient-depleted sand. They were fertilized to saturation with media containing either nitrate or urea as the sole N source at normal (15 mM N) or low level (1.5 mM N). The variation in N-response among genotypes differed by N form/dose and increased developmentally from vegetative to reproductive parameters. This indicates survival adaptation by reinforcing variation in every generation. Principal component analysis segregated vegetative parameters from reproduction and germination. Analysis of variance revealed that relative to low level, normal N facilitated germination, flowering and vegetative growth but limited yield and NUE. Network analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype. It constitutes germination and flowering, shoot/root length and biomass parameters, six of which were common to nitrate and urea. Field-validation confirmed the NUE differences between two genotypes chosen phenotypically. The correspondence between multiple approaches in shortlisting parameters for NUE makes it a novel and robust phenotyping methodology of relevance to other plants, nutrients or other complex traits. Thirty-Four N-responsive genes associated with the phenotype have also been identified for genotypic characterization of NUE.
Why it matches plant phenotyping methodsNUEの複合形質を定義・選抜するため、複数形質の評価、特徴選択、統計解析、フィールド検証を統合したフェノタイピング手法が中心的に開発・検証されている。
abstractNetwork analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype.
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 3
Mean values of the measured 25 phenotypic parameters in nitrate/urea sources and normal (15 mM) or low (1.5 mM) doses.Open asset ↗lines:557-644Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Nutrient uptake is critical for crop growth and is determined by root foraging in soil. Growth and branching of roots lead to effective root placement to acquire nutrients, but relatively little is known about absorption of nutrients at the root surface from the soil solution. This knowledge gap could be alleviated by understanding sources of genetic variation for short-term nutrient uptake on a root length basis. A modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.). Using this system, uptake rates were characterized for the crop macronutrients nitrate, ammonium, potassium, phosphate, and sulfate among the Nested Association Mapping (NAM) population founder lines. The data revealed substantial genetic variation for multiple ion-uptake rates in maize. Interestingly, specific nutrient uptake rates (nutrient uptake rate per length of root) were found to be both heritable and distinct from total uptake and plant size. The specific uptake rates of each nutrient were positively correlated with one another and with specific root respiration (root respiration rate per length of root), indicating that uptake is governed by shared mechanisms. We selected maize lines with high and low specific uptake rates and performed an RNA-seq analysis, which identified key regulatory components involved in nutrient uptake. The high-throughput multiple ion-uptake kinetics pipeline will help further our understanding of nutrient uptake, parameterize holistic plant models, and identify breeding targets for crops with more efficient nutrient acquisition.
Why it matches plant phenotyping methods根の複数イオン吸収速度を高スループットで測定するRhizoFluxプラットフォームを開発し、性能・遺伝的変異を評価しており、植物表現型取得法が研究の中心です。
abstractA modular platform called RhizoFlux was developed for high-throughput phenotyping of multiple ion-uptake rates in maize (Zea mays L.).
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 statistical analysis R codes including the packages needed are available at https://doi.org/10.5281/zenodo.3893944 ( Griffiths and York, 2020b )Open asset ↗Zenodo · 10.5281/zenodo.3893944lines:83-89Code / 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.
192
10 of 24
.
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 confirmedarXiv · checked 13 Sept 2026
Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is the leaf area index (LAI) if algorithms account for variability in the atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6S were used to simulate a look-up table (LUT) of TOA radiance data and associated input variables. This LUT was then used to train the Bayesian machine learning algorithms Gaussian processes regression (GPR) and variational heteroscedastic GPR (VHGPR). PROSAIL simulations were also used to train GPR and VHGPR models for LAI retrieval from S2 images at bottom-of-atmosphere (BOA) level (L2A product) for comparison purposes. The VHGPR models led to consistent LAI maps at BOA and TOA scale. We demonstrated that hybrid LAI retrieval algorithms can be developed from TOA radiance data given a cloud-free sky, thus without the need for atmospheric correction.
Why it matches plant phenotyping methodsSentinel-2のTOA放射輝度からLAIを推定する機械学習アルゴリズムを開発・比較しており、植物形質の取得手法が研究の中心である。
abstractWe demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework.
Reproduction assets foundThe paper's hybrid LAI retrieval (GPR/VHGPR) was developed within the authors' ALG-ARTMO software framework, and code snippets/demos for GPR and VHGPR are publicly available from the authors' UV-ES soft regression page. Both are explicitly stated as freely downloadable in the supplied text. No paper-specific phenotype/Code · publicCode snippets and demos for both GPR, VHGPR and other machine learning regression algorithms is available from https://isp.uv.es/soft_regression.html .Open asset ↗isp.uv.es/soft_regression.htmllines:485-521Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wet chemistry analysis of agricultural plant materials such as leaves is widely performed to quantify key chemical components to understand plant physiological status. Visible and near-infrared (Vis-NIR) spectroscopy is an interesting tool to replace the wet chemistry analysis, often labour intensive and time-consuming. Hence, this study accesses the potential of Vis-NIR spectroscopy to predict nitrogen (N) and potassium (K) concentration in bell pepper leaves. In the chemometrics perspective, the study aims to identify key Vis-NIR wavelengths that are most correlated to the N and K, and hence, improves the predictive performance for N and K in bell pepper leaves. For wavelengths selection, six different wavelength selection techniques were used. The performances of several wavelength selection techniques were compared to identify the best technique. As a baseline comparison, the partial least-square (PLS) regression analysis was used. The results showed that the Vis-NIR spectroscopy has the potential to predict N and K in pepper leaves with root mean squared error of prediction (RMSEP) of 0.28 and 0.44%, respectively. The wavelength selection in general improved the predictive performance of models for both K and N compared to the PLS regression. With wavelength selection, the RMSEP's were decreased by 19% and 15% for N and K, respectively, compared to the PLS regression. The results from the study can support the development of protocols for non-destructive prediction of key plant chemical components such as K and N without wet chemistry analysis.
Why it matches plant phenotyping methodsVis-NIR分光と波長選択・回帰モデルにより、ベル pepper葉のN・K濃度という植物形質を非破壊推定する方法の開発・比較検証が中心である。
abstractthis study accesses the potential of Vis-NIR spectroscopy to predict nitrogen (N) and potassium (K) concentration in bell pepper leaves.
Reproduction assets foundThe paper's Vis-NIR spectra of 119 dried bell pepper leaves with reference K and N measurements are explicitly stated to be freely available on EcoSIS, making it a public, paper-specific phenotyping dataset. The MATLAB Central link refers only to third-party generic wavelength-selection code, not authors' analysis codeDataset · publicsamples [32]. For spectral measurement, the powder of each leaf was
placed on the probe and covered with a black cover. All spectral data
ranged from 400 to 2400 nm with 5 nm resolution. The data set is freely
available at the official website of ecological spectral information system
(EcoSIS) and can be obtained with the link: https://ecosis.org/package
/fresh-and-dry-pepper-leaf-spectra-with-associated-potassium-and-nit
rogen-measurements.
2.2. Data analysis
The data were partitioned to calibration (60%) and test (40%) set
using the duplex algorithm [33]. The reflectance data were used directly
for the data processing as using chemometric pre-processing methods
may remove theOpen asset ↗EcoSISpdf-raw-page:2 lines:1-78Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
WheatRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
Summary The root economics space is a useful framework for plant ecology, but rarely considered for crop ecophysiology. In order to understand root trait integration in winter wheat, we combined functional phenomics with trait economic theory utilizing genetic variation, high-throughput phenotyping, and multivariate analyses. We phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images. We uncovered substantial variation for specific root respiration (SRR) and specific root length (SRL), which were primary indicators of root metabolic and construction costs. Multiple linear regression estimated that lateral root tips had the greatest SRR, and the residuals of this model were used as a new trait. SRR was negatively correlated with plant mass. Network analysis using a Gaussian graphical model identified root weight, SRL, diameter, and SRR as hub traits. Univariate and multivariate genetic analyses identified genetic regions associated with aspects of the root economics space, with underlying gene candidates. Combining functional phenomics and root economics is a promising approach to understand crop ecophysiology. We identified root traits and genomic regions that could be harnessed to breed more efficient crops for sustainable agroecosystems.
Why it matches plant phenotyping methods根の呼吸と形態を対象に、CO2フラックスの新規ハイスループット測定法と画像解析ソフトウェアを用いたフェノタイピングが研究の中心である。
abstractWe phenotyped a diversity panel of 276 genotypes for root respiration and architectural traits using a novel high-throughput method for CO 2 flux and the open-source software RhizoVision Explorer for analyzing scanned images.
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: (1) the root respiration measurement protocol and R script for computing CO2 flux from LI-850 text files (Zenodo 4247873), and (2) all trait data, GEMMA output, and R analysis scripts for the statistical analysis and plotting (Zenodo 4247894). Both are theCode · publicThe protocol for the root
respiration measurements and the R script for calculating total flux from a directory of text files
are available at https://doi.org/10.5281/zenodo.4247873 (Guo et al., 2020a).Open asset ↗Zenodo · 10.5281/zenodo.4247873pdf-page:8 lines:1-41Dataset · publicAll trait data, GEMMA output, and R analysis scripts necessary for doing the statistical
analysis and plotting are available at https://doi.org/10.5281/zenodo.4247894 (Guo et al.,
2020b).Open asset ↗Zenodo · 10.5281/zenodo.4247894pdf-page:12 lines:1-35Code / dataset availability confirmedEurope PMC · bioRxiv · checked 9 Sept 2026
Summary Soil is a heterogenous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically-based variation in the elemental composition ‘ionome’, of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool-IonExplorer to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory QTL, GWA studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.
Why it matches plant phenotyping methods大規模植物イオノーム取得のための高スループット実験・正規化パイプラインとデジタルツールを開発し、再利用可能なデータ資源として提示しているため、測定法が中心的です。
abstractWe present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection.
Reproduction assets foundThe paper's species-wide leaf and seed ionomic dataset for 1,135 A. thaliana accessions is made publicly accessible through the authors' interactive web tool Ion Explorer, which allows filtering, analysis, and download of the complete dataset as .csv files. This is a paper-specific, public, actionable asset. No author-Dataset · publicwe present an interactive web-based tool made available online: Ion Explorer
https://ffionexplorer.nottingham.ac.uk/ionmap/. Ion Explorer allows interactive visualisation,
analysis and comparison of the two large datasets.Open asset ↗Ion Explorerpdf-page:11 lines:1-44Code / 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
Grassland-based ruminant livestock production provides a sustainable alternative to intensive production systems relying on concentrated feeds. However, grassland-based roughage often lacks the energy content required to meet the productivity potential of modern livestock breeds. Forage legumes, such as red clover, with increased starch content could partly replace maize and cereal supplements. However, breeding for increased starch content requires efficient phenotyping methods. This study is unique in evaluating a non-destructive hyperspectral imaging approach to estimate leaf starch content in red clover for enabling efficient development of high starch red clover genotypes. We assessed prediction performance of partial least square regression models (PLSR) using cross-validation, and validated model performance with an independent test set under controlled conditions. Starch content of the training set ranged from 0.1 to 120.3 mg g -1 DW. The best cross-validated PLSR model explained 56% of the measured variation and yielded a root mean square error (RMSE) of 17 mg g -1 DW. Model performance decreased when applying the trained model on the independent test set (RMSE = 29 mg g -1 DW, R 2 = 0.36). Different variable selection methods did not increase model performance. Once validated in the field, the non-destructive spectral method presented here has the potential to detect large differences in leaf starch content of red clover genotypes. Breeding material could be sampled and selected according to their starch content without destroying the plant.
Why it matches plant phenotyping methodsレッドクローバー葉のデンプン含量という植物形質を、非破壊ハイパースペクトル画像とPLSRで推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractbreeding for increased starch content requires efficient phenotyping methods.
Reproduction assets foundThe paper's data availability statement points to a Zenodo deposit containing the study's spectral and starch datasets. The simplerspec R package (Baumann 2019) is cited as a generic tool, not a paper-specific asset; the pls package is a generic CRAN library.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/3598699#.XhOLvdko_d4 .Open asset ↗zenodo · 3598699lines:389-441Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Enhancing nitrogen fertilization efficiency for improving yield is a major challenge for smallholder farming systems. Rapid and cost-effective methodologies with the capability to assess the effects of fertilization are required to facilitate smallholder farm management. This study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe. Among the measurements made on individual leaves, chlorophyll readings were the best indicators for both N content in leaves (R < 0.700) and grain yield (GY) (R < 0.800). Canopy indices reported even higher correlation coefficients when assessing GY, especially those based on the measurements of the vegetation density as the green area indices (R < 0.850). Canopy measurements from both ground and aerial platforms performed very similar, but indices assessed from the UAV performed best in capturing the most relevant information from the whole plot and correlations with GY and leaf N content were slightly higher. Leaf-based measurements demonstrated utility in monitoring N leaf content, though canopy measurements outperformed the leaf readings in assessing GY parameters, while providing the additional value derived from the affordability and easiness of using a pheno-pole system or the high-throughput capacities of the UAVs.
Why it matches plant phenotyping methods葉・キャノピーのリモートセンシング手法を比較し、窒素含量や収量推定との相関を検証しており、植物表現型取得法が研究の中心である。
abstractThis study compares maize leaf and canopy-based approaches for assessing N fertilization performance under different tillage, residue coverage and top-dressing conditions in Zimbabwe.
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 · publicregions of interest corresponding to each plot were segmented and exported using the MosaicTool (Shawn C. Kefauver, https://integrativecropecophysiology.com/software-development/mosaictool/ , https://gitlab.com/sckefauver/MosaicTool , University of Barcelona, Barcelona, Spain) integrated as a plugin for FIJIOpen asset ↗sckefauver/MosaicToollines:152-165Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plant thermal tolerance is a crucial research area as the climate warms and extreme weather events become more frequent. Leaves exposed to temperature extremes have inhibited photosynthesis and will accumulate damage to photosystem II (PSII) if tolerance thresholds are exceeded. Temperature-dependent changes in basal chlorophyll fluorescence (T-F0) can be used to identify the critical temperature at which PSII is inhibited. We developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system. We examined how experimental conditions: wet vs dry surfaces for leaves and heating/cooling rate, affect CTMIN and CTMAX across four species. CTMAX estimates were not different whether measured on wet or dry surfaces, but leaves were apparently less cold tolerant when on wet surfaces. Heating/cooling rate had a strong effect on both CTMAX and CTMIN that was species-specific. We discuss potential mechanisms for these results and recommend settings for researchers to use when measuring T-F0. The approach that we demonstrated here allows the high-throughput measurement of a valuable ecophysiological parameter that estimates the critical temperature thresholds of leaf photosynthetic performance in response to thermal extremes.
Why it matches plant phenotyping methods葉の熱耐性・PSII機能の臨界温度を高スループットに測定する蛍光イメージング手法を開発・検証しており、表現型取得法が研究の中心である。
abstractWe developed and tested a high-throughput method for measuring the critical temperatures for PSII at low (CTMIN) and high (CTMAX) temperatures using a Maxi-Imaging fluorimeter and a thermoelectric Peltier plate heating/cooling system.
Reproduction assets foundThe paper provides authors' public R code and example files for extracting Tcrit values from T-F0 chlorophyll fluorescence curves, hosted on the authors' GitHub repository. The paper also states phenotype data are openly available in figshare (10.6084/m9.figshare.12545093), but no figshare URL is present in the allowedCode · publican leaf temperature estimated from
227 two thermocouples attached to leaves on the plate and relative F0 values using the segmented R
228 package (Muggeo 2017) using the R Environment for Statistical Computing (R Core Team
229 2020). We provide example files and example R code for extracting Tcrit values from T-F0
230 curves at https://github.com/pieterarnold/Tcrit-extraction.
231
232 Surface wetness experiment: effect of wet vs dry surfaces for leaves on CTMIN and CTMAX
233 Most experiments that measure T-F0 have measured leaf samples with all excess surface
234 moisture removed, on a dry surface. However, maintaining water content of detached leaves by
235 providing a wet surface where leaOpen asset ↗pieterarnold/Tcrit-extractionpdf-layout-page:8 lines:1-46Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Future increases in yield potential will rely largely on improved photosynthesis. Whereas emphasis has traditionally been given to measuring leaf photosynthesis, wheat spikes have an important role in filling grains since they can intercept up to a third of incident light. In the present study, 196 genetically diverse spring wheat lines were evaluated for spike photosynthesis (SP) under temperate (yield potential) and heat stressed, irrigated conditions. Two different methods to estimate SP were used: (i) gas exchange measurements of SP rate and (ii) integrative measurements using a SP inhibition treatment (consisting of a permeable textile covering the spikes). Rate of SP was measured directly in 45 selected genotypes under yield potential conditions using a custom-made illuminating chamber. In these lines, a variation of 2.8-fold for spike photosynthetic rate is reported for the first time with good heritability estimates. Correlations between SP rate and yield, thousand grain weight, number of grains per spike and radiation use efficiency are reported across different panels. Genotypic variation in SP was independent from flag leaf photosynthesis suggesting that any strategy aiming to increase canopy photosynthesis should also consider SP. The SP inhibition treatments were applied on the 196 lines in both environments to estimate SP contribution to grain weight per spike, which was 30–40 % under both heat stressed and yield potential conditions averaged across lines. Positive correlations with grain yield were observed for spike photosynthesis contribution across all of the panels under heat stress and when combining heat and yield potential environments (P < 0.001, r = 0.401). These results indicate a highly significant genotypic variation of spike photosynthetic rate and spike photosynthesis contribution to grain yield among wheat lines and highlight its importance under irrigated and heat stressed conditions.
Why it matches plant phenotyping methods小麦穂の光合成速度・寄与を高スループットに取得する2種類の測定法を用い、カスタム照明チャンバーによる直接測定も実施しており、植物生理形質のフェノタイピング手法の実質的適用が研究の中心である。
titleSpike photosynthesis measured at high throughput indicates genetic variation independent of flag leaf photosynthesis
Reproduction assets foundThe article reports spike photosynthesis phenotyping of 196 wheat lines (gas-exchange rates and SP inhibition treatments) but contains no explicit public dataset or code deposit. The only paper-specific, publicly accessible asset indicated is the article's supplementary material (Supplementary Tables 4-5 and Fig. 1), 'Supplement · publicnical assistance with measurements, data
and trial management. A special thanks to J.M. Esquer who was re-
sponsible to design the spike illumination chamber used in these ex-
periments for the measurements.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.fcr.2020.107866.References
Abbad, H., El Jaafari, S., Bort, J., Araus, J.L., Jaafari, S.E., Bort, J., Araus, J.L., 2004.
Comparison of flag leaf and ear photosynthesis with biomass and grain yield of
durum wheat under various water conditions and genotypes. Agronomie 24, 19–28.
https://doi.org/10.1051/agro:2003056.Acreche, M.M., Slafer, G.A., Open asset ↗pdf-raw-page:11 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.
Why it matches plant phenotyping methods小麦葉の窒素含量を推定するハイパースペクトル画像・非画像センサーを比較評価しており、植物形質取得法の技術的検証が研究の中心です。
abstractThis study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer.
Reproduction assets foundThe authors deposited the pre-processed hyperspectral reflectance data and demonstration Python code for this wheat nitrogen experiment on Adelaide Figshare, explicitly linked in the Supplementary Materials section.Dataset · publicThe pre-processed data and the Python codes for the demonstration are available at https://adelaide.figshare.com/articles/public_data_for_wheat_n_experiment/12502160 .Open asset ↗adelaide.figshare.com · 12502160lines:411-465Code · publicThe pre-processed data and python codes for demonstration is public available (refer to Supplementary Materials ).Open asset ↗lines:34-48Code / 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:218-250