The automation and standardization of hyperspectral imaging, particularly at high spatial resolution, are essential for advancing plant phenomics and supporting diverse plant science research. We developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments. System calibration established a spatial resolution of 24.3 μ m (full width at half maximum; FWHM), a spectral resolution of 1.78 nm (FWHM), and a depth of field of 4.53 mm, producing hyperspectral data cubes of 2195 × 2000 × 950 pixels across the 400–1000 nm spectral range. System-level and biological-sample consistency assessments confirmed stable spectral measurements during extended scanning sessions and across independently prepared trays. Thermal evaluation confirmed that the 150 W illumination design introduced minimal sample heating during scanning. HyperBird was first evaluated using grape downy and powdery mildews, where no consistent measurable effect of repeated imaging on pathosystem development was detected under the tested experimental conditions. Subsequently, HyperBird was used to characterize spatiotemporal spectral progression in grapevine leaves inoculated with Plasmopara viticola . An automated regional spectral tracing pipeline was developed to isolate spectra from retrospectively traced regions and compare them with whole-leaf averaged spectra across 0–9 days post-inoculation (DPI). Categorical mixed-effects modeling showed that these spatially resolved regional spectra exhibited significant disease-associated spectral change by 5 DPI, with a sharp transition at 6 DPI, whereas whole-leaf spectra showed delayed and weaker disease-aligned changes beginning at 7 DPI. These results demonstrate that HyperBird enables high-throughput, spatially resolved hyperspectral imaging for quantifying localized plant disease progression and provides a scalable platform for studying spectral biology across plant phenotyping applications.
Why it matches plant phenotyping methodsHyperBirdの高スループット・ハイパースペクトル画像取得ロボットを開発し、校正、再現性、加熱影響、病害進展の定量性能を検証しており、植物フェノタイピング手法が中心である。
abstractWe developed HyperBird, a hyperspectral microscopic imaging robot, to automate the acquisition of up to 351 leaf-disc samples in a single tray within approximately 2.4 h and thereby support scalable plant experiments.
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the authors' code and processed data supporting the phenotyping analyses; raw hyperspectral image data are request-only.Code · publicData Availability
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The code and processed data supporting the findings of this study are available in the GitHub
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repository at https://github.com/jy773Cornell/HyperBird-Robot. Raw hyperspectral image
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data are available from the corresponding author upon reasonable request due to file size and storage
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Supplementary Materials
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Supplementary materials accompany this article as a separate document (supplementary.pdf).
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Supplementary Figure S1. Representative GSAM-based segOpen asset ↗HyperBird-Robotpdf-raw-page:39 lines:1-75Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Precision agriculture is becoming more and more of a challenge that requires the use of intelligent systems that are able to predict stress and prevent yield loss before it is too late. Traditional methods of agricultural surveillance are predominantly reactive with irrigation demands being based on thresholds or individual yield forecasts models that do not represent the intricate spatio-temporal interactions that exist between crop physiology, soil status, and environmental stresses. Besides, the majority of the current practices do not have an autonomous decision-making approach to preventive intervention which leads to inefficient use of water and slows down the response to stress. This paper suggests a cognitive UAV-assisted agro-surveillance system to predict yield vulnerability caused by crop stress and optimize adaptive irrigation with the help of spatio-temporal deep and reinforcement learning. The framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data obtained with the Crop Health and Environmental Stress Dataset. A new GeoSpatio-TRiNet model is used to acquire long-range spatial relationship, time stress development, and diffusion of stresses across agricultural regions. The model predicts the vulnerability trajectories of the stress instead of the direct yield regression, and this allows early detection of yield risk. Such predictions serve to generate a cognitive environmental state of a Soft ActorCritic (SAC) reinforcement learning agent that autonomously computes zone-based irrigation behaviors to reduce the recurrence of stress at the minimum water usage cost. As shown by the results of the experiment, the proposed framework has a stress forecasting accuracy of 96.3% and performs much better than the traditional machine learning, CNN-based, and transformer-based baselines. The system also decreases the predicted yield vulnerability by 46.6 and enhances water-use efficiency by 41.1 as compared to irrigation strategies based on rules. The results confirm the usefulness of spatio-temporal intelligence with predictive control in terms of effectiveness, and the proposed framework is a scalable and sustainable solution to precision agriculture of the next generation.
Why it matches plant phenotyping methodsUAV画像とセンサーデータから作物ストレスの時系列状態および収量脆弱性を推定する計算・センシング手法が研究の中心であり、灌漑制御への応用も技術評価の一部として記述されている。
abstractThe framework combines UAV-obtained RGB and multispectral and thermal imagery with measurements of soil sensors and meteorological data
Reproduction assets foundThe paper uses the public Kaggle Crop Health and Environmental Stress Dataset (UAV RGB/multispectral/thermal imagery plus soil/weather measurements and stress labels) as its phenotyping data source, and the authors provide an explicit public GitHub repository for the analysis code.Dataset · publicThe current research is based on the Crop Health and Environmental Stress Dataset, which is a publicly available
dataset on Kaggle, specially created to help perform a spatio-temporal analysis of crop health in response to changing
environmental and water-stress factors [26].Open asset ↗pdf-raw-page:10 lines:1-62Code · publicturn: Final zone-wise stress predictions 𝐶
𝑡
𝑧, Yield vulnerability trajectories 𝑉𝑡
𝑧, Optimal adaptive irrigation policy
𝜋∗
End Algorithm
Code availability:
The data used to support the findings of this study are included in the article.
Code availability:
The code used in this research work is available in the following link.
https://github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillance
4. Result and Discussion
The architectural agro-surveillance solution, which is proposed to be executed by UAVs, is executed through a
modular and scalable software framework to guarantee reproducibility and extensibility. The experiments are all
performed in Python as a main programming languageOpen asset ↗github.com/replyvenugopal/Cognitive-UAV-Driven-Agro-Surveillancepdf-raw-page:24 lines:1-55Code / dataset availability 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 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 confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Crop diseases pose significant challenges to productivity in resource-constrained settings, often remaining undiagnosed when diagnostic tools and infrastructure are either non-existent or inadequate. Current crop disease diagnosis relies on manual inspection methods that are labor-intensive, prone to error, and incapable of delivering real-time or region-specific insights in the process. Such limitations call for developing advanced diagnostic systems that are scalable and efficient in resource-constrained settings. This research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities. Built within its core is the 3D Spectral-Spatial Convolutional Neural Network (3D SSCNN) that extracts high-resolution spectral-spatial features from hyperspectral image cubes. The accuracy achieved is around ~ 95% within 0.3 s per sample. Fed-DiagNet has provided support for distributed training that enables scalability and also data privacy to enhance the accuracy of regional models at approximately 92% as well as reduces training by almost 40%. Temporal disease progression modeling is enabled by Temporal Progression LSTM that provides dynamic trends with 90% accuracy up to a horizon of 10 days. This means that in addition to integrating disparate data sources-including hyperspectral imagery, environmental data, and pest observations-MTAN achieves an almost ~ 93% stress identification accuracy. Lastly, an RL-FO system tailors its treatment recommendations to local conditions so as to optimize for yield improvement and cost-effectiveness. With the proposed system, diagnostic precision increases to ~ 94%, and it is manifested in real-time efficiency while supporting scalability with actionable insights to empower farmers to mitigate crop losses and augment food security across several scenarios.
Why it matches plant phenotyping methods植物病害の状態をマルチスペクトル画像から推定する画像・機械学習フレームワークの開発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。
abstractThis research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities.
Reproduction assets foundThe paper's Data Availability statement points to two public repositories containing the data analyzed: a Kaggle PlantVillage dataset and a GitHub hyperspectral datasets repository. No author code or models are explicitly deposited.Dataset · publicAll data analyzed during this study are available in the Kaggle and Github repository, in the links https://www.kaggle.com/datasets/rohithaaiswarya/plant-village and https://github.com/antmedellin/HyperspectralDatasets .Open asset ↗Kaggle · rohithaaiswarya/plant-villagelines:563-583Dataset · publicAll data analyzed during this study are available in the Kaggle and Github repository, in the links https://www.kaggle.com/datasets/rohithaaiswarya/plant-village and https://github.com/antmedellin/HyperspectralDatasets .Open asset ↗GitHub · antmedellin/HyperspectralDatasetslines:563-583Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings. Here, we propose Lite U-net FiLM (LUF-net), a lightweight U-net framework integrated with feature-wise linear modulation (FiLM) layers, which incorporates both multispectral-derived irradiance and camera exposure parameters as auxiliary inputs. This metadata-aware design enables dynamic modulation of intermediate image features, allowing the network to disentangle unified canopy color from environmental artifacts. Experiments conducted across diverse crop types (soybean, rice, maize), flight altitudes (6m, 12m, 25m, 40m), and illumination conditions (overcast skies, cloudy, sunny, early morning) demonstrate that the LUF-net model substantially reduces the mean absolute percentage error to below 5.5%, outperforming conventional Gray-world, U-net, AlexNet-MLP, and SIDBlock-MLP methods. Ablation experiments further show that both irradiance and exposure metadata provide complementary information, while FiLM-based conditioning effectively integrates these acquisition parameters into feature learning, jointly contributing to improved color reconstruction performance. Moreover, correlation analysis indicates that the model's color reconstruction errors are weakly dependent on irradiance and exposure settings, confirming that LUF-net reduces sensitivity to external imaging conditions while maintaining physically meaningful and robust corrections.
Why it matches plant phenotyping methodsUAV画像の色校正手法を開発し、複数作物・撮影条件で性能検証しており、作物フェノタイピングの画像取得・補正が中心である。
abstractAccurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings.
Reproduction assets foundThe paper's data and code availability statement explicitly points to a public GitHub repository containing training/evaluation/inference code, pretrained model weights, and example data for the LUF-net color calibration method.Code · publicThe data and source code for model training, evaluation, and inference, together with pretrained model weights, example data, and detailed usage instructions, is publicly available at: https://github.com/wangchufeng3652/color-correction.Open asset ↗wangchufeng3652/color-correctionhtml-lines:278-299Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Abstract Blanket-rate agrochemical scheduling — a practice wherein the same quantity of fertilizer or pesticide is spread uniformly across an entire field irrespective of spatial or temporal crop need — persists as the dominant farm management paradigm across rural India and large parts of South Asia. This approach generates cascading inefficiencies: excess nitrogen drains into waterways, off-target pesticide deposits devastate pollinators, input costs erode thin profit margins, and wide-scale greenhouse gas release from soil microbial activity accelerates climate change. The study documented here addresses this challenge through a purpose-built, four-layer intelligent field management platform. The platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack. A fine-tuned YOLOv8-L network performs real-time pest and foliar disease localisation; a ResNet-50 backbone quantifies canopy health across five stress gradients; a two-layer stacked LSTM projects short-horizon yield trajectories; and a Deep Q-Network autonomously plans drone spray routes weighted by field-specific prescription maps. Field validation spanned two consecutive growing seasons (Rabi 2022–23 and Kharif 2023–24) across six georeferenced plots covering 4.8 ha at Baramati, Maharashtra. Outcome metrics recorded during head-to-head comparison with conventional practice included a disease detection score of 95.6% mAP, a 47.3% reduction in total nitrogen applied, a 38.1% decrease in pesticide volume, and a 22.4% uplift in harvested grain weight. Together, these field-verified numbers substantiate the operational readiness of integrated AI precision agriculture for smallholder deployment.
Why it matches plant phenotyping methodsマルチスペクトル画像・深層学習による病害局在化とキャノピー健康状態の定量化を中核機能とする統合プラットフォームであり、植物の病害状態・生育状態を直接推定して現地検証している。
abstractThe platform ingests continuous data from drone-mounted multispectral cameras, in-field IoT soil probes, a wireless weather station, and cloud-sourced Sentinel-2 satellite imagery, then passes these inputs through a cascaded AI inference stack.
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 · publicData Availability The annotated image dataset (14,300 images, 23 classes), trained YOLOv8-L and ResNet-50 weights, LSTM model files, DQN policy checkpoint, and all analysis scripts are archived at https://github.com/precision-agri-ai (Zenodo DOI: 10.5281/zenodo.XXXXXXX).Open asset ↗precision-agri-ailines:161-182Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Why it matches plant phenotyping methodsUAV multispectral HTPによる小麦黄さび病重症度の推定と、複数の予測モデルの比較・検証が研究の中心であり、植物病害状態を直接推定する実質的なフェノタイピング手法研究である。
abstractHTP data were processed to extract spectral wavelengths and vegetation indices (VIs)
Reproduction assets foundThe article's Data Availability statement deposits the datasets generated and analyzed in this study (yellow rust phenotyping with UAV spectral data and genomic markers in bread and durum wheat) in the CIMMYT repository under DOI 10.71682/10549375, which is an allowed URL. No author analysis code or trained model is avDataset · publicand scalable strategy for YR assessment in wheat breeding.
Funding
The authors gratefully acknowledge financial support from the Government of Mexico through
the “MasAgro – Cultivos para México” initiative.
Data Availability
The datasets generated and/or analyzed during the current study are available in the CIMMYT
repository: https://doi.org/10.71682/10549375.Acknowledgements
We are deeply grateful to Julio Huerta-Espino for his guidance and support throughout all stages
of this manuscript. We also thank Hedilberto Velásquez Miranda for his valuable assistance with
rust visual score phenotyping, and Neftalí Cruz Pérez for his dedicated support in trial sowing and
field management.
Conflict Open asset ↗10.71682/10549375pdf-raw-page:30 lines:1-37Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Aims Climate change is altering northern peatland plant communities, shifting from Sphagnum mosses to vascular plants. This transition impacts ecological functions like carbon sequestration, making long-term vegetation monitoring at the site scale more critical than ever. However, current monitoring methods tend to focus on specific species or functional groups with limited spatial coverage. This study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities. Location Temperate peatland in Pyrenees Mountains, France (Bernadouze, Vicdessos). Methods Nine plots were selected across diverse microhabitats and sampled three times over the growing season of 2023 (May, June, and July). Plant species abundances were recorded, and 45 vegetation indices were derived from drone and Sentinel-2 multispectral imagery. Five vegetation indices were selected to fit a joint Species Distribution Model (JSDM) and a Random Forests (RF) model, and map species spatial distribution. Principal Coordinates Analysis (PCoA) identified plant community composition, and spatiotemporal variations were quantified in relation to environmental variables. Results Plant species occurrences could be predicted from multispectral imagery using the JSDM, with drone-based inferences (mean R 2 = 0.36) outperforming Sentinel-2 (mean R 2 = 0.29). Model performance was high for abundant species ( R 2 > 0.5), whereas predictions for rare species were less accurate ( R 2 R 2 > 0.65, P R 2 = 0.40; P R 2 = 0.04; P Conclusion This study demonstrates that drone multispectral imagery can be used to predict peatland vegetation richness and community composition and capture fine-scale heterogeneity in a small and fragmented peatland site, outperforming satellite data in spatial precision. Although our model was less accurate using satellite imagery, the use of Sentinel-2 imagery enabled long-term community tracking. By combining both, our predictive modelling framework provides a promising preliminary tool to monitor climate-induced shifts in species distributions, supporting targeted conservation.
Why it matches plant phenotyping methodsドローンおよび衛星マルチスペクトル画像から植物種の空間分布、植生多様性、群集組成を推定する画像・モデリング手法が研究の中心であり、植物状態の測定に直接結びつく。
abstractThis study uses remote sensing to infer the spatial structure and temporal variations of peatland plant communities.
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 to replicate main analyses are available at https://github.com/vjassey/peatland_vegetation_mapping .Open asset ↗vjassey/peatland_vegetation_mappinglines:369-375Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT While whole-genome sequencing captures millions of single nucleotide polymorphisms (SNPs) and hyperspectral imaging (HSI) enables non-destructive plant phenotyping, integrating these modalities to link genotype to phenotype remains challenging due to their high dimensionality and non-linearity. This study presents DeepPheno a deep learning framework that predicts SNP genotypes from HSI data, using model predictability as a proxy for genotype-phenotype association. HSI data were acquired from 194 lettuce genotypes under field conditions. HSI data patches (20×20 pixels × 224 spectral bands) were used to train a hybrid CNN to predict the variant of a specific SNP. The framework was validated on SNPs with known phenotypic effects (anthocyanin, leaf serration, pale pigmentation), achieving high predictive performance (AUC ranging from 0.806 to 0.935), whereas models trained on randomly shuffled labels performed at chance (mean AUC ≈ 0.51). Extending the workflow to 50 randomly selected putatively neutral SNPs, most yielded low predictability, but two showed high performance (AUC > 0.76), suggesting uncharacterized genotype-phenotype links. Explainable AI, including SHAP and Grad-CAM, identified relevant spectral and spatial features driving these predictions, particularly the green and red-edge wavelengths associated with pigment dynamics and leaf structure. These results establish a framework for understanding complex genotype-phenotype interactions in plants and extracting these links from HSI data without predefining the exact trait values. It provides an avenue for high-throughput trait discovery and description and extends the integration of image-based phenomics with plant genetics.
Why it matches plant phenotyping methodsHSIと深層学習を統合し、遺伝子型関連の植物表現型情報を抽出する枠組みを開発・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study presents DeepPheno a deep learning framework that predicts SNP genotypes from HSI data, using model predictability as a proxy for genotype-phenotype association.
Reproduction assets foundThe paper's Data Availability statement deposits authors' code, scripts, and supplementary material in a public GitHub repository, including a downscaled de-identified sample dataset demonstrating the pipeline. The raw HSI/genotype datasets are proprietary under NDA and not public.Code · publicThe code, scripts, and supplementary material supporting the findings of this study have been deposited in the GitHub repository at https://github.com/frankgyan/Utrecht-University--HSI .Open asset ↗frankgyan/Utrecht-University--HSIlines:195-223Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methods植物フェノタイピング用の画像解析ソフトウェア開発と、形態形質測定機能の実証が中心である。
abstractPlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions.
Reproduction assets foundThe paper's data availability statement explicitly says that scripts used for the analyses in this paper are publicly available on GitHub (danforthcenter/plantcv-4-paper), and PlantCV source code is available via the PlantCV homepage. This is a paper-specific, public, actionable analysis-code asset.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-92Code / 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 confirmedCrossref · checked 15 Sept 2026
Abstract Mangroves play a critical role in coastal ecosystem services, particularly through their capacity to sequester large amounts of atmospheric carbon, contributing to climate change mitigation. Developing accurate mangrove carbon models is therefore essential for monitoring ecosystem condition and carbon stocks at relevant scales. This study aimed to estimate mangrove Above-Ground Carbon (AGC) in Baluran National Park by integrating field measurements and remote sensing data within a Machine Learning (ML) framework. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model, using field-measured tree height, Ratio Vegetation Index (RVI), and Transformed Soil-Adjusted Vegetation Index (TSAVI), achieved the best performance ( R² training = 0.93, R² testing = 0.84, 5-fold CV RMSE = 12.20 Mg C ha⁻¹). Predicted AGC ranged from 5.39 to 57.18 Mg C ha⁻¹ (mean ± Standard Deviation (SD) = 30.43 ± 16.09 Mg C ha⁻¹) and showed improved accuracy compared to the global mangrove biomass dataset of (Simard et al., 2019). A key contribution of this study is the integration of field-measured tree height within a satellite-based ML framework, which enhances the accuracy and ecological relevance of AGC estimation compared to approaches relying solely on spectral predictors or remotely sensed canopy height products, offering a practical and cost-effective alternative for sites where UAV or LiDAR data are unavailable. This approach provides a practical method for regional mangrove carbon monitoring, national carbon accounting and supports climate change mitigation efforts.
Why it matches plant phenotyping methodsマングローブの樹高・樹冠情報と衛星データを統合し、機械学習で個体・プロットレベルの地上部炭素量という植物状態を推定する手法を開発・交差検証しており、単なる生態系測定ではなく表現型取得手法が中心である。
abstractAGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE).
Reproduction assets foundThe authors state that all analysis code (model development, hyperparameter configuration, diagnostics, accuracy assessment) is publicly available in their GitHub repository Mangroves-AGC-Baluran, which reproduces this paper's mangrove AGC machine-learning analysis.Code · publicThe Python codes were available on
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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 confirmedCrossref · checked 14 Sept 2026
Abstract. As key components of agricultural management, planting and harvesting schedules have strongly influenced crop production by defining the length of the crop growing season and shaping the environmental conditions crops experience. Accurate knowledge of these management data is crucial for enhancing crop yield estimates by capturing the timing of crop development relative to weather and soil conditions, assessing climate adaptation by tracking shifts in farming practices over time, and supporting agricultural carbon accounting. Yet, existing planting and harvesting date datasets are largely based on state-level statistics or rule-based calendars that overlook intra-regional variability and the influence of human decision-making. The absence of long-term, high-resolution planting and harvesting date information hinders our ability to reconstruct historical agricultural practices and assess their agronomic and environmental consequences. In this study, we introduce CropPlantHarvest, the first dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution from 2001 to 2024. Planting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates. Harvesting dates are retrieved using the Normalized Harvest Phenology Index (NHPI), a novel index that integrates Normalized Difference Vegetation Index (NDVI) and near-infrared (NIR) reflectance to detect harvesting events by capturing the distinct spectral transition from senescent crops to exposed crop residues. Validation against USDA crop progress reports and field-level dataset demonstrates high accuracy of CropPlantHarvest, with a mean absolute error of approximately 5 d for both crop species. This large spatial and temporal dataset captures management-driven variability in crop season timing and duration, supporting improved modeling of crop yields, greenhouse gas emissions, and resource use. It could also serve as a benchmark for refining remote-sensing phenology products and evaluating the agro-environmental impacts of evolving crop management decisions. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).
Why it matches plant phenotyping methods衛星観測と作物モデルによる圃場レベルの作付・収穫時期推定手法を開発し、NHPIを提案して独立データで検証した大規模データセット研究であり、植物の生育・収穫状態の取得が中心的です。
abstractPlanting dates are estimated using CropSow, an integrative remotely sensed crop modeling system that aligns simulated crop growth trajectories with satellite observations to retrieve field-level planting dates.
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 · publicOur CropPlantHarvest dataset, which provides planting and harvesting dates for corn and soybean fields at 500 m spatial resolution across the U.S. Midwest from 2001 to 2024, can be accessed via Zenodo: https://doi.org/10.5281/zenodo.16967482 (Liu and Diao, 2025).Open asset ↗Zenodo · 10.5281/zenodo.16967482lines:322-333Code / 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
Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。
abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).
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 data can be accessed at: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:787-847Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Background Sorghum ( Sorghum bicolor ) is a versatile C4 crop used for food and feed and as biomass for bioproducts and energy. Improving nitrogen use efficiency (NUE) in sorghum is important because fertilizer is costly and excessive fertilizer use has negative environmental impacts. Leaf senescence mediates nutrient recycling, but its dynamic progression is difficult to quantify at scale. We evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum and link these phenotypes to gene expression. Sorghum Tx430 plants were grown under four N treatments (6, 9, 12, and 15 mM), imaged from vegetative growth through grain fill, and destructively sampled for RNA-seq at four developmental stages. Results A supervised support vector machine with a radial basis function kernel classified pixels from a hyperspectral image of sorghum plants grown under different N levels into green leaf, yellow leaf, dry leaf, stalk, panicle, and background classes with 0.93 accuracy. We defined the senescence ratio as the sum of yellow and dry leaf areas divided by the green leaf area and computed it across multiple growth stages and nitrogen levels. The senescence ratio did not differ among N treatments during vegetative growth, but it declined with increasing N during boot, anthesis, and grain fill, indicating earlier senescence under N limitation. Among the genes whose expression positively correlated with senescence ratio were 13 putative transcription factors, including SbiRTX430.02G247100, a WRKY1/ZAP1 homolog and a WRKY4 homolog. Gene regulatory network analysis of the top 1% of genes associated with SbiRTX430.02G247100 showed enrichment for processes associated with leaf senescence and chlorophyll catabolism. In contrast, the network associated with the WRKY4 homolog was enriched for autophagy-related terms. Conclusions Our study shows that automated hyperspectral imaging is highly effective for monitoring dynamic plant phenotypes, such as stress-induced senescence, that are difficult to visually score with the naked eye. Here, nitrogen deficiency served as the stress condition. Still, this approach supports large-scale phenotypic data collection for any such stressor and enables analyses with greater statistical power, yielding more robust conclusions and the potential for new insights that can be applied to engineering and breeding better crops.
Why it matches plant phenotyping methodsソルガムの動的な老化表現型を高スループットに取得する hyperspectral imaging と、SVMによる画像分類・senescence ratio算出が研究の中心であり、植物状態の定量化手法を実証している。
abstractWe evaluated whether visible-near-infrared hyperspectral imaging can provide high-throughput measures of N-limitation-induced senescence in sorghum
Reproduction assets foundThe paper's availability statement points to a public GitHub repository containing the authors' image-processing, machine-learning classification, transcriptomic analysis, and figure-generation scripts. The 148 GB hyperspectral image data is only promised 'upon acceptance' (not yet public), and the RNA-seq deposit is aCode · publicle in the NCBI SRA repository,
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(RNA-seq raw reads SRR38119224 to SRR38119282). Scripts used for image processing,
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machine-learning classification, transcriptomic analyses, and figure generation will be
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accessible through GitHub (https://github.com/belafif2/TX430_Senescence). Image data (148
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GB) will be made available in a data repository upon acceptance. Other relevant processed data
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files and supporting figures are available as supplementary data documents.
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Competing interests
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The authors declare that they have no competing interests.
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Funding
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This work was funded Open asset ↗belafif2/TX430_Senescencepdf-raw-page:22 lines:1-54Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping based on unmanned aerial vehicles still faces challenges regarding the direct correlation between spectral information with field-collected variables, due to the influence of environmental factors and the considerable variation among maize phenological stages. Therefore, the objectives of this research were: I) to evaluate the interaction of nitrogen doses and evaluation environments (phenological stages and growing seasons) and variance components for field variables and vegetation indices; II) to identify the most suitable indices according to the evaluation environments; and III) to predict field variables based on relevant vegetation indices identified through the proposed methodology. The study was conducted using a randomized complete block design with four repetitions, in which treatments consisted of six nitrogen (N) topdressing doses (0, 50, 100, 200, 300, and 400 kg ha−1) during the 2022/2023 and 2023/2024 growing seasons. Evaluations of agronomic variables and image acquisition were performed in five distinct phenological stages throughout the maize crop cycle. The data were analyzed using deviance analysis and variance components, principal component analysis (PCA), and multivariate linear modeling for the prediction of field variables. Our results demonstrated that all indices were affected by the interaction between N doses and evaluation environments (phenological stages and growing seasons). Additionally, the most reliable were EXGRaw, TGI, GNDVI, NDRE, CIRE, GVI, CVI, BNDVI, PanNDVI, SRNIRRe, SFDVI, RGBindex, NDVI, SAVI, MSAVI, and OSAVI, which showed clustering patterns according to growing season condition and phenological stage. Finally, the variables predicted using the proposed methodology achieved coefficients of determination above 0.80, except for shoot biomass and 100-grain weight. Therefore, it can be concluded that vegetation indices are influenced by the evaluated environment; however, the proposed framework based on the deduction of fixed and random effects enables the prediction of field variables with high accuracy using relatively simple models.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を選定し、農業形質を予測する方法論の開発・評価が研究の中心であり、植物形質の取得・推定に直接関与している。
titleMethodology for Selecting Stable UAV-Based Vegetation Indices for Prediction of Agronomic Variables in Maize Using a Multispectral Sensor.
Reproduction assets foundThe paper's supplementary file contains the REML-BLUP adjusted values for all vegetation indices and field variables, which directly reproduce the paper's phenotyping measurements and underpin its computational analysis. The raw UAV imagery and field data are only available on request, and the EstimateBreed R package (Dataset · 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/plants15121782/s1 , Table_Supplementary_1. This table contains all vegetation indices and field variables with values adjusted using the RELM-BLUP methodology.
Author Contributions
C.d.S.L.: Conceptualization, methodology, validation, visualization, writing—original draft, writing—review and editing. A.J.T.S.: Data collection and iOpen asset ↗lines:76-146Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield ( r = 0.79-0.82, p r = 0.72 in hard spring, r = 0.53 in soft spring). These conditions also improved discrimination between varieties. Although heritability patterns were not clearly differentiated by stress clusters, environments with higher genetic control of yield also tended to show stronger NDVI heritability. Overall, NDVI reliably captured wheat grain yield, which is governed by the genotype × environment driven variability, with its predictive value strongest in stress-prone conditions. These findings underline NDVI's usability as a practical digital trait for improving variety testing and guiding breeding decisions in challenging environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを抽出し、複数環境・品種で収量との関係、予測性、遺伝率を評価しており、デジタル植物形質の測定・検証が中心である。
abstractThis study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials.
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 · publicTrial data, including grain yield, variety, and market class information, were obtained from the Washington State University Extension Cereal Variety Selection and Testing Program ( https://smallgrains.wsu.edu/variety/ ).Open asset ↗lines:38-48Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
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.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
ToBRFV is a major threat to tomato and pepper crops because it spreads quickly and survives for a long time in the environment. Since there are few ways to control it after infection, early detection before symptoms are visible is crucial. Yet, only limited public datasets are available for this research. We present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection. In this study, two tomato cultivars and two pepper cultivars, all of which are commercially important and widely cultivated in greenhouses, were selected. Using these plants ensures that the dataset reflects real-world agricultural practices and captures variability across commercially grown types. Both healthy and ToBRFV-inoculated plants from each cultivar were included in the imaging process. All plants were cultivated under fully controlled greenhouse conditions in Adana Province, Türkiye. Healthy and infected tomato plants were grown in two separate greenhouses to prevent cross-contamination. Imaging was conducted over a 29-day period using Red-Green-Blue (RGB) and Visible Near Infrared (VNIR) cameras, including narrowband captures at 800 nm and 1000 nm, from multiple viewing angles. Infection status was confirmed via Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) analysis at multiple time points. The dataset is organized into four clean, labelled subsets and released under a CC BY 4.0 license. This resource provides unique opportunities for developing and benchmarking computer vision and machine learning approaches for pre-symptomatic plant disease detection, spectral feature analysis, and integration into precision agriculture systems. By combining controlled experimental design, spectral diversity, and open access, it establishes a robust foundation for cross-disciplinary research in plant pathology, agricultural engineering, and artificial intelligence.
Why it matches plant phenotyping methods植物病害状態を対象にした縦断マルチスペクトル画像データセットであり、公開データセットとして開発・ベンチマーク利用を目的とするため、表現型取得が中心です。
abstractWe present one of the first openly accessible, longitudinal multispectral image dataset dedicated to ToBRFV detection.
Reproduction assets foundThe article is a Data in Brief describing the authors' own openly released longitudinal multispectral plant image dataset (ToBRFV-LMID) for tomato and pepper disease detection, deposited on Zenodo under CC BY 4.0 with a direct DOI URL. This is a paper-specific, public, directly actionable phenotype/image asset. No codeDataset · publicData accessibility
Repository name: ZENODO
Data identification number: 10.5281/zenodo.17244968
Direct URL to data: https://doi.org/10.5281/zenodo.17244968Open asset ↗ZENODO · 10.5281/zenodo.17244968html-lines:98-126Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plant leaf diseases must be detected and treated early to improve crop yield and reduce agricultural losses. However, pixel-level representations and the inability to be read limit the applicability of existing deep learning approaches to the agricultural sector. A graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease. The proposed framework models leaf pictures as a graph with nodes representing discriminative leaf areas and edges representing their spatial connection. Before creating the global context vector and classification, graph features are aggregated, and an attention weighting method is applied to refocus on disease-relevant nodes obscured by less informative background characteristics. Final disease prediction uses a multilayer perceptron classifier. A curated dataset of half-spinach and curry leaf pictures is used to assess the proposed method for fifteen illnesses and their healthy classifications. Grad-CAM-based explainable AI methods make the model predictions' most important areas clearer. The dataset and source code from this work are available on GitHub for reproducibility and openness. Experimental results reveal that the proposed AE-GNN outperforms convolutional neural networks and graph-based models in classification. Graph-structured learning, attention enhancement, and explainability create a robust and interpretable framework for multi-plant leaf disease diagnosis.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類・診断する画像解析手法を提案し、既存モデルとの比較評価と説明可能性解析を行っているため、植物フェノタイピング手法が中心である。
abstractA graph neural network termed the Attention-Enhanced Graph Neural Network (AE-GNN) may explain and diagnose multi-plant leaf disease.
Reproduction assets foundThe paper's Data availability section explicitly links a public GitHub repository containing the paper's spinach/curry leaf fungal disease image dataset used for the AE-GNN phenotyping/classification analysis.Dataset · publicData availability
The dataset is available at the link below. https://github.com/MeganathanE1990/FINAL-DISEASE-DATA-SET/tree/mainOpen asset ↗MeganathanE1990/FINAL-DISEASE-DATA-SETlines:413-463Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB). HLB, also known as citrus greening disease, is a major pathology caused by the bacterial pathogen Candidatus Liberibacter asiaticus , particularly in species of the citrus genus. The dataset is constituted of terrestrial images acquired in a commercial sweet orange orchard of the variety Pera Rio ( Citrus sinensis (L.) Osbeck). The images describe large portions of canopy, with healthy leaves and sections infected by HLB as well as some confounding factors naturally present in orchards. Multispectral images were acquired with a multi-lens camera within the visible-near-infrared domain, resulting in 14 narrow spectral bands. The image acquisition was conducted during two field campaigns in 2023 and 2024. In total, the dataset contains 2,978 images divided into two classes HLB (1,681) and non-HLB (1,297). Originally, data are stored in TIFF format as 14 monochromatic images, organised by spectra band. Additionally, an HDF5-format version is provided, where images are stored as 3D arrays with spectral bands in ascending order. This format is compatible with various programming languages, enables efficient data handling, and is optimised for machine learning and image processing applications, supporting reproducible and portable analysis. This dataset is a valuable resource for the development and benchmarking of classification models, including deep learning approaches, aimed at the detection of HLB. Phytopathology imaging datasets are scarce yet essential for advancing digital agriculture and the development of robust tools for crop disease detection worldwide.
Why it matches plant phenotyping methods柑橘葉・樹冠のマルチスペクトル画像からHLB感染状態を推定するデータセットであり、植物病害状態の表現型取得と機械学習ベンチマークを中心とする。
abstractThis article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB).
Reproduction assets foundThe article is a Data in Brief describing a public multispectral HLB citrus image dataset deposited on Data INRAE (Recherche Data Gouv, DOI 10.57745/054NAB), plus an authors' GitHub repository with preprocessing, registration, and model training scripts. Both are paper-specific, public, and directly actionable.Dataset · publicData accessibility
Repository name: Data INRAE
Data access link: https://doi.org/10.57745/054NABOpen asset ↗Data INRAE · 10.57745/054NABhtml-lines:89-123Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Spatially accurate estimates of forest above-ground biomass (AGB) are indispensable for carbon-stock accounting and sustainable silviculture. Existing mapping approaches face challenges in densely vegetated Coastal Plain forests because of seasonal optical variability, radar–optical saturation, and limited wall-to-wall structural information. We aimed to (i) develop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping, (ii) quantify the added value of seasonal optical data and photogrammetric canopy-height profiles, and (iii) interpret model drivers using explainable artificial intelligence (AI) to relate predictors to forest structure and composition. We mapped AGB across ~ 10,500 km 2 in southeastern North Carolina using wall-to-wall predictors from optical, radar, and photogrammetric sources. Forest Inventory and Analysis plot data (n = 305) were used to train and evaluate an ensemble of gradient-boosted tree models (CatBoost, LightGBM, XGBoost) and a neural network (RealMLP) via cross-validation. Model behavior was interpreted using feature importance and partial dependence analysis. Expanding Sentinel-2 temporal coverage from summer-only to four-season composites improved normalized RMSE by 8.7%. Incorporating canopy-height profiles from NAIP produced the largest accuracy gain, lowering nRMSE by 15.9–18.0% relative to the multisensor baseline, which underscores the critical value of structural information for AGB prediction. Three key predictors illustrated complementary ecological dimensions: the 10th percentile canopy height captured canopy openness, L-band polarimetric alpha indicated volume-scattering regime, and spring red-edge reflectance captured vegetation biochemistry. These findings show that fusing structure, polarimetry, and spectral phenology yields robust AGB maps and improves generalizability across heterogeneous landscapes. This transferable, broadly accessible framework integrating structural, polarimetric, and spectral phenology data enables landscape-scale AGB monitoring and supports targeted conservation planning, restoration tracking, and adaptive management for carbon sequestration. The incorporation of high-resolution wall-to-wall structural data is particularly valuable for improving the accuracy and usability of forest AGB maps, thereby informing more responsive decision-making.
Why it matches plant phenotyping methods森林の地上部バイオマスという植物群落形質を対象に、光学・レーダー・写真測量データを融合した推定フレームワークを開発・評価しており、形質推定手法が研究の中心である。
abstractdevelop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping
Reproduction assets foundThe authors explicitly state that the code reproducing all figures and analyses is archived in a GitHub repository and permanently preserved via Zenodo (doi 10.5281/zenodo.18688899). The GEDI-derived CHM25 product (Zenodo 11176727) is a cited prior-work dataset from Wang et al. (2025), not this paper's own asset, and FCode · publicGEDI data products are distributed by NASA’s Land Processes
Distributed Active Archive Center and are accessible through
Google Earth Engine. The code used to reproduce all figures
and analyses has been archived in a GitHub repository (https://
github.com/ChaoEcohydroRS/NC_SoutheastBiomass) and
permanently preserved via Zenodo (https://doi.org/10.5281/zenodo.18688899, submitted on 20 February 2026).
Declarations
Conflict of interest The authors declare no competing inter-
ests.
Disclaimer The findings and conclusions in this publication
are those of the author(s) and should not be construed to rep-
resent any official USDA or U.S. Government determination or
policy.
Open Access This articleOpen asset ↗Zenodo · 10.5281/zenodo.18688899pdf-raw-page:23 lines:1-89Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
Hyperspectral imaging is an imaging technique that allows for acquisition of high-resolution spectral information beyond that of the visible spectrum. When applied to plants, it effectively enables non-invasive characterization of physiological status and has been widely used in agricultural settings. Marchantia is a model bryophyte species whose flat morphology and visually distinct stress-response phenotypes makes it an ideal candidate for imaging studies. Here, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing. This protocol features a streamlined data processing pipeline hosted on a web-based development platform that automates 1) the segmentation of plant area into spatially distinct regions for localized analysis of intra-specimen physiological gradients, and 2) classification of plant pixels based on their spectral signatures. All results are exported as structured CSV files for ease of further analysis as desired by the user.
Why it matches plant phenotyping methodsマーチャンティアを対象としたハイパースペクトル撮像プロトコルと、植物領域のセグメンテーション・スペクトル分類を含む処理パイプラインを開発しており、植物の生理状態取得が中心的な方法論的貢献である。
abstractHere, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing.
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 · publicExample images used in this protocol have previously been published by Krishnamoorthi et al. (2024) 4 and can be downloaded from https://github.com/dr-daisuke-urano/PlantHyperspectralSVDOpen asset ↗PlantHyperspectralSVDlines:47-85Code / 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 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 confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Timely and accurate plant disease detection is important for enhancing agricultural productivity and promoting sustainability. The study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases. The proposed method combines fuzzy logic with multimodal data fusion to effectively address the complex interactions and uncertainties in agricultural datasets. The MAF-DNN employs a robust adaptive fuzzy framework with dynamic rule optimization and integrates Hyperspectral Imaging Data (HID) with RGB imaging data to acquire detailed spectral information and high-resolution visual cues for disease classification. The multimodal fusion enhances the model’s ability to capture intricate patterns that relate to plant health, improving the accuracy of disease classification. The experimental results showed that the MAF-DNN outperforms traditional models by achieving an accuracy of 97.8%, precision of 96.5%, recall of 98.2%, and F1-score of 97.3%. Additionally, the adaptive design reduces computational overhead, increases efficiency, and improves scalability for large-scale agricultural applications. The MAF-DNN represents a significant advancement in plant disease classification and provides a robust and efficient solution for precision agriculture.
Why it matches plant phenotyping methods植物病徴を画像から分類するマルチモーダル画像・深層学習手法の開発と性能評価が中心であり、植物の病害状態を直接推定するため。
abstractThe study introduces Multimodal Adaptive Fuzzy-based Deep Neural Network (MAF-DNN) for classification of plant diseases.
Reproduction assets foundThe paper uses two public Kaggle plant disease image datasets (New Plant Diseases Dataset and CCMT Plant Disease Dataset) as its phenotyping inputs and states that the authors' custom MAF-DNN code is publicly available on GitHub, with all three URLs given in the article and matching allowed URLs.Code · publicThe custom code used to develop and evaluate the proposed Multimodal Adaptive Fuzzy Deep Neural Network (MAF-DNN) framework is publicly available at: https://github.com/skbsangeetha/MAF-DNN-Plant-disease-classificationOpen asset ↗skbsangeetha/MAF-DNN-Plant-disease-classificationhtml-lines:102-118Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle ( Sonchus oleraceus ) and little mallow ( Malva parviflora ). Hyperspectral imaging provides rich spectral-spatial data cubes that can support the development of deep learning models and autonomous technology for precision weed management. Plants were grown in a greenhouse under five conditions: standard, drought, overwatering, excess fertilizer, and no fertilizer. Custom MATLAB scripts were utilized for preprocessing, including k-means clustering to define regions of interest (ROIs), and extraction of spectral metrics. Data visualization was performed using Wolfram language and MATLAB. The dataset includes both raw and ENVI-formatted hyperspectral cubes and pre-processed MATLAB outputs, supporting spectral feature engineering, benchmark development, and exploratory machine learning workflows for controlled environment stress classification.
Why it matches plant phenotyping methods植物のストレス状態を対象とするハイパースペクトル画像データセットで、ROI抽出・スペクトル指標化と機械学習ベンチマークを中心的に提供しているため、植物フェノタイピング手法・データセットとして適格。
abstractThis work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species
Reproduction assets foundThe paper is a Data in Brief article describing its own hyperspectral imaging dataset of annual sowthistle and little mallow under five abiotic stress treatments, deposited publicly on Zenodo (record 17398082). The dataset includes raw ENVI-format hyperspectral cubes, preprocessed MATLAB outputs (ROI masks, extracted植被Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: zenodo.17398082
Direct URL to data: https://doi.org/10.5281/zenodo.17398082Open asset ↗Zenodo · zenodo.17398082html-lines:92-120Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Latest imaging technologies play a vital role in the extraction of plant phenotypic traits in high ranges. Most existing analytical methods treat these traits as independent features, overlooking the complex interaction patterns that focus on plant responses to environmental stress. Proposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits plat information graph structured interaction networks leverages perceptual similarity learning to capture higher-order phenotypic patterns. In the PGK framework, traits extracted from RGB (Red, Green, Blue) and multispectral imagery are encoded as nodes, and biologically meaningful relationships amongst trait pairs are represented as weighted edges. Extracted trait values are continuously transformed into perceptual states to enhance biological interpretability, and a graph kernel is employed to measure similarity between trait graphs. Experiments performed in an agricultural field with a precision agriculture dataset for plant stress phenotyping demonstrated that the proposed PGK achieved 93.8% classification accuracy, improving performance by 5.3 percentage points over the CNN baseline. The outcome results clearly highlight the effectiveness of the perceptual graph model for plant phenotyping and provide a robust, interpretable computational framework for sustainable crop monitoring and decision-support in precision agriculture.
Why it matches plant phenotyping methods画像由来の植物形質を抽出・関係グラフ化し、ストレス表現型分類を行う計算手法が研究の中心であるため。
abstractProposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository (marathonengineer/Agriproject) containing the datasets used in this plant stress phenotyping study. The other allowed URL (PlantCV) is a generic phenotyping library, not a paper-specific asset.Dataset · publicThe datasets used in this study are available in publicly accessible online repositories. The repository can be accessed at: https://github.com/marathonengineer/Agriproject.Open asset ↗marathonengineer/Agriprojecthtml-lines:589-657Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Why it matches plant phenotyping methods葉の高次元形質とスペクトル反射を用いて、測定困難な根形質を非破壊・高スループットに推定する方法が研究の中心である。
abstractHigh-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Disease prevention and water management are important to all the crops, particularly rice and sugarcane production in India. The article proposes a reinforcement learning (RL) based intelligent irrigation management system that is capable of optimising water consumption and crop nutrition in response to the changing agricultural climatic conditions. Decentralised reinforcement learning (RL) is used in a network of irrigation agents that utilise soil and microclimate sensor networks to set the terms of water allocation, water use efficiency (WUE) and crop health. At the same time, deep convolutional networks can be used to differentiate between plant stress/disease and leaf images and take applicable proactive actions. It is a framework that incorporates satellite-derived indices (NDVI, EVI, land surface temperature) with local sensor measurements and image-based health measurements through multimodal deep learning. Far-reaching simulations (including Indian climate and crop calendars) demonstrate that the multi-agent system lowers water consumption and preserves the yields and properly notifies stressed plants. The scores of disease detection with plantvillage-based fine-tuned on rice (120 (3 disease types) and 3829 (5 disease types) and sugarcane (2569 images for all disease types, Convolutional Neural Network (CNN) yield results of >98 % accuracy. Crop mapping (rice/sugarcane) Satellite/LSTM-based crop mapping (with Sentinel-1 / Sentinel-2) achieves more than 97 % accuracy. The suggested structure provides a data-driven, scalable system for precision agriculture to enhance the management of irrigation periods and crop health. Simulation experiments show that the RL-based controller can reduce water consumption while preserving optimal soil moisture levels when compared to rule-based irrigation strategies.
Why it matches plant phenotyping methods画像・衛星・センサーを統合して植物ストレス／病害状態を推定するマルチモーダル基盤が提案され、病害検出性能も評価されているため、植物表現型推定が実質的な構成要素である。
abstractdeep convolutional networks can be used to differentiate between plant stress/disease and leaf images
Reproduction assets foundThe paper reports simulation-based experiments using public leaf-image datasets. The only paper-specific public asset explicitly identified is the Kaggle rice leaf diseases dataset (vbookshelf/rice-leaf-diseases) cited as a data source for the rice disease fine-tuning set. No authors' code, trained models, or data dépDataset · publicConflict of interest: Authors do not have any conflict of interest 2026 Mar 31). Available from: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:16 lines:1-58Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Purpose The primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix, and to assess whether integrating spectral, texture, soil, and fertilization information could improve prediction performance for precision sugarcane management. Methods Jilin-1 satellite imagery acquired at four growth stages, from seedling to maturity, was used to derive vegetation indices (VIs) and texture indices (TIs), including the normalized difference texture index (NDTI), enhanced vegetation texture index (EVTI), and double-difference ratio texture index (DDRTI). Soil chemical properties (SCPs) and fertilization information (FI) were further incorporated with the remotely sensed variables. Machine learning models were developed for plot-level prediction of sugarcane traits across plant cane and first ratoon cane, and texture window size was optimized to improve TI extraction and model performance. Results For yield, the combination of VIs and TIs outperformed VIs alone at the tillering stage (R 2 CV = 0.65, RMSECV = 15.06 t/ha, RPDCV = 1.68). Adding SCPs and FI further improved yield prediction across plant cane and first ratoon cane (R 2 CV = 0.70, RMSECV = 13.84 t/ha, RPDCV = 1.83). Millable stalk population was best predicted at the maturation stage by VIs and Tis, achieving the best performance (R 2 CV = 0.63, RMSECV = 6602 stalks/ha, RPDCV = 1.66). The best Brix model integrated VIs, TIs, SCPs, and FI at the maturation stage (R 2 CV = 0.44, RMSECV = 0.53 °Bx, RPDCV = 1.33). SHAP analysis identified VIs as the dominant features for sugarcane traits prediction. And, DDRTI contributed more than NDTI and EVTI in yield and Brix prediction. Conclusion It is concluded that integrating spectral, texture, soil, and fertilization information from high spatial resolution Jilin-1 imagery is a promising approach for improving plot-level prediction of key sugarcane traits.
Why it matches plant phenotyping methods衛星画像からサトウキビの収量、可販茎数、Brixを plot レベルで推定し、テクスチャ特徴抽出の最適化と機械学習性能評価を行っており、表現型取得・推定手法が中心である。
abstractThe primary objective of this study was to evaluate the potential of high spatial resolution Jilin-1 satellite imagery for plot-level prediction of sugarcane yield, millable stalk population, and Brix
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the authors' model-training code and test data for the sugarcane trait prediction analysis. No public phenotype dataset or imagery deposit is stated; additional data are only available on request.Code · publicPart of the code and test data for model training are available at https://github.com/guangtaoxu08-dev/SPT_JL .Open asset ↗guangtaoxu08-dev/SPT_JLlines:228-248Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Phenotyping plant traits using UAV-based multispectral imaging offers a robust and unbiased approach to assessing crop status. With approximately 70% of smallholder farmers in East and Southern Africa cultivating common beans as a key source of food and income, there is a critical need for accurate and timely measurements of crop health and yield to support data-driven management decisions and disease mitigation. Traditional phenotyping methods are labor-intensive, and existing remote sensing and machine learning approaches remain limited. This study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery. Data collected over three growing seasons (2022–2024) were used to extract canopy variables and vegetation indices (VIs) across phenological stages. For yield prediction, traditional machine learning models achieved a root mean squared error (RMSE) of 242.33 kg ha⁻¹ and an R² of 0.66 using an Extra Trees Regressor. A novel BY-GRU architecture improved performance, achieving an RMSE of 242.40 kg ha⁻¹ and an R² of 0.79. The analysis also identified 45–60 days after sowing as the optimal window for prediction. To address limitations in conventional plant health assessments, this study introduces a novel Health Index. Comparative analysis demonstrated its robustness across genotypes and stronger correlation with yield. Machine learning and deep learning models, including MaxViT, were applied to estimate the Health Index, achieving improved predictive performance. Overall, this work integrates UAV sensing and modelling to provide scalable tools for phenomics, crop management, and breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像から作物の健康状態、収量、キャノピー形質を推定するセンシング・機械学習フレームワークが研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study presents a comprehensive framework for plot-level assessment of common bean health and yield using time-series RGB and multispectral imagery.
Reproduction assets foundThe preprint's DATA AVAILABILITY section states that all processed data required to reproduce the results are publicly available in a Google Drive repository, which qualifies as a paper-specific public phenotype dataset asset. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicCommon Bean Breeding Program for
facilitating field trials. We also thank the Phenomics team for their valuable assistance with UAV-based
data collection.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY
The datasets generated and/or analyzed during the current study are publicly available at:
https://drive.google.com/drive/folders/1fN3Q9n3bK_YoXFK8VFKZ3uEb13y9iRWj?usp=sharing. This
repository includes all processed data required to reproduce the results presented in this study.
SUPPLEMENTAL MATERIAL
Supp. Figure 1. Drone-based field view of the bean trial site at CIAT Palmira Research Station: A) RGB
image and B) NDVI image.
Supp. Figure 2. Drone Features Open asset ↗pdf-raw-page:40 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.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: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Code / 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 confirmedOpenAlex · arXiv · checked 15 Sept 2026
Multispectral / hyperspectralLeafCalibration / preprocessingSegmentationVisualization / data management
Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it reveals details about plant biochemistry and stress that standard cameras miss. However, processing this data is often challenging. Many labs still rely on loosely organized collections of lab-specific MATLAB or Python scripts, which makes workflows difficult to share and results difficult to reproduce. MVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data. The software handles everything from calibrating raw ENVI files to detecting and clipping individual leaves based on multiple vegetation indices (NDVI, CIRedEdge and GCI). It also includes tools for data augmentation to create training-time variations for machine learning and utilities to visualize spectral profiles. MVOS_HSI can be used as an importable Python library or run directly from the command line. The code and documentation are available on GitHub. By consolidating these common tasks into a single package, MVOS_HSI helps researchers produce consistent and reproducible results in plant phenotyping
Why it matches plant phenotyping methods葉レベルHSIの校正・葉検出・切り出しを含む再現可能な植物表現型解析用ソフトウェアであり、手法が中心。
abstractMVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data.
Reproduction assets foundThis is a software paper describing MVOS_HSI, the authors' open-source Python library for hyperspectral plant-phenotyping preprocessing (calibration, leaf segmentation/clipping, augmentation, spectral plotting). The authors' code is explicitly and publicly available on GitHub at the allowed URL, making it a paper-phenyCode · publicyping.
K eywords Hyperspectral imaging ⋅ \cdot
Plant phenotyping ⋅ \cdot
Data preprocessing ⋅ \cdot
Vegetation indices ⋅ \cdot
Data augmentation ⋅ \cdot
Python
Table 1: Code Metadata for MVOS_HSI
Nr.
Code metadata description
Metadata
C1
Current code version
v0.2.1
C2
Permanent link to code/repository used for this code version
https://github.com/MVOSlab-sdstate/mvos_hsi
C3
Permanent link to Reproducible Capsule
N/A
C4
Legal Code License
MIT License
C5
Code versioning system used
git
C6
Software code languages, tools, and services used
Python 3.x; NumPy, SciPy, Matplotlib
C7
Compilation requirements, operating environments & dependencies
Standard scientific Python environment on Windows, LinOpen asset ↗MVOSlab-sdstate/mvos_hsi · mvos_hsilines:1-122Code / 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 confirmedarXiv · checked 13 Sept 2026
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.
Why it matches plant phenotyping methodsSentinel-2 NDVIを用いて森林キャノピーの季節変動から褐変・攪乱状態を推定する手法を開発し、適合度と独立参照データで検証しているため、単なる森林地図作成ではなく植物状態の取得・評価が中心である。
abstractwe present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2.
Reproduction assets foundThe paper explicitly states that its code and interactive content are publicly available in the authors' GitHub repository. Other URLs in the article are cited third-party data sources (swisstopo, EnviDat, GDAL, TauDEM, WhiteboxTools) rather than paper-specific assets.Code · publicThe code and interactive content are available at https://github.com/SamanthaBiegel/s2-forest-browning-monitoring .Open asset ↗SamanthaBiegel/s2-forest-browning-monitoringlines:51-55Code / 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.
(PDF)
S2 File
Inclusivity in global research questionnaire.
(PDF)
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 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring remains challenging because of its underground location. The use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible. In this study, we investigated and compared the performance of RGB and hyperspectral imaging for predicting root dry weight in hydroponically grown spinach (Spinacia oleracea L.). RESULTS: Using 430 root segments divided from 60 plants, three models were developed: (1) an area-based regression based on root coverage, (2) a convolutional neural network (CNN) using RGB images, and (3) a partial least squares regression (PLSR) model using hyperspectral data (450-950 nm). The area-based regression exhibited limited accuracy (R² = 0.446) because of saturation at high root coverage. The CNN model improved predictive performance (R² = 0.739) but tended to overestimate sparse roots as a result of resolution constraints. The PLSR model achieved the highest accuracy (R² = 0.822, RMSE = 0.019 g/segment), with significantly lower error than RGB-based approaches (P < 0.01). Variable importance in projection analysis indicated that PLSR effectively exploited spectral signatures at 450 nm (background contrast) and 750 nm (tissue scattering), thereby maintaining stable accuracy across the full biomass range. When validated using 104 independent plants, the PLSR model achieved high predictive accuracy. Furthermore, as a proof of concept, this model successfully visualized the spatiotemporal dynamics of root biomass accumulation over 50 days, with only a 7.70% relative error at harvest. CONCLUSIONS: To our knowledge, this study is among the first to demonstrate the non-destructive monitoring of biomass distribution within entire root systems under production conditions. Hyperspectral imaging combined with PLSR outperforms RGB-based approaches by capturing spectral signatures that reflect internal tissue properties of roots, thereby overcoming limitations caused by morphological occlusion. This approach provides a robust tool for precision agriculture and high-throughput phenotyping, enabling continuous assessment of root growth through simple modifications to the existing hydroponic systems.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像と機械学習／PLSRを用いて根乾物重を非破壊推定・検証する方法研究であり、植物表現型の取得と定量化が中心である。
abstractThe use of transparent nutrient film technique (NFT) systems enables direct observation of entire root systems, rendering image-based phenotyping feasible.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific phenotyping assets (raw hyperspectral images, RGB images, and root dry weight measurements) in a Zenodo record. The provided URL includes a token and 'preview=1', suggesting the record may not yet be fully open, but it is the authors' stated public URLDataset · publicThe datasets generated and analyzed during the model construction of the current study are available in the Zenodo repository: [https://zenodo.org/records/18072801?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImFiYTkzMzY2LTIzZjktNDlkMy1iZTBjLTk3M2E5YTUyOTFmZCIsImRhdGEiOnt9LCJyYW5kb20iOiIzNGE1ZjMxNDZhYjhiYjlhZWRiOWFjNzBkNzcwY2I3NyJ9.uR4HfosoSaVWhtSblMOS1v9bJFA5MvHwXvcW9uoNbcTWRDU4RNxZpVHjXTC3ulBM1JTlBbeHp_4T5EcILawxdg].The dataset includes:
- Raw hyperspectral images and data- RGB images
- Root dry weight measurementsOpen asset ↗Zenodo · 18072801lines:176-248Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
None
1.
Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Developing climate-resilient wheat varieties requires combining high yield with stability across diverse environments, especially under increasingly variable precipitation and rising temperatures. This study evaluated 64 post-Green Revolution durum wheat cultivars under irrigated and rainfed conditions at two contrasting Mediterranean sites in Spain. A classification framework was developed to support genotype selection based on yield and yield stability, estimated using linear mixed models and yield slopes across environments. Genotypes were classified by interquartile thresholds, and those showing either low yield or low stability were considered undesirable for selection. High-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data. VIs and TIR at anthesis and grain filling, and their differences (senescence proxies), were used to train Random Forests for yield and stability estimation including sequential feature selection. Environmental covariates (water input, reference evapotranspiration) were integrated in yield models, with strong outcomes (R 2 > 0.74; MAPE <23.6%). Stability predictions were based on VI stability and, though moderate (R 2 up to 0.56; MAPE <17.75%), outperformed previous studies. Selected features were used to evaluate seasonal reflectance phenotypes: “keep” genotypes (intermediate/high yield or/and stability) exhibited early-vigor but lower green retention by the end of grain filling, while “discard” genotypes (low yield or/and stability) showed reduced early vigor and “stay-green” behavior. This study highlights early-vigor and earlier senescence over “stay-green” for wheat selection, offering a cost-effective approach shifting the breeding focus from yield maximization to joint yield-stability evaluation, promoting sustainability.
Why it matches plant phenotyping methods高スループットの地上・UAVセンサーによる表現型取得と、機械学習による収量・安定性推定が研究の中心であり、育種選抜に用いる手法を実質的に評価・適用している。
abstractHigh-throughput phenotyping was conducted throughout the season using ground-sensor Red-Green-Blue (RGB) and multispectral (MS) vegetation indices (VIs), along with UAV-derived RGB, MS, and thermal-infrared (TIR) data.
Reproduction assets foundThe authors explicitly state that the datasets and analysis scripts for all analyses (yield/stability modeling, VI extraction, Random Forest workflows) are publicly available in their Zenodo repository (DOI 10.5281/zenodo.17435708), referenced both in the statistical analysis section and the Data Availability statementCode · publicThe datasets and scripts for all the analyses conducted are available in our repository ( https://doi.org/10.5281/zenodo.17435708 ).Open asset ↗zenodo · 10.5281/zenodo.17435708lines:222-237Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11-57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near-Infrared spectra measured on leaves reflect phenotypic evolution unrelated to domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate phenotypic divergence index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild vs domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいmPDI指標を開発しており、植物形質の統合・比較手法が明示的な貢献であるため。
abstractestablished a framework for cross-species comparisons
Reproduction assets foundThe paper's phenotypic data, NIR spectra, and trait ontology are deposited at doi 10.57745/QWEKVK, and the authors' R analysis scripts are publicly available on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.
Why it matches plant phenotyping methodsリモートセンシングによる林冠高構造の定量と生物学的成長モデルを組み合わせ、森林の構造回復をスケーラブルにモニタリングする枠組みが研究の中心である。
abstractWe extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the analysis data and code on Zenodo with a public DOI, which is a paper-specific, publicly actionable asset for reproducing the forest recovery analysis.Code · publicthank three anonymous reviewers for providing helpful
suggestions on an earlier version of the work.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.foreco.2026.123616.
Data availability
Data and code of the analysis are available at Zenodo: https://doi.org/10.5281/zenodo.17804070.References
Anderson-Teixeira, Kristina J., Miller, Adam D., Mohan, Jacqueline E., Hudiburg, Tara
W., Duval, Benjamin D., DeLucia, Evan H., 2013. Altered Dynamics of Forest
Recovery under a Changing Climate. Glob. Change Biol. 19 (7), 2001–2021. https://
doi.org/10.1111/gcb.12194.
Arano, Kathryn G., Munn, Ian A., 2006. Evaluating Open asset ↗Zenodo · 10.5281/zenodo.17804070pdf-raw-page:10 lines:1-55Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Grapevines ( Vitis vinifera L.) undergo structural and physiological changes throughout the growing season, progressing through distinct phenological stages that require regular monitoring. This dataset consists of high-resolution point cloud data acquired with a stationary terrestrial laser scanner (TLS) to document grapevine development from early leaf development to dormancy. Georeferenced point clouds were generated from 15 TLS scans along two vineyard rows at nine phenological stages. The dataset also includes multispectral and RGB photogrammetric point clouds and orthorectified raster products from an unmanned aerial vehicle survey conducted before harvest. Ground-truth measurements leaf area index, grape production, and pruning wood biomass were collected for each monitored grapevine. As a result, the dataset provides multi-temporal TLS observations that support grapevine structural analysis and development, phenological monitoring, and can be used for the development of AI-based models for precision viticulture.
Why it matches plant phenotyping methodsブドウの生育・構造・フェノロジーを対象とするTLS点群および関連画像データセットであり、植物フェノタイピング用の再利用可能なデータ基盤として中心的です。
titleTLS-grapevine2024: A terrestrial laser scanner point cloud dataset of grapevines at different phenological stages.
Reproduction assets foundThe paper is a Data in Brief article describing the TLS-grapevine2024 dataset itself, publicly deposited on Zenodo with DOI 10.5281/zenodo.16751663. This is a paper-specific, openly available asset containing the TLS point clouds, UAV imagery/rasters, and ground-truth agronomic measurements (LAI, grape production, prunDataset · publicditions: clear sky.
Data source location
Institution: University of Trás-os-Montes e Alto Douro
City/Town/Region: Arroios, Vila Real, Norte
Country: Portugal
Coordinates: 41°17′28.83″N 7°43′17.90″W,
Altitude: 435 m
Data accessibility
Repository name: Zenodo
Data identification number: 10.5281/zenodo.16751663
Direct URL to data: https://doi.org/10.5281/zenodo.16751663
Related research article
None
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Value of the Data
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This dataset covers nine phenological stages of grapevine growth from April 2024 to January 2025, providing multi-temporal terrestrial laser scanner (TLS) observations for structural and phenological analysis.
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It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use‐case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methodsPlantCV v4は、画像から植物形質を自動抽出するオープンソースソフトウェアの開発・機能拡張・比較評価を主題としており、植物フェノタイピング手法が中心である。
abstractPlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' analysis scripts on GitHub (danforthcenter/plantcv-4-paper), which directly reproduces this paper's phenotyping analyses.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-97Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Segmentation of hyperspectral image data is a well-established technique in remote sensing. While it is commonly applied to individual field crops, its use for individual trees is less prevalent. Conifers are crucial in forestry, and assessing physiological status, or genetic diversity is required for effective early-age treatment in nurseries and hyperspectral imaging (HSI) combined with high-throughput phenotyping (HTP) offers faster and non-destructive evaluation. NDVI-based thresholding is sufficient for detection of leaves with large projection areas, but needles of conifers present challenges due to spatial resolution constraints and increased proportion of border pixels. This study monitored the offspring of three locally adapted Scots pine (Pinus sylvestris L.) populations, representing distinct upland and lowland ecotypes. This study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings. Using a K-means algorithm, 23 hyperspectral centroids were successfully derived and subsequently classified into ten biologically distinct groups. Random forest classification model effectively differentiated Scots pine seedlings based on origin during water stress and recovery periods. This study highlights the potential of hyperspectral imaging and machine learning in evaluating the physiological state of conifer seedlings, demonstrating promising applications in forest tree physiology research and tree breeding.
Why it matches plant phenotyping methods個体のマツ苗を分離・セグメンテーションするハイパースペクトル画像処理パイプラインを開発し、機械学習で生理状態や由来を評価しており、表現型取得手法が中心である。
abstractThis study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings.
Reproduction assets foundThe paper's Data availability statement explicitly deposits demonstration hyperspectral sample data on Zenodo and the segmentation/classification scripts on GitHub; both are paper-specific, public, and actionable. Full experimental data is request-only and not listed as a public asset.Dataset · publicDemonstration sample data and their accompanying descriptions are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17167809).Open asset ↗Zenodo · 10.5281/zenodo.17167809lines:161-192Code · publicThe scripts developed for this study are available on GitHub at: https://github.com/JCepl/Pine-hyperspectral-image-segmentaionCompleteOpen asset ↗GitHub · JCepl/Pine-hyperspectral-image-segmentaionCompletelines:161-192Code / 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 confirmedOpenAlex · checked 5 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Multispectral / hyperspectralFlowerObject detectionStress response / tolerancePlant / canopy temperature
Hyperspectral imaging (HSI) is a noncontact camera-based technique that enables deep learning models to learn various plant conditions by detecting light reflectance under illumination. In this study, we investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses, which are vulnerable to abiotic stresses. Cut roses 'All For Love' and 'White Beauty' were used to compare cultivar-specific visible reflectance characteristics associated with contrasting petal pigmentation. HSI was performed at four time points, yielding 640 images per light source from 40 cut roses. The results revealed that the light source strongly affected both the image quality (mAP@0.5 60-80%) and VL (0-3 d) of cut roses. The HAL lamp produced high-quality spectral images across wavelengths (WL) ranging from 480 to 900 nm and yielded the highest object detection performance (ODP), reaching mAP@0.5 of 85% in 'All For Love' and 83% in 'White Beauty' with the YOLOv11x models. However, it increased petal temperature by 2.7-3 °C, thereby stimulating leaf transpiration and consequently shortening the VL of the flowers by 1-2.5 d. In contrast, INC produced unclear images with low spectral signals throughout the WL and consequently resulted in lower ODP, with mAP@0.5 of 74% and 69% in 'All For Love' and 'White Beauty', respectively. The INC only slightly increased petal temperature (1.2-1.3 °C) and shortened the VL by 1 d in the both cultivars. Although FLU and LED had only minor effects on petal temperature and VL, these illuminations generated transient spectral peaks in the WL range of 480-620 nm, resulting in decreased ODP (mAP@0.5 60-75%). Our results revealed that HAL provided reliable, high-quality spectral image data and high object detection accuracy, but simultaneously had negative effects on flower quality. Our findings suggest an alternative two-phase approach for illumination applications that uses HAL during the initial exploration of spectra corresponding to specific symptoms of interest, followed by LED for routine plant monitoring. Optimizing illumination in HSI will improve the accuracy of deep learning-based prediction and thereby contribute to the development of an automated quality sorting system that is urgently required in the cut flower industry.
Why it matches plant phenotyping methods切り花の状態評価に用いるHSIについて、照明条件が画像品質と検出精度に及ぼす影響を比較・検証し、実運用向けの照明戦略を提案しているため、植物フェノタイピング手法が中心である。
abstractwe investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S1: SNR of hyperspectral images acquired under different illumination sources in two cut rose cultivars (‘All For Love’ and ‘White Beauty’); Figure S1: Effect of light sources on hyperspectral image (HSi) quality in cut roses ‘All For Love’ and ‘White Beauty’.Open asset ↗lines:64-174Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms.
Why it matches plant phenotyping methods航空機搭載ハイパースペクトルセンサーによる植物表現型時系列データから潜在特徴を抽出し、収量予測に統合する解析手法が研究の中心であるため。
abstractfactor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction
Reproduction assets foundThe paper's Data and code statement provides public GitHub repositories containing the authors' analysis scripts for the hyperspectral latent-factor/Procrustes workflow and the glfBLUP R package implementing the genomic prediction methodology. The hyperspectral phenotype dataset itself is only available upon request, iCode · publicy of secondary trait data and successful integration in multivariate genomic prediction.
As such, this method can contribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning mOpen asset ↗KunstJF/glfBLUP-Procrusteslines:388-492Code · publicntribute to a greater understanding of high-dimensional data in plant breeding trials.
Data and code
Scripts to generate the hyperspectral datasets, as well as the results presented in this paper, are available at https://github.com/KunstJF/glfBLUP-Procrustes . The glfBLUP methodology is implemented in an R-package available at https://github.com/KillianMelsen/glfBLUP . The hyperspectral dataset is available upon reasonable request from J. Crossa
References
Antonio et al. (2022)
O. Antonio, M. López, A. Montesinos López, and J. Crossa
Multivariate statistical machine learning methods for genomic prediction .
Springer , Cham, Switzerland .
External Links: ISBN 978-3-030-89009-4 978-3-030-8901Open asset ↗KillianMelsen/glfBLUPlines:388-492Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.
Why it matches plant phenotyping methods複数センサー・ロボットプラットフォームによる圃場フェノタイピング用データセットを構築・提供し、形質推定法の開発、センサー比較、汎化評価を可能にすることが中心的な貢献である。
abstractwe present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset
Reproduction assets foundThe paper's MuST-C multi-sensor, multi-temporal crop phenotyping dataset (RGB/multispectral images, LiDAR point clouds, LAI and biomass reference measurements) is publicly available via the authors' project webpage, and the authors' custom Python processing/loading code is publicly available on GitHub.Dataset · publicThe MuST-C dataset is available via our project webpage https://www.ipb.uni-bonn.de/data/MuST-C/or directly via the bonndata public access repository 10.60507/FK2/OX9XTM34Open asset ↗html-lines:421-440Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
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 I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Timely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI's superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.
Why it matches plant phenotyping methods植物ストレス状態を推定する動的植生指数と強化学習フレームワークを開発し、実圃場・ベンチマークデータで性能検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection.
Reproduction assets foundThe paper publicly releases its authors' field-captured mobile RGB rice canopy dataset on Kaggle and its full RL-VI analysis code (RL formulation, preprocessing, VI computation, training, evaluation) on GitHub. Sentinel-2 imagery and benchmark datasets are third-party public sources, not paper-specific deposits.Dataset · publicThe Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)).Open asset ↗Kaggle · 10.34740/kaggle/dsv/14105754html-lines:616-683Code · publicAll custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).Open asset ↗GitHub · Poornisrm/Vegetation-Indexhtml-lines:684-711Code / 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 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants move chloroplasts in response to light, changing the optical properties of leaves. Low irradiance induces chloroplast accumulation, while high irradiance triggers chloroplast avoidance. Chloroplast movements may be monitored through changes in leaf transmittance and reflectance, typically in red light. We present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light. We show how to employ machine learning methods to classify leaves according to the chloroplast positioning. The convolutional network is a method of choice for the analysis of the reflectance spectra, as it allows low levels of misclassification. As a complementary approach, we propose a vegetation index, called the Chloroplast Movement Index (CMI), which is sensitive to chloroplast positioning. Our method offers a high-throughput, contactless way of chloroplast movement detection. Key features • Protocol for detached leaves handled in laboratory conditions. • Based on differential (dark-adapted versus irradiated) hyperspectral images of plant leaves. • Data analysis includes machine learning methods and the calculation of a vegetation index. • Requires irradiation equipment apart from the hyperspectral camera set.
Why it matches plant phenotyping methods葉の反射ハイパースペクトル画像から葉緑体位置を検出・分類する手法と指標を開発し、高スループット測定として提示しており、植物表現型取得が中心である。
abstractWe present a step-by-step procedure for the detection of chloroplast positioning using reflectance hyperspectral imaging in white light.
Reproduction assets foundThe protocol explicitly deposits its authors' analysis code (HyperspectralImageProcessing.m, including the pretrained CNN classifier for chloroplast positioning) on GitHub and makes the original hyperspectral images of Arabidopsis and Nicotiana leaves used in the paper's figures available on figshare. Both are paper-‐Code · publicAll code has been deposited to GitHub: https://github.com/plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detection (access date, 08/18/2025)Open asset ↗plantPhotobiologyLab/machine-learning-for-chloroplast-movement-detectionhtml-lines:104-130Dataset · publicOriginal files with hyperspectral images of Nicotiana benthamiana and Arabidopsis thaliana (WT and phot2) leaves, including recordings shown in Figure 3 and Figure 4 of this protocol, can be downloaded from https://figshare.com/articles/dataset/Hyperspectral_images_of_Arabidopsis_thaliana_and_Nicotiana_benthamiana_leaves/30402409?file=58898569Open asset ↗html-lines:104-130Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoGreenhouseMultimodalRGB / grayscaleMultispectral / hyperspectralFruitClassificationSegmentationGrowth / development / phenology
Computer vision and multispectral imaging have increasingly become essential tools in modern precision agriculture. Accurate ripeness assessment is critical for yield optimization, reducing post-harvest losses, and enabling automated harvesting systems. However, traditional RGB-based approaches struggle to differentiate subtle maturity changes, and existing solutions often fail under varying lighting, occlusion, or cultivar-specific conditions. To address these challenges, this study focuses on the integration of complementary spectral cues for reliable tomato ripeness evaluation. The work utilizes a curated RGB-NIR tomato dataset comprising 224 hyperspectral samples, processed into aligned multimodal image pairs with balanced ripeness categories.The proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues. The novelty of the methodology lies in the dynamic cross-attention mechanism, which learns inter-modal dependencies between RGB and NIR signals for enhanced ripeness interpretation. Performance metrics including accuracy, precision, recall, F1-score, mIoU, and AUC were used to comprehensively evaluate the system. Experimental results demonstrate that TomatoRipen-MMT significantly outperforms all baseline RGB-only, NIR-only, and fusion methods, achieving 94.8% classification accuracy and 82.6% mIoU. These findings establish the effectiveness of multimodal Transformers for robust, high-precision fruit maturity assessment in controlled and greenhouse environments.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を、RGB・NIR画像融合とTransformerで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す。
abstractThe proposed TomatoRipen-MMT model employs a multimodal Transformer framework with dual encoders, cross-spectral attention, and a joint decoder to fuse spatial and biochemical cues.
Reproduction assets foundThe paper's phenotyping analysis is built on a publicly available USDA/NAL hyperspectral tomato dataset, explicitly linked in the Data Availability statement with an exact URL match. No author code or model checkpoints are disclosed.Dataset · publicThe dataset analyzed in this study is publicly available at the https://agdatacommons.nal.usda.gov/articles/dataset/Data_from_b_Hyperspectral_Imaging_Analysis_for_Early_Detection_of_Tomato_Bacterial_Leaf_Spot_Disease_b_/26046328.Open asset ↗26046328html-lines:1038-1053Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints are explicitly deposited.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://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Repeated occurrences of extreme weather events, such as low temperatures, due to global warming present a serious risk to the safety of wheat production. Quantitative assessment of frost damage can facilitate the analysis of key genetic factors related to wheat tolerance to abiotic stress. We collected 491 wheat accessions and selected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage. Image descriptors can complement the visual estimation of frost damage. Combined with genome-wide association study (GWAS), a total of 107 quantitative trait loci (QTL) (r 2 ranging from 0.75% to 9.48%) were identified, including the well-known frost-resistant locus Frost Resistance (FR)-A1/ Vernalization (VRN)-A1. Additionally, through quantitative gene expression data and mutation experience verification experiments, we identified two other frost tolerance candidate genes TraesCS2A03G1077800 and TraesCS5B03G1008500. Furthermore, when combined with genomic selection (GS), image-based descriptors can predict frost damage with high accuracy (r ≤ 0.84). In conclusion, our research confirms the accuracy of image-based high-throughput acquisition of frost damage, thereby supplementing the exploration of the genetic structure of frost tolerance in wheat within complex field environments.
Why it matches plant phenotyping methods小麦の霜害を画像記述子で定量評価し、その精度を検証しているため、画像ベース植物フェノタイピングが研究の中心です。
abstractselected four image-based descriptors (BLUE band, RED band, NDVI, and GNDVI) to quantitatively assess their frost damage.
Reproduction assets foundThe paper's data processing code is publicly available on GitHub. Genotype and phenotype data are only available on reasonable request, so they do not qualify as public assets.Code · publicThe data processing code presented in this study is available on the website https://github.com/yurui2024/Frost-tolerance .Open asset ↗yurui2024/Frost-tolerancelines:156-271Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Problems Tomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control. Methodology A UAV-mounted hyperspectral camera (400-1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms-support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting -with the derived feature variables. Results This study demonstrates that while all integrated models combining Relief- and Correlation- selected feature bands with three machine learning algorithms delivered excellent performance, the SVM-Relief model achieved the most outstanding results (OA = 97.3%, AUC = 0.994, Kappa=0.947). Based on the SVM-Relief combination, a proposed method called RPR -which integrates PCA with recursive feature elimination- was further employed to reduce the number of feature indicators from 15 to 4 (775.6/772.9/781.1/756.4 nm). The resulting SVM-RPR combination model achieved performance (OA = 97.3%, AUC = 0.990, Kappa=0.947) comparable to that of the SVM-Relief model. Contribution This indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習により、タバコ個体のウイルス感染状態を圃場規模で推定する手法の開発・評価が研究の中心であるため。
abstractThis study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control.
Reproduction assets foundThe paper states its tobacco TSWV UAV hyperspectral dataset is publicly available on GitHub, matching an allowed URL.Dataset · publicThe datasets are available in the GitHub repository ( https://github.com/smith22357/hyperspectral-dataset : tobacco TSWV hyperspectral-dataset).Open asset ↗smith22357/hyperspectral-dataset · tobacco TSWV hyperspectral-datasetlines:369-377Code / 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 confirmedEurope PMC · checked 6 Sept 2026
Vegetation vertical structure refers to the 3D distribution of vegetation aboveground biomass. Vegetation vertical structure of tropical forests influences other ecological and environmental variables that are essential for the functioning of the ecosystems. Integrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020. We mapped canopy height, the height of half the cumulative returned energy from GEDI (RH50), total canopy cover, foliage height diversity, and total plant area index. The resulting maps tended to have the highest errors in the Amazon and Andean regions. Total cover had the highest relative error. Interrelationship curves between forest structural metrics of GEDI footprints are maintained across mapped metrics, indicating that the predictive models preserve structural relationships observed in GEDI data. Due to the medium-high spatial resolution and national coverage of the forest structural maps presented in this work, these maps will be useful for evaluating and mapping other ecological variables and conservation priorities in Colombia.
Why it matches plant phenotyping methodsGEDI LiDAR・マルチスペクトル・SARを統合し、森林キャノピー高、被覆率、葉群高多様性、植物面積指数などの植物構造形質を全国規模で推定・検証することが中心であり、単なる生態学的応用ではない。
abstractIntegrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020.
Reproduction assets foundThe paper's resulting forest vertical structure maps (CH, COVER, FHD, PAI, RH50 for Colombia, 2020) are publicly available on Zenodo and via Google Earth Engine assets, and the authors' analysis code is publicly available on GitHub. These are paper-specific, public, actionable assets.Code · publicCode availability
The code is publicly accessible on Github76: https://github.com/CamiloFaguaUNAL/Forest_Structure_Colombia.Open asset ↗GitHubhtml-lines:731-755Code / 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
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Why it matches plant phenotyping methods植物の生育段階・生理・構造特性を対象に、標準化されたマルチスペクトル画像とフィールド分光データを収集・前処理した再利用可能なデータセットであり、ベンチマークやアルゴリズム検証を目的とするため、フェノタイピング手法が中心です。
abstractThis data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo)
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
Related research article
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1.
Value of the Data
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These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Precision agriculture technologies based on satellite remote sensing remain largely inaccessible to smallholder farmers in developing countries due to technical complexity, cost barriers, and infrastructure demands. This study presents the design and implementation of an open-source, web-based platform for processing Sentinel-2 Level-2A imagery tailored to the specific needs of family farming systems. The platform integrates a FastAPI backend for geospatial data processing with a Next.js frontend providing simplified tools for spectral index computation (NDVI, EVI, SAVI, NDWI, NDBI), crop classification using supervised and unsupervised machine learning, and interactive 2D/3D visualization. A laboratory module implements thirteen digital image processing techniques—including Gaussian filtering, edge detection, morphological operations, and thresholding—for educational and comparative analysis. The browser-based system eliminates installation requirements and automates key workflows such as coordinate reprojection, JP2 band extraction, and statistical evaluation. Validation using ground-truth data from coffee and soybean fields in the Brazilian Cerrado achieved classification accuracies above 85% and correlation coefficients exceeding 0.90 for biomass estimation based on NDVI-derived metrics. The platform contributes to the democratization of remote sensing technologies and enhances accessibility of precision agriculture tools for smallholder farmers.
Why it matches plant phenotyping methods植物圃場の衛星画像を処理し、NDVI等からバイオマスを推定するオープンソース基盤の設計・実装・検証が中心であり、植物形質推定ワークフローとして収録対象。
titleAn Open-Source Web Platform for Sentinel-2 Multispectral Analysis in Smallholder Agriculture: Design, Implementation and Validation
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' complete source code, documentation, and example datasets for the Sentinel-2 phenotyping/analysis platform on a public GitHub repository under MIT license. Sentinel-2 imagery is from the public Copernicus browser, but that is a generic data sourceCode · publicresearch received no external funding
Institutional Review Board Statement: Not applicable. This study did not involve humans or animals.
Informed Consent Statement: Not applicable. This study did not involve humans.
Data Availability Statement: Complete source code, documentation, and example datasets are publicly available
at https://github.com/rexionmars/icev-remote-sensing under MIT license. The platform can be deployed locally
or accessed via hosted instance for testing purposes. Sentinel-2 satellite imagery used in this study was obtained
from the Copernicus Open Access Hub (https://browser.dataspace.copernicus.eu/) and is freely available.
Acknowledgments: The authors thank the iCEV IOpen asset ↗https://github.com/rexionmars/icev-remote-sensing · icev-remote-sensingpdf-layout-page:11 lines:1-70Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.
Why it matches plant phenotyping methods小麦葉の感染状態をハイパースペクトル画像と深層学習で分類する手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractHyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' training/deployment/testing code for the hyperspectral wheat disease classification models in a public GitHub repository. The 1447-image hyperspectral dataset itself has no stated public deposit, so it is not included as an asset.Code · publicCode for training, deploying and testing the models can be found at https://github.com/mc2295/hyperspectralplants .Open asset ↗mc2295/hyperspectralplantslines:140-218Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In the arid cultivation region of Xinjiang, China, shrinkage disease severely compromises the quality, yield, and market value of jujube. Published research has achieved high accuracy in detecting larger lesions using RGB imaging and hyperspectral imaging (HSI). However, these methods lack sensitivity in detecting early and subtle symptoms of disease. In this study, a multi-source data fusion strategy combining RGB imaging and HSI was proposed for non-destructive and high-precision detection of early-stage jujube shrinkage disease. Firstly, a total of 317 fruits of the 'Junzao' cultivar were collected during multiple stages of natural infection, covering early-stage shrinkage disease detection across different growth stages, including both green and mature red fruits. Secondly, morphological features were extracted from RGB images in multiple dimensions, while a three-stage feature selection strategy combining Principal Component Analysis (PCA), the Successive Projections Algorithm (SPA), and the Genetic Algorithm (GA) was implemented to identify four key wavelengths from HSI. Thirdly, a hybrid convolutional neural network-multilayer perceptron (CNN-MLP) architecture was constructed, with dynamic feature weighting employed to achieve effective multimodal fusion and optimize detection performance. Experimental results demonstrated that compared to the MLP and CNN models, the proposed method achieved approximately 8.0% and 5.4% improvements in accuracy and 38.6% and 32.4% improvements in F1 scores, respectively. It offers a robust and scalable solution for early disease detection and postharvest quality assessment in jujube production.
Why it matches plant phenotyping methodsRGB画像・HSIから果実の病斑形態と分光特徴を抽出し、マルチモーダル深層学習で植物病害状態を検出する手法の開発・性能評価が中心であるため。
abstracta multi-source data fusion strategy combining RGB imaging and HSI was proposed for non-destructive and high-precision detection of early-stage jujube shrinkage disease.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the study's dataset (RGB images and hyperspectral data of jujube fruits). No separate analysis code availability is stated, but the deposited dataset is a paper-specific, publicly actionable asset.Dataset · publicThe data from this study are publicly available. The dataset is available at https://github.com/2484733079/Early-detection-of-Jujube-Shrinkage-Disease-by-Multi-source-Data-on-Multi-task-Deep-Network.git (accessed on 13 October 2025).Open asset ↗2484733079/Early-detection-of-Jujube-Shrinkage-Disease-by-Multi-source-Data-on-Multi-task-Deep-Networklines:365-367Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
We investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies. Rather than relying on a single vegetation index, we utilize all available data collectively to predict the human-assigned visual score (VS). The conceptual motivation is that when a trained model is available, these predictions may provide a more accurate assessment of disease symptoms than the use of a specific vegetation index (VI). To evaluate the PP approach, we employ the predicted VS in a genome-wide association study (GWAS) and consider strength and position of the detected genetic signal. We use two different sets of predictor variables: i) the five basic wavelengths captured by a multispectral and a thermal camera (basic traits model, BT) or ii) all traits (AT), consisting of the five basic wavelengths plus ten vegetation indices. As statistical methods, we compare a) (linear) ordinary least squares regression (OLS), b) (linear) ridge regression (RR), c) (linear) least absolute shrinkage and selection operator (LASSO) d) an artificial neural network (ANN) and e) a gradient boosted regression tree method (GBRT). Our results indicate that the simple linear OLS regression on the five basic wavelengths (BT-OLS) performs on a level comparable to the best individual vegetation index G. The use of all traits in the OLS regression (AT-OLS) leads to overfitting, which was prevented by the regularization in AT-RR and AT-LASSO. The non-linear ANN approach seems to improve the results further, but the differences between the methods were not statistically significant. The strongest improvement for the purification of the genetic signal was observed when genomic estimated breeding values (GEBVs) for the different traits (VS, basic wavelengths, vegetation indices) instead of their adjusted phenotypes were used. Across all approaches, the combination of GEBVs with Ridge Regression or the non-linear ANN provided the best results.
Why it matches plant phenotyping methodsリモートセンシング画像・センサーデータからトウモロコシさび病の視覚的症状スコアを推定する複数の統計・機械学習手法を比較評価しており、表現型取得・抽出法が研究の中心である。
abstractWe investigate the potential of phenomic prediction (PP) in remote-sensing-based phenotyping for genetic studies.
Reproduction assets foundThe paper's phenotypic (visual scores, adjusted phenotypes) and remote-sensing (multispectral/thermal) data, plus genomic marker data, are publicly deposited in the CIMMYT Research Data repository. No authors' analysis code or trained model checkpoints are deposited; R packages cited are generic libraries.Dataset · publicWe use the data previously published by Loldaze et al. [ 21 , 22 ], which is available on the CIMMYT Research Data repository at https://hdl.handle.net/11529/10548898 .Open asset ↗CIMMYT Research Data repository · 11529/10548898lines:35-47Code / 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 confirmedCrossref · checked 6 Sept 2026
ABSTRACT As an essential species across European forests, Scots pine ( Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time‐consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single‐nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid‐to‐total‐chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water‐related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress‐related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype‐by‐environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate‐adaptive breeding in Scots pine.
Why it matches plant phenotyping methods針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。
abstracthyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code, Dataset · publicThe data supporting the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.27134907.v2 .Open asset ↗Figshare · 10.6084/m9.figshare.27134907.v2lines:454-598Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (
Why it matches plant phenotyping methods植物の病徴が現れる前の茎さび病状態を、ハイパースペクトル画像と機械学習で検出・推定する方法の開発および転移性検証が中心である。
abstractThis study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the hyperspectral imaging datasets (864 hyperspectral cubes of wheat and barley, control and stem-rust-inoculated) used for the phenotyping and machine learning analysis. No code or model checkpoints are explicitly deposited.Dataset · publicData is available via online download link https://drive.google.com/drive/folders/1qAgWEAH5BruTNmT0bmSm4HTbXe_iitap (accessed on 21 October 2025).Open asset ↗1qAgWEAH5BruTNmT0bmSm4HTbXe_iitaplines:233-282Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
ABSTRACT The development of remote sensing methods to estimate plant functional diversity is limited by mismatches between ecology and remote sensing sampling schemes, and the limited representativeness of local field campaigns. The Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies. We used BOSSE to simulate 180 different synthetic “Scenes” encompassing a two-year-long time series of plant trait maps and imagery of hyperspectral reflectance factors, spectral indices, sun-induced chlorophyll fluorescence, land surface temperature, and estimates of plant traits (optical traits). We used these simulations to answer five fundamental, yet unsolved, questions: Q1. How should remote sensing characterize functional diversity in large surfaces (sites)? Diversity metric values saturate with the number of pixels involved, hampering comparisons between plant traits and remote sensing estimates in large areas. The average value of metrics computed over small samples should be used instead. Q2. Which sources of spectral information (or combinations thereof) can best capture plant functional diversity at the site scale? Accounting for background effects is the key. Optical traits (remote sensing estimates of plant traits) are the best estimators for plant functional diversity. Other variables succeed when filtered out of the soil pixels; their combination did not yield additional advantages. Q3. How should remote sensing estimates be validated/compared with plant functional diversity measurements? Leaf area index (LAI) is a better proxy of abundance than the pixel for Q Rao, but not for variance-based partitioning. It is more sensitive to sample size, but also more resistant to suboptimal spatial resolution. Q4. When (in the phenological year) can remote sensing best capture site-scale plant functional diversity? The estimation error decreased with LAI and stabilized at values above 1 m²/m². Q5. Which approaches and remote sensing variables are more resistant to the effects of suboptimal spatial resolution? Optical traits, fluorescence, and reflectance factors were the most robust variables. Still, field data resolution needs to be degraded to match the sensor’s resolution. We found a relative spatial resolution threshold of ∼30 % (where the pixel is around three times larger than the plants). Simulation frameworks like BOSSE enable testing methodologies beyond local contexts and address the current shortage of suitable global datasets, supporting the application and development of methods for assessing plant functional diversity with remote sensing. In the future, BOSSE could contribute to understanding observational results, refining and pre-testing new methodologies, and supporting the development of comparable experimental datasets.
Why it matches plant phenotyping methodsBOSSEを用いてリモートセンシングによる植物形質・機能多様性推定手法をシミュレーションベンチマークし、検証・比較する研究であり、植物フェノタイピング手法が中心である。
abstractThe Biodiversity Observing System Simulation Experiment (BOSSE) provides a modeling framework for benchmarking new methodologies.
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 · publicvariables, we used the “pyGNDiv” package (https://github.com/JavierPachecoLabrador/pyGNDiv-Open asset ↗JavierPachecoLabrador/pyGNDiv- · pyGNDivpdf-page:11 lines:1-60Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines ( Hordeum vulgare ) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage emerged as key classification features. A temporal phenomic prediction model of harvest-related traits achieved particularly high mean R 2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Importantly, prediction accuracy for these traits remained high (R 2 ≥ 0.84) even when restricted to early developmental phase data, including the stem elongation stage. Models trained on pooled drought and control data outperformed single-treatment models and maintained high predictive power across treatments. Together, these findings highlight the value of integrating high-throughput phenotyping with temporal modeling to enable earlier, more cost-effective selection of drought-resilient genotypes and demonstrate the broader potential of phenomics-driven strategies for accelerating crop improvement under stress-prone environments.
Why it matches plant phenotyping methodsRGB・熱赤外・蛍光・ハイパースペクトルによる時系列表現型取得と、収穫形質予測モデルの構築・評価が研究の中心であるため。
abstractdynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors
Reproduction assets foundThe authors explicitly state that the data and analysis pipeline code for this barley phenotyping study is publicly available on GitHub at https://github.com/hatiez/barley-TPP-pipeline. This is a paper-specific computational asset (the temporal phenomic classification/prediction pipeline) with an authors' public URL. DCode · publicThe data and analysis pipeline code is available on https://github.com/hatiez/barley-TPP-pipeline .Open asset ↗https://github.com/hatiez/barley-TPP-pipelinelines:390-415Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Potato cyst nematodes pose a major threat to potato cultivation, with infestations often going undetected for years. Early and accurate detection is crucial for effective management, necessitating reliable, large-scale monitoring methods. Hyperspectral imaging shows great promise for non-invasive nematode detection, yet distinguishing between biotic (e.g., nematodes) and abiotic (e.g., drought) stressors remains a challenge. This study investigated the stress responses of potato plants to potato cyst nematodes Globodera rostochiensis and G. pallida , and water deficiency. We generated datasets to isolate and evaluate single and combined stressor effects on plant physiology and morphology. Various machine learning models and spectral processing techniques were applied to assess classification performance. Exploratory methods identified key spectral wavelengths, while statistical analyses evaluated the significance of physiological and morphological traits. Results showed that water deficiency was the dominant classification factor (F1 = 0.95). The distinction between infected and non-infected plants reached F1 = 0.70 in well-watered conditions and 0.80 in water-deficient plants. Distinguishing nematode species and inoculation levels yielded moderate accuracy (F1 = 0.65-0.80), improving to 0.80 when combining biotic and abiotic stress. However, classifying multiple stress categories simultaneously reduced performance (F1 = 0.58). These findings highlight the challenges of stressor separation and the potential of hyperspectral imaging for nematode detection. Further research is needed to refine classification models and validate findings under field conditions, facilitating the integration of hyperspectral imaging into precision agriculture.
Why it matches plant phenotyping methodsジャガイモの感染状態・生理／形態ストレスをハイパースペクトル画像と機械学習で推定する手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractHyperspectral imaging shows great promise for non-invasive nematode detection
Reproduction assets foundThe authors explicitly state that processed hyperspectral data plus morphology and physiology measurements are publicly available on Zenodo, and their analysis code is on GitHub. Both are paper-specific, public, and actionable. The SiaPy library is a generic third-party tool and is excluded.Code · publicthe code repository at https://github.com/Manuscripts-code/Potato-plants-nemdetect--PP-2025 (accessed on October 5, 2025)Open asset ↗github · Manuscripts-code/Potato-plants-nemdetect--PP-2025lines:539-558Code / 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 confirmedCrossref · checked 6 Sept 2026
Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection.
Why it matches plant phenotyping methodsトウモロコシの病徴・病害重症度をUAV画像および衛星データから推定する深層学習・時系列解析パイプラインが研究の中心であり、植物状態の取得・評価手法に該当する。
abstracttraining deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease
Reproduction assets foundThe paper's UAV maize disease imagery is a public Kaggle dataset (corn disease drone images from Cornell's Musgrave Research Farm) explicitly cited as the source of the 9967 images and 42,117 annotations used for training the deep learning classifiers. No author analysis code, trained models, or processed MODIS/weatherDataset · public25. UAV dataset Musgrave Research Farms. Available online: https://www.kaggle.com/datasets/alexanderyevchenko/corn-Open asset ↗Kaggle · alexanderyevchenko/corn-pdf-page:31 lines:57-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Summary We used domestication as an in vivo replicated experiment to investigate how divergent selection has shaped the evolution of multivariate phenotypic spaces. We measured 11 to 57 qualitative and quantitative traits in 13 species, either unique or shared between species, and established a framework for cross-species comparisons. Our results revealed significant convergence that translated into a cross-species domestication syndrome. Most species exhibited a reduction of the multivariate phenotypic space during domestication. We brought evidence that Near Infrared spectra measured on leaves reflect phenotypic evolution unrelated with domestication, enabling its use as a control for sampling effects across species. Building on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication. We found a high disjunction of wild and domestic phenotypic spaces in all species. Neither the mPDI nor the relative size of wild versus domestic multivariate phenotypic spaces was influenced by the domestication timing or mating system. Lastly, we observed a progressive decoupling of trait correlations with increasing time since domestication. In addition to introducing a new index that can be applied for cross-species comparisons, our study uncovers recurring patterns shared among species, pointing to general principles underlying plant domestication.
Why it matches plant phenotyping methods多変量形質空間を比較する枠組みと新しいPhenotypic Divergence Index（mPDI）を開発しており、植物表現型の定量・比較手法が主要な貢献です。
abstractBuilding on this, we developed a multivariate Phenotypic Divergence Index (mPDI) to rank species by the extent of phenotypic divergence under domestication.
Reproduction assets foundThe paper's phenotypic data, NIR spectra, trait ontology, and R analysis scripts are explicitly deposited publicly: phenotype/NIRS data and MIAPPE trait ontology at doi 10.57745/QWEKVK, and R scripts on INRAE Forge. Both are paper-specific, public, and actionable.Dataset · publicPhenotypic data and NIR spectra are available on https://doi.org/10.57745/QWEKVK .Open asset ↗10.57745/QWEKVK · 10.57745/QWEKVKlines:283-349Code · publicR scripts are available on the INRAE Forge at https://forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaces .Open asset ↗forge.inrae.fr/gqe‐gevad/domisol_phenotypic_spaceslines:283-349Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing yield prediction models for an entire field or individual plots are based on satellite-derived vegetation indices (VIs) and widely used machine learning-based feature extraction models, including principal component analysis (PCA) and autoencoders (AE). Here, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery. Our approach uses a dataset of approximately 4,000 satellite images collected from replicated plots of 84 hybrid maize varieties grown at five distinct locations across the U.S. Corn Belt. By deploying the CAE model, we improve the separation of genotype and environment effects, enabling more accurate incorporation of genotype-by-environment (GxE) interactions for downstream prediction tasks. Results show that the CAE-based features improve early-stage yield predictions by up to 10% compared to traditional autoencoder-based features and outperform vegetation indices (VIs) by 9% across various growth stages. The CAE model also excels in separating environmental factors, achieving a high silhouette score of 0.919, indicating effective clustering of environmental features. Moreover, the CAE consistently outperforms standard models in unseen environments and unseen genotypes yield predictions, demonstrating strong generalizability. This study demonstrates the value of disentangling G and E effects for providing more accurate and early yield predictions that support informed decision-making in precision agriculture and plant breeding.
Why it matches plant phenotyping methods作物プロットの収量という植物形質を、衛星画像から推定する深層学習特徴抽出法を開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractHere, we significantly enhance pre-harvest yield prediction at plot-scale using Compositional Autoencoders (CAE) - a deep-learning-based feature extraction approach designed to disentangle genotype (G) and environment (E) features - on high-resolution, plot-level satellite imagery.
Reproduction assets foundThe paper's data availability statement provides public access to both the authors' analysis code (Bitbucket repository) and the paper-specific satellite plot-level images with ground-truth yield data (Dryad DOI deposit), directly reproducing this study's phenotyping measurements and analysis.Code · publicAll code is available at bitbucket at https://bitbucket.org/ JS has equity interests in Data2Bio, LLC, and Dryland GeneticsOpen asset ↗pdf-page:14 lines:1-66Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation and detect signatures of local adaptation across populations. We combined hyperspectral data, inverse modeling, and network analysis to investigate population-level variation in Streptanthus tortuosus. Using a common garden experiment with four geographically distinct populations, we applied partial least square discriminant analysis (PLS-DA) and ridge regression for population discrimination, inverse PROSPECT modeling to estimate leaf biochemical traits, and canonical correlation analysis to examine trait-climate relationships across historical (1900-1994) and recent (1995-2024) periods. We developed a spectral network approach treating wavelength correlations as biologically meaningful trait networks. Populations showed distinct, heritable spectral signatures with high classification accuracy. Significant population differences emerged in anthocyanins, carotenoids, chlorophyll, and water content. Trait-climate correlations shifted between time periods, consistent with historical climate adaptation. Network analysis revealed population-specific integration patterns, with more variable environments displaying greater spectral modularity. Hyperspectral signatures provide a high-throughput tool for detecting population-level adaptation and trait coordination. Our findings provide a framework to investigate how plant populations respond to climate change through evolved shifts in trait networks rather than isolated traits alone.
Why it matches plant phenotyping methodsハイパースペクトル計測と逆モデリングを用いて葉の機能形質を推定し、集団間比較・適応評価を行う手法が研究の中心であるため。
abstractHyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw hyperspectral data and source code in a public GitHub repository, which is an allowed URL.Code · publicRR, JL; Formal Analysis:
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RR, JNM, TSM; Funding acquisition: JRG, JNM, TSM; Investigation: RR, JNM, TSM;
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Visualization: RR; Writing – original draft: RR; Writing – review & editing: RR, BQ-C, JL, SA,
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JRG, JNM, TSM
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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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ScieOpen asset ↗rishavray/spectral-networkpdf-raw-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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
Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone varieties is an essential alternative to traditional defoliant spraying. Nevertheless, it is hampered by low throughput and artificial error in manual field surveys. In this study, a framework for collecting high-throughput defoliation data in large fields was established. Three spectral indices (MTCI, VDVI, CI) and leaf area index (LAI) were first screened as core predictors through hierarchical segmentation analysis in three levels: leaf number (LN), leaf number difference (LND), and defoliation rate (DR). Four deep learning architectures (CNN, BiGRU, CNN-BiGRU, and CNN-BiGRU-Attention) were developed, and the CNN-BiGRU-Attention hybrid model demonstrated superior performance at all three levels, with R 2 values exceeding 0.85. Importantly, the inversion accuracy of this model at the LN and LND levels was superior to that at the DR level, which was also confirmed by the results of the genome-wide association study (GWAS). We combined GWAS and transcriptome results to identify a new gene, GhDR_UAV1 , associated with defoliation traits. The overexpression of GhDR_UAV1 significantly promoted the wilting of cotton leaves, indicating that GhDR_UAV1 plays a positive regulatory role in cotton defoliation. This study proposed a strategy to invert cotton defoliation data at three levels using deep learning fusion of UAV remote sensing data and LAI data and confirmed that LND can provide accurate phenotypic data for GWAS analysis. This study provides a new theoretical basis for cotton defoliation regulation and genetic improvement by integrating cotton high-throughput defoliation phenomics and genomics from an innovative perspective.
Why it matches plant phenotyping methodsUAVリモートセンシング、LAI、深層学習を統合し、ワタの落葉形質を高スループット推定する方法を開発・評価しており、表現型取得が研究の中心である。
abstracta framework for collecting high-throughput defoliation data in large fields was established
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's data and code (UAV/LAI defoliation phenotyping data and analysis code) in a public GitHub repository, which matches an allowed URL.Code · publicThe data and code utilized in this study are available at GitHub ( https://github.com/xbw322/Data_upload.git ).Open asset ↗https://github.com/xbw322/Data_upload.gitlines:169-205Code / 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 confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Field / plotMultispectral / hyperspectralLeafSegmentationLeaf traits
Abstract Accurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture, as it directly impacts yield estimation, disease monitoring, and weed management. Conventional Convolutional Neural Networks (CNNs), such as UNet and its variants, often struggle with capturing long range contextual dependencies and preserving fine structural boundaries, while pure transformer based architectures like the Vision Transformer (ViT) suffer from poor inductive bias and limited data efficiency. To overcome these challenges , we propose a SegFormer inspired model that integrates Edge Gated Multi Head Spectral Attention (EG MHSA) for robust leaf area segmentation. The spectral attention mechanism captures discriminative frequency domain representations across spectral bands, while the edge gating module enhances boundary preservation by adaptively fusing multiscale edge features. Evaluated on the benchmark CWFID dataset, the proposed model achieves superior performance with an F1score of 97.33%, IoU of 95.84%, and the lowest loss of 0.0395, outperforming UNet variants and transformer based baselines. Qualitative analysis further demonstrates its effectiveness in accurately delineating fine leaf boundaries under complex field conditions. The ablation results highlight the complementary contributions of spectral attention and edge gating in boosting segmentation performance. With its lightweight architecture, edge focused refinement, and strong generalization capability, the proposed approach sets a new benchmark for leaf area segmentation and provides a practical, scalable solution for agricultural applications.
Why it matches plant phenotyping methods葉面積の画像セグメンテーション手法を開発・ベンチマーク評価しており、植物フェノタイピングにおける形態形質抽出が中心である。
abstractAccurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture
Reproduction assets foundThe paper evaluates its leaf area segmentation model on the public CWFID dataset (60 field images with pixel-level annotations), and the authors explicitly state the datasets are publicly available at the cwfid GitHub repository. No author analysis code or trained model checkpoints are reported.Dataset · publicThe datasets used in the study are publicly available in the repository: https://github.com/cwfid/Open asset ↗https://github.com/cwfid/pdf-page:22 lines:1-27Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Multispectral leaf canopy reflectance as measured by unmanned aerial vehicles is the result of genetic and environmental interactions driving plant physiochemical processes. These measures can then be used to construct relationship matrices for modeling genetic main effects. This type of phenotypic prediction is particularly relevant for trials with many entries, such as those used in early generation potato (Solanum tuberosum) breeding. We compared three methods for making predictions in our potato breeding program: first, using multispectral-derived relationship matrices; second, using the traditional approach based on genomic derived relationships; and third, using a combination of both. Multispectral bands were collected at five different time points for two market classes of potato: chipping and fresh market. We modeled genetic main effects for yield and quality traits at each time point and all stages combined. Models with multispectral relationship matrices exhibited better prediction accuracy for yield and roundness than genomic only models and models featuring spectra plus genomic kernels outperformed both single-kernel predictions in terms of accuracy for most traits. Time points were variably informative depending on the trait measured, however, for all traits combining across time points performed as well or better than single time point models. Similarly, using feature selection to limit our models to important variables did not improve prediction accuracy significantly. This work highlights two potential uses for spectral data in genomic prediction: first, as an alternative to genetic data and second, in combination with genetic data to increase precision of selection.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングを用いたキャノピー反射データをゲノム予測に組み込み、複数手法と予測精度を比較しており、植物表現型取得・推定ワークフローが研究の中心です。
abstractMultispectral leaf canopy reflectance as measured by unmanned aerial vehicles is the result of genetic and environmental interactions driving plant physiochemical processes.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing all data and scripts used for the multispectral genomic prediction analysis.Code · publicAll data and scripts used for this work are available at: https://github.com/shannonlabumn/GS_multispectra_analysis.git .Open asset ↗shannonlabumn/GS_multispectra_analysislines:375-518Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Observing biodiversity across space and time is essential for advancing and verifying conservation efforts toward global biodiversity and sustainability goals. Spaceborne imaging spectroscopy has emerged as a revolutionary tool for quantifying and tracking forest diversity, yet its application at large spatial scales remains a central challenge. We develop a framework to map multiple dimensions of forest community composition and diversity by integrating imaging spectroscopy from two spaceborne sensors (DESIS and EMIT) with taxonomic, phylogenetic, and functional trait datasets, and 43,155 forest inventory plots across the Eastern United States. We find that spectral dissimilarity among forest communities is positively correlated with β-diversity matrices of compositional dissimilarity. We then show that imaging spectroscopy can be used to predict ordination axes of β-diversity and to map multiple dimensions of forest diversity at high spatial resolution (30 or 60 m). Predicted β-diversity axes can be used to model forest attributes, including forest types, plant lineages, and community plant traits. On average, β-diversity axes explain more than 48% of the variance—outperforming climatic and topographic predictors—and enable accurate mapping of 95 forest attributes. Our framework shows that spaceborne imaging spectroscopy, when combined with inventory data, allows indirect yet comprehensive observation of forest diversity attributes across broad spatial extents. This integrative approach sets the stage for scalable forest monitoring in support of global biodiversity conservation and forthcoming satellite missions.
Why it matches plant phenotyping methods宇宙空間イメージング分光と在庫データを統合し、森林群集の多様性や植物形質を推定・マッピングする枠組みが研究の中心であり、植物状態の大規模な表現型推定に該当する。
abstractWe develop a framework to map multiple dimensions of forest community composition and diversity by integrating imaging spectroscopy from two spaceborne sensors (DESIS and EMIT) with taxonomic, phylogenetic, and functional trait datasets, and 43,155 forest inventory plots across the Eastern United States.
Reproduction assets foundThe paper's plant-phenotyping/community-composition analysis relies on FIA forest inventory data (public via FIA DataMart), author analysis code publicly hosted on GitHub, and paper-specific spaceborne data products (Level 3/4 maps of β-diversity and forest attributes) released via Harvard Dataverse. The SDS link only Dataset · publicen
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concentration. The application of our mapping efforts to all the scenes used from DESIS and EMIT are
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Forest inventory data were obtained from the USDA Forest Service’s FIA Program and are available
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through the FIA DataMart (https://apps.fs.usda.gov/fia/datamart/datamart.html). However, as noted in the
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analyses (for more information on federally protected FIA data, see
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https://research.fs.usda.gov/programs/fia/sds). All code associated with this research is available on
4Open asset ↗FIA DataMartpdf-raw-page:14 lines:1-100Code · public). However, as noted in the
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analyses (for more information on federally protected FIA data, see
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https://research.fs.usda.gov/programs/fia/sds). All code associated with this research is available on
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GitHub (https://github.com/Antguz/mapping-communities), and will be archived in Zenodo under version
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1.0 upon publication. Data that do not compromise federally protected information are being prepared for
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(https://dataversOpen asset ↗GitHub · Antguz/mapping-communitiespdf-raw-page:14 lines:1-100Dataset · publicz/mapping-communities), and will be archived in Zenodo under version
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1.0 upon publication. Data that do not compromise federally protected information are being prepared for
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release in the Harvard Dataverse. The spaceborne data products developed in this research are also
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(https://dataverse.harvard.edu/previewurl.xhtml?token=cfb44b92-ec7f-4cd8-9c7c-c2b4b43612d6).498
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501Open asset ↗Harvard Dataversepdf-raw-page:14 lines:1-100Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The Transition to a Data-Driven Agriculture (TTADDA) project focuses on advancing the shift toward high-tech, circular agriculture. By developing cutting-edge sensor technologies and AI-driven tools, the project aims to boost productivity through a data-centric potato production system that supports circular agricultural practices. Potato phenotyping is crucial for creating high-yielding, resilient, and sustainable potato crops, which are essential in global food systems. Specifically in the Netherlands, the global leader in seed potato production and Japan that produces certified seed potatoes under strict quality controls and phytosanitary regulations. A multi-season drone dataset from five potato trials-three in Japan and two in the Netherlands was collected. Each trial field was divided into small plots, each planted with a specific cultivar to assess varietal performance. Data included drone imagery (RGB and multispectral), manual yield and ground coverage measurements, and weather data. The combination of sensor versatility, diverse potato varieties, and varying climate and soil conditions between Japan and the Netherlands makes this dataset highly valuable and potentially reusable for a wide range of applications. Using MIAPPE for this dataset ensures consistent, clear documentation of sensors, varieties, and conditions, making the data findable, reusable, and easy to integrate with other studies. It also supports reproducibility and automated analysis across the multi-location trials.
Why it matches plant phenotyping methodsジャガイモの表現型解析を目的としたUAV RGB・マルチスペクトル画像と圃場測定を含む、多季節・多地点の再利用可能なデータセットの構築・標準化が中心である。
abstractA multi-season drone dataset from five potato trials-three in Japan and two in the Netherlands was collected.
Reproduction assets foundThe paper is a data descriptor for the TTADDA-UAV potato phenotyping dataset (RGB/multispectral orthomosaics, DSMs, yield, ground coverage, weather) publicly deposited on 4TU.ResearchData with a DOI, plus an authors' GitHub repository for loading the MIAPPE-formatted data.Dataset · publicThe dataset is part of the following collection:
Data identification number: doi.org.10.4121/936b5772–09fc–4856–983d-1f9cc2f38d15
Direct URL to data: ( https://doi.org/10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15 )
The collection consist of metadata, and five related studies: TTADDA_NARO_2021, TTADDA_NARO_2022, TTADDA_NARO_2023, TTADDA_WUR_2022, TTADDA_WUR_2023
To visualise the metadata and download the dataset we recommend the following GIT:
https://github.com/NPEC-NL/MIAPPE_TTADDA_dataset
Related research article
None
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Value of the DOpen asset ↗10.4121/936b5772-09fc-4856-983d-1f9cc2f38d15lines:57-83Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The biodiversity function of the desert steppe ecosystem faces many challenges under the pressure of climate change and human activities. Accurate and efficient assessment of plant diversity is critical for guiding desert steppe restoration efforts. However, desert steppe vegetation has sparse leaves and sparse distribution. It is difficult to accurately distinguish micro-vegetation types based on a single spectrum, vegetation index or texture feature, and the resolution of satellite remote sensing cannot meet the needs of high-precision diversity assessment. To this end, this study proposed a novel method for assessing plant diversity index in degraded desert grassland based on multimodal UAV hyperspectral data and Encoder-CNN. Through experiments on different modal feature combinations, spatial spectra, vegetation indices and texture features were targeted and fused. Channel Attention Fusion (CAF) was introduced into Encoder to achieve cross-layer "soft" residual fusion, the Encoder and CNN models were fused to construct a global-local co-expression structure, and finally the quantitative calculation of the plant diversity index at the pixel level was realized. The results show that the vegetation types determined by the fusion of multimodal data and deep learning are consistent with the existing species, dominant species and sub-dominant species of the actual community, and the calculated diversity index results are also consistent with the actual situation. The use of multimodal data combining spatial spectral features with index features, combined with the Encode-CNN model, can provide the most accurate information on community composition. The overall accuracy of sparse vegetation classification can reach 90.01%, and the average accuracy can reach 85.23%, which is better than single mode or traditional 3DCNN, VIT models. This study demonstrates the application potential of UAV hyperspectral multimodal technology and deep learning in the assessment of desert steppe plant diversity, providing important technical support for ecological protection and conservation.
Why it matches plant phenotyping methodsUAVハイパースペクトルとEncoder-CNNを用いて、植物多様性指数を画素レベルで定量推定する手法を開発・評価しており、植物状態の取得・抽出が研究の中心である。
abstractthis study proposed a novel method for assessing plant diversity index in degraded desert grassland based on multimodal UAV hyperspectral data and Encoder-CNN.
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 codes used in this study are available at https://github.com/15204718180/encoder-cnn.Open asset ↗15204718180/encoder-cnnpdf-page:17 lines:56-74Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract The development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits. We used imagery from unoccupied aerial vehicles (UAVs) with red/green/blue (RGB) and multispectral cameras flown over multiple site‐years in Saskatchewan, Canada, and Metaponto, Italy, to gather data for crop height, area, and volume in a lentil diversity panel (324 genotypes). The temporal nature of the UAV image‐derived data enabled the modeling of growth curves for volume, height, and area, something that would be impractical under traditional phenotyping procedures in such a large population grown in multiple environments. A principal component analysis and hierarchical clustering revealed differential growth patterns across contrasting environments, with large variations in temperature and photoperiod, within our lentil diversity panel. Combining this analysis with genome‐wide genotyping data, we identified markers, from an exome capture array (267,845 single nucleotide polymorphisms), associated with crop growth that could be used for marker‐assisted selection. Our study demonstrates the potential for UAV‐based imaging to obtain large‐scale time‐series data across multiple environments to model growth curves and investigate genotype‐by‐environment interactions. In addition, we can now use phenotypic traits that were once impractical to collect and derive novel phenotypes to improve our understanding of crop growth and the genetics underlying adaptation in lentil, approaches that will be useful for both researchers and breeders.
Why it matches plant phenotyping methodsUAV画像からレンティルの高さ・面積・体積を時系列推定し、大規模集団で成長曲線をモデル化するフェノタイピング手法の実質的な適用・評価が中心である。
abstractThe development of high‐throughput phenotyping platforms to capture time‐series data on large, diverse populations holds promise for crop researchers and breeders investigating growth‐related traits.
Reproduction assets foundThe paper's UAV-derived lentil growth phenotypes are publicly available on KnowPulse, and the authors' full analysis code/workflow is public on GitHub with a rendered vignette. Both are explicitly stated in the data availability statement and methods.Dataset · publiciluppo e di Innovazione
in Agricoltura) in Metaponto, Italy. Special thanks to Laura
Jardine for help with editing.
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 data that support the findings of this study are
available online at https://knowpulse.usask.ca/research-experiment/AGILE-UAV and https://github.com/derekmichaelwright/AGILE_LDP_UAV or from the authors
upon request.
O RC I D
DerekM. Wright https://orcid.org/0000-0002-9639-7596
SandeshNeupane https://orcid.org/0000-0003-3679-1046
Tania Gioia https://orcid.org/0000-0001-8980-3034
Giuseppina Logozzo https://orcid.org/0000-0002-7951-2425
SOpen asset ↗knowpulse.usask.ca · AGILE-UAVpdf-raw-page:11 lines:1-84Code · publical user-
calculated traits as described in Figure 2. G × E analysis was
done with “lme4” using linear mixed models (Bates et al.,
2015). Principal component analysis (PCA) and hierarchical
k-means clustering were performed using the “FactoMineR”
R package (Lê et al., 2008). The source code for all data
analyses is available at: https://derekmichaelwright.github.io/AGILE_LDP_UAV/LDP_UAV_Vignette.html.25782703,
2025,
1,
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from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.70040,
Wiley
Online
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[20/08/2025].
See
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and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
WileyOpen asset ↗derekmichaelwright.github.io/AGILE_LDP_UAV · LDP_UAV_Vignettepdf-raw-page:3 lines:1-106Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Boreal peatlands, which act as significant sinks and storage of global soil organic carbon, are increasingly threatened by the changing climate conditions as well as land use changes. Despite the importance of these ecosystems, their vegetation and ecological features remain poorly mapped compared to other terrestrial ecosystems. Hyperspectral satellite imaging shows promise for detailed vegetation mapping and biodiversity monitoring of boreal peatlands. However, its effective application requires a fundamental understanding of the spectral properties of the vegetation communities of boreal peatlands. To address this, we combined newly available, open-source data consisting of close-range sensed spectral libraries of boreal peatland vegetation communities and single species. Our aim was to examine the extent to which close-range spectral data can be used to predict species-specific fractional cover in minerotrophic and ombrotrophic peatland habitats using hyperspectral and multispectral data, and to assess the connection between spectral signatures and α-diversity of the vegetation communities. Our findings show that hyperspectral data can be used to predict the fractional cover of certain plant species with moderate accuracy ( R 2 = 0.58). When comparing data types, hyperspectral data typically produced slightly better model fits for species with larger sample sizes, appearing to be superior to multispectral data. However, in certain cases, such as in the prediction of litter cover in ombrotrophic peatland habitats, multispectral data yielded marginally better results ( R 2 = 0.4-0.45). Furthermore, using hyperspectral data, we observed that the prediction of α-diversity of the ombrotrophic habitats was moderately better ( R 2 = 0.44) than that of the minerotrophic habitats ( R 2 = 0.22). These results enhance our understanding of the spectral properties of the complex, multilayered vegetation communities and thus aid in the mapping of these vital ecosystems.
Why it matches plant phenotyping methodsハイパースペクトルおよびマルチスペクトルデータから植物種別被覆率と植生α多様性を推定する手法を中心に評価しており、植物群落形質の技術的推定が主題である。
abstractOur aim was to examine the extent to which close-range spectral data can be used to predict species-specific fractional cover in minerotrophic and ombrotrophic peatland habitats using hyperspectral and multispectral data, and to assess the connection between spectral signatures and α-diversity of the vegetation communities.
Reproduction assets foundThe paper's own spectral libraries (vegetation plot spectra, Sphagnum moss spectra, vascular plant/litter spectra) are openly deposited on Mendeley Data with DOIs stated in Table 1 and the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicData are available at https://doi.org/10.17632/3866tj3w8v.1 (Salko, Hovi, Burdun, et al. 2024a , spectral library of the vegetation plots)Open asset ↗10.17632/3866tj3w8v.1lines:761-789Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
In recent years, accurate and low-cost variant calling has enabled the genotyping of large diversity panels for genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. This has created a strong need for high-throughput, accurate, and low-cost in-field phenotyping. Here, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera. Our high-throughput phenotyping approach integrates an RGB- and MSP camera to measure the color and height of lettuce in this large-scale field experiment. We used the mean and other summary statistics, such as median, quantiles, skewness, kurtosis, minimum, and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using these summary statistics as traits for GWAS, we confirm several previously described genetic associations, now under field conditions, and identify additional novel associations for color and height traits in lettuce.
Why it matches plant phenotyping methodsドローン搭載RGB・マルチスペクトルカメラを用いて、レタスの色と高さを大規模・非破壊・定量測定する高スループット表現型解析手法が研究の中心であり、GWASへの応用も行っている。
abstractHere, we present a genome-wide association study (GWAS) study on 194 field-grown accessions of lettuce (Lactuca sativa). These accessions were non-destructively phenotyped at two time points 15 days apart using a drone equipped with an RGB and multispectral (MSP) camera.
Reproduction assets foundThe paper's authors publicly deposited their image processing, GWAS, and figure scripts on GitHub (SnoekLab/Dijkhuizen_etal_2025_Drone) and all raw/intermediate phenotyping data (including weather data) at a UU Yoda DOI (10.24416/UU01-S5FCM9). Both are paper-specific, public, and actionable.Code · publicThe scripts for making the SNP map from the filtered VCF file and for the image processing, GWAS, and figures in this manuscript are available on https://github.com/SnoekLab/Dijkhuizen_etal_2025_Drone .Open asset ↗SnoekLab/Dijkhuizen_etal_2025_Dronelines:362-531Dataset · publicData available at https://doi.org/10.24416/UU01‐S5FCM9 . This includes all raw data, all intermittent steps, the data required to generate all figures, and data on the weather during the experiment.Open asset ↗10.24416/UU01‐S5FCM9lines:362-531Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
AppleLaboratory / benchtopMultispectral / hyperspectralFruitClassificationGrowth / development / phenology
The study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system. The system was optimized to allow spectral information to be obtained in 8 discrete wavebands, which enabled non-destructive determination of such key fruit components as ripeness, sugar type and cultivar. Stringent environmental conditions were maintained during image acquisition for optimal measurement consistency and experimental repeatability. The detailed dataset encompasses 32,463 multi-spectral images across three distinct classification categories. For sweetness evaluation, 1620 images spanning Brix values from 10 % to 15 % were collected from five apple varieties. Ripeness evaluation includes 29,160 images documenting the complete maturation cycle over 18 days, while variety classification contains 1683 images from three distinct cultivars. Each image was captured under controlled lighting conditions using eight specific wavelengths, ensuring spectral consistency crucial for machine learning applications. These multi-spectral images were concatenated for grading by sweetness, ripeness, and variety, creating a processed dataset of concatenated images optimized for AppleNet processing. The concatenation process combines the eight wavelength channels into unified image representations suitable for deep learning applications. Sample collection included the picking of different apple cultivars at different physiological development phases of fruit from local orchards. Single specimens were imaged sequentially using a multi-spectral technique. Information on sugar content concentration ( % Brix), maturation phase classification and varietal identification was recorded according to standard laboratory procedure. The resulting annotated database includes such quantitative reference points, which can be used to train supervised learning classifiers in computational classification systems. The reuse value of the dataset covers a wide range of applications such as machine learning-based fruit quality evaluation, agricultural automation and food industry examination. This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. Furthermore, the proposed multi-spectral imaging can be generalized to cover other fruits and agricultural products, extending the application of the method in smart agriculture. This dataset serves as a valuable resource for researchers in computer vision, machine learning, and agricultural technology, fostering advancements in non-destructive fruit quality evaluation methodologies.
Why it matches plant phenotyping methodsリンゴの甘度・成熟度・品種を推定するマルチスペクトル撮像システムと、注釈付き大規模画像データセットを中心に構築しており、植物器官の品質・状態を定量化する再利用可能なフェノタイピング手法に該当する。
abstractThe study created a detailed database for apple quality inspection using a cost-effective, self-designed multi-spectral imaging system.
Reproduction assets foundThe article is a Data in Brief describing a public multi-spectral apple image dataset (sweetness/Brix, ripeness over 18 days, variety) deposited on Mendeley Data with DOI 10.17632/y5h6v8w6ms.2 and a direct URL, explicitly stated as publicly accessible. This is the paper's own phenotyping image dataset. The MATLAB code,Dataset · publicme environment using a custom-built multi-spectral imaging chamber . The imaging conditions were carefully maintained to ensure consistency. The dataset is securely stored for research and study purposes.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/y5h6v8w6ms.2
Direct URL to data: https://data.mendeley.com/datasets/y5h6v8w6ms/2
Instructions for accessing these data:
Dataset Title: Dataset of Apples for Grading by Sweetness, Ripeness, and Variety
Public Access: The dataset titled ``Dataset of Apples for Grading by Sweetness, Ripeness, and Variety'' is publicly available on Mendeley Data and can be accessed via the following DOI:
https://doi.org/Open asset ↗Mendeley Data · 10.17632/y5h6v8w6ms.2lines:40-82Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract The application of small unmanned aircraft systems (sUAS)‐based high‐throughput phenotyping in plant breeding has advanced significantly over the past decade. Hyperspectral images and machine learning approaches offer potential to enhance drought resistance screening in turfgrass. However, large‐scale field applications remain limited, and the transition from controlled environments to real‐world phenotyping is not well understood. This study aimed to develop an sUAS‐based hyperspectral image workflow to monitor changes in turfgrass canopy reflectance during drought, validate previously reported indices from controlled environment studies in a large‐scale field study, and estimate visual turfgrass quality (TQ) from hyperspectral images. Images were collected from a zoysiagrass ( Zoysia spp.) mapping population at three dates under varying soil moisture conditions. Vegetation indices (VIs) related to light use efficiency, leaf pigments, senescence, water status, and green vegetation were computed and compared. Top‐performing genotypes under drought exhibited greater absorption in blue and red wavelengths and higher near‐infrared reflectance than poor‐performing ones. The photochemical reflectance index and plant senescence reflectance index were highly correlated with TQ ( r = 0.84 and −0.76), showed higher coefficient of variation (range 18%–37%), and had higher broad‐sense heritability (0.73–0.74) than normalized difference vegetation index (0.69), warranting their use in large‐scale field study. Machine learning models estimated TQ with a mean absolute error of 0.46. These findings highlight the importance of integrating VIs related to light use efficiency, leaf pigments, senescence, and water status to gain deeper insights into turfgrass drought response and support breeding for stress tolerance.
Why it matches plant phenotyping methodssUASハイパースペクトル画像によるキャノピー形質取得ワークフローを開発し、指標を検証して芝草品質を推定しており、フェノタイピング手法が中心である。
abstractThis study aimed to develop an sUAS‐based hyperspectral image workflow to monitor changes in turfgrass canopy reflectance during drought, validate previously reported indices from controlled environment studies in a large‐scale field study, and estimate visual turfgrass quality (TQ) from hyperspectral images.
Reproduction assets foundThe paper's data availability statement points to a Zenodo-hosted dataset of spectral reflectance measurements from the zoysiagrass mapping population under drought, which directly reproduces this paper's phenotyping measurements. No author analysis code or trained models were identified.Dataset · publicDATA AVA I L A B I L I T Y S TAT E M E N T
The data referenced in this paper are available in a repository
hosted by Zenodo (Zhang, 2025).Open asset ↗Zenodopdf-raw-page:17 lines:1-85Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R2
Why it matches plant phenotyping methodsUAV LiDARとフィールド分光法を用いて、植生残渣量（RDM）という植物状態を推定するセンサー手法の評価が研究の中心であり、単なる農業実験での routine measurement ではない。
titleEvaluating UAV LiDAR and Field Spectroscopy for Estimating Residual Dry Matter Across Conservation Grazing Lands
Reproduction assets foundThe paper's UAV LiDAR data (used to derive canopy height models for RDM estimation) is explicitly stated to be publicly available in the OpenTopography Community Dataspace. The KNB deposit containing RDM weights, field spectra, and analysis data is also mentioned, but its DOI URL is not among the allowed URLs, so only Dataset · publicAll the LiDAR data used in this study are publicly available in the
Open Topography Community Dataspace: https://doi.org/10.5069/G9S180QVOpen asset ↗10.5069/G9S180QVpdf-page:17 lines:1-33Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design.
Why it matches plant phenotyping methodsトウモロコシのハイパースペクトル反射データによる形質推定について、複数の機械学習モデル、未知遺伝子型・季節への汎化性能、データ統合の影響を系統的かつネスト化交差検証で評価しており、フェノタイピング手法の検証が中心である。
abstractWe use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance.
Reproduction assets foundThe paper's data availability statement explicitly provides all code and raw data (hyperspectral reflectance and trait measurements) for reproducibility via the authors' public GitHub repository.Code · publicidge, Cambridge, UK
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†
These authors contributed equally.
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*
Corresponding authors.
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Email address:
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rudan.xu@uni-potsdam.de
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jfergu@essex.ac.uk
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jk417@cam.ac.uk
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nikoloski@mpimp-golm.mpg.de
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Data availability statement
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All code and raw data to ensure reproducibility of the results can be accessed at:
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https://github.com/Rudan-X/HyperspectralML
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Funding statement:
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J.F. was supported by the European Union’s Horizon 2020 research and innovation program
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grant 862201 (to J.K. and Z.N.). R.X. was supported by the International Max Planck Research
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School "Molecular Plant Science" between the Max Planck Institute of Molecular Plant
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Physiology and the UniveOpen asset ↗Rudan-X/HyperspectralMLpdf-raw-page:1 lines:1-71Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
The modelling and prediction of important agronomic traits using remotely sensed data is an evolving science and an attractive concept for plant breeders, as manual crop phenotyping is both expensive and time consuming. Major limiting factors in creating robust prediction models include the appropriate integration of data across different years and sites, and the availability of sufficient genetic and phenotypic diversity. Variable weather patterns, especially at higher latitudes, add to the complexity of this integration. This study introduces a novel approach by using photothermal time units to align spectral data from unmanned aerial system images of spring, winter, and facultative oat (Avena sativa) trials conducted over different years at a trial site at Aberystwyth, on the western Atlantic seaboard of the UK. The resulting regression and classification models for various agronomic traits are of significant interest to oat breeding programmes. The potential applications of these findings include optimising breeding strategies, improving crop yield predictions, and enhancing the efficiency of resource allocation in breeding programmes.
Why it matches plant phenotyping methodsUASマルチスペクトル画像とフォトサーマル時間単位を統合し、オート育種試験の農業形質を予測する手法が研究の中心である。
abstractThis study introduces a novel approach by using photothermal time units to align spectral data from unmanned aerial system images
Reproduction assets foundThe paper's Data Availability Statement points to a public deposit of the study's UAS spectral and ground-truth oat trial data at the Aberystwyth Data Repository (DOI 10.20391/ec0863ab-3b5c-434b-837e-74bae4400387). No author analysis code or trained models are explicitly deposited; the supplementary materials contain只有Dataset · publicData Availability Statement: Data are available from the Aberystwyth Data Repository:
https://doi.org/10.20391/ec0863ab-3b5c-434b-837e-74bae4400387.Open asset ↗Aberystwyth Data Repository · 10.20391/ec0863ab-3b5c-434b-837e-74bae4400387pdf-page:19 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring plant physiology. However, challenges remain in identifying direct physiological indicators of plant stresses, hindering the development of accurate diagnostic models for predicting symptom progression. Here, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes. MapS-Wear continuously tracks leaf surrounding temperature, humidity, and unique in-situ transmission spectra, which are critical stress-related indicators. The machine learning framework processes these multimodal data to predict gradual stress progression and diagnose nutrient deficiencies in plants over 10 days earlier than conventional computer vision methods. Moreover, MapS-Wears enables portable and large-scale screening of grafted tomato varieties in greenhouses, accelerating the identification of compatible grafting combinations. This demonstration highlights the potential for high-throughput plant phenotyping and yield improvement.
Why it matches plant phenotyping methods植物ストレスの生理状態を連続センシングし、機械学習で早期診断・進行予測するウェアラブル計測システムが研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractHere, we introduce a machine-learning-powered spectral-dominant multimodal soft wearable system (MapS-Wear) for precise, long-term, and early-stage diagnosis of stresses in tomatoes.
Reproduction assets foundThe paper's Data and materials availability statement explicitly deposits the tomato leaf photos, transmission spectral data, and ML algorithms on Zenodo, matching an allowed URL.Dataset · publicThe photos of tomato leaves in different health statuses, the transmission spectral data of these leaves, and the ML algorithms are openly available on Zenodo ( https://zenodo.org/doi/10.5281/zenodo.15192884 ).Open asset ↗Zenodo · 10.5281/zenodo.15192884lines:129-274Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
Why it matches plant phenotyping methodsUAV画像から個体検出・地理空間的形質抽出を行うオープンソース基盤の開発と性能検証が中心であり、植物形質（草丈・NDVI）を抽出する再利用可能なワークフローを提供している。
abstractThis study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction.
Reproduction assets foundThe paper's MatchPlant pipeline code is publicly available on GitHub, and the maize case study training dataset and pre-trained model are publicly available on Zenodo.Dataset · publicinistration, Funding acquisition.
Declaration of Competing Interest
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.
Data availability
The public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025). The source code and documentation for MatchPlant are available on GitHub at https://github.com/JacobWashburn-USDA/MatchPlant (accessed on February 14, 2025).
Acknowledgments
This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation PrOpen asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVM VI ) for AGB prediction. Nevertheless, there is limited investigation into how these methods perform across different agricultural conditions. This study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments. We utilized LiDAR, multispectral (MS), thermal infrared (TIR), along with measured AGB and Leaf Area Index (LAI) data from various growth stages to develop a stacking ensemble learning model. This model effectively integrates data from multiple sources, resulting in a strong prediction performance with R 2 of 0.86, Mean Absolute Error (MAE) of 1.54 t/ha, and Root Mean Square Error (RMSE) of 2.06 t/ha. Meanwhile, the analysis of the accuracy of CVM VI revealed its efficacy during the early-stage when corn is short, with its predictive capability diminishing as AGB increases. Consequently, we recommend the CVM VI for early-stage AGB prediction, which can streamline data collection and computational efforts. In contrast, the ML approach, which benefits from data fusion, is more appropriate for predicting AGB during the mid to late growth stages. This study enhances AGB prediction accuracy and speed, providing critical understanding of regional AGB dynamics and supporting better agricultural decision-making.
Why it matches plant phenotyping methodsUAVのLiDAR・マルチスペクトル・熱赤外データを統合し、トウモロコシの地上部バイオマスを推定するモデルを開発・比較・評価しており、植物形質取得手法が中心である。
abstractThis study aims to fill this gap by creating specific methodologies for estimating corn AGB under diverse fertilization and irrigation treatments.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits authors' model training code and test data at a public GitHub repository, which qualifies as a paper-specific public code asset for the AGB prediction analysis.Code · publicCode and test data for model training are available at https://github.com/Joker1xuan/model_training .Open asset ↗Joker1xuan/model_traininglines:323-356Code / 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 confirmedEurope PMC · checked 14 Sept 2026
The present dataset is a collection of multispectral images designed for development of detection algorithms for grapevine diseases like Flavescence dorée (FD) and Esca (ED). Although FD severely threatens viticulture, there are few public datasets and none with multispectral data collected in the field. The collected images have been taken from a frontal perspective of vineyard plants that highlights details of leaves and trunks facilitating detailed disease analysis. The data were collected using a Micasense RedEdge-P multispectral camera, capturing six spectral bands across 172 image captures of three different grapevine varieties used in Lambrusco wines: Ancellotta, Marani, and Salamino. The dataset includes raw and processed images, calibration images for the multispectral camera, annotations detailing plant health conditions, and Python-based usage examples for researchers. Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. The dataset is a valuable resource for advancing remote and proximal sensing in precision agriculture.
Why it matches plant phenotyping methodsブドウ樹の病害状態を対象とするマルチスペクトル画像データセットで、画像・校正・アノテーション・利用例を含む再利用可能な資源として構築されており、植物フェノタイピング手法の基盤が中心です。
titleA dataset for vineyard disease detection via multispectral imaging.
Reproduction assets foundThe paper is a data descriptor for a multispectral vineyard disease detection dataset deposited by the authors on Zenodo, including raw/processed images, annotations, and Python usage examples. The two Micasense GitHub repositories are generic vendor libraries, not paper-specific assets.Dataset · publicgio Emilia, Emilia-Romagna, Italy). It is managed by the RIMLab laboratory at the University of Parma, Parco Area delle Scienze 181/A, 43100 Parma, Italy.
Data accessibility
Repository name: A Dataset for Vineyard Disease Detection via Multispectral Imaging
Data identification number: 10.5281/zenodo.14936376
Direct URL to data: https://zenodo.org/records/14936376
Related research article
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Value of the Data
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The dataset features grapevine images which is a high-value plant used for wine production. Italy and other European nations are among the world's largest wine exporters. For this reason, diseases such as Flavescence Dorée (FD) and Esca, that cause severe damage to both the plant aOpen asset ↗Zenodo · 10.5281/zenodo.14936376lines:1-49Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Multispectral optical data significantly enhances cereal crop monitoring by enabling precise tracking of growth stages, early detection of germination issues, and assessment of plant health. This study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring. The aim was to determine the optimal UAV imaging timing that aligns with proximal sensor measurements to improve growth stage assessments. Experiments were conducted on 41 cereal genotypes, including ancient and modern varieties, under two nitrogen top-dress dosages across 130 plots. The top ten performing genotypes were analyzed to identify resilient varieties adaptable to climate change and evolving field conditions. Our results demonstrate that vegetation indices during booting and spike emergence stages consistently predict yield potential, offering a robust framework for early-stage yield estimation. Additionally, we provide a comparative analysis of UAV and handheld sensor data, highlighting their respective strengths and limitations. Three vegetation indices, GRDVI, NDVI and SAVI demonstrated a very strong average positive correlation: 0.957, 0.954 and 0.944 across the selected genotypes from different performance levels. The combined dataset supports improved fertilization strategies, optimized seeding cycles, and identification of genotypes with stable agronomic traits. This study underscores the synergistic potential of aerial and proximal sensing technologies for next-generation cereal crop management and precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と近接センサーを統合し、画像取得時期、センサーデータの比較、植物生育段階・収量予測を評価しており、植物形質取得手法が研究の中心である。
abstractThis study evaluates the potential of integrating UAV multispectral sensor with the handheld Plant-O-Meter device for high-precision crop monitoring.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's dataset (UAV multispectral and Plant-O-Meter phenotyping measurements) on Zenodo with a public DOI, matching an allowed URL. No separate analysis code repository is stated.Dataset · publicWe have made the dataset publicly available, and it can be accessed through the following reference: Grbović Ž, Ivošević B, Buden M, Waqar R, Pajević N, Ljubičić N, et al. (2025) Integrating UAV multispectral imaging and proximal sensing for high-precision cereal crop monitoring [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15133473 .Open asset ↗Zenodo · 10.5281/zenodo.15133473lines:281-306Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Leaf spot is a devastating disease in cultivated peanut ( Arachis hypogaea L.) that can lead to significant yield losses without chemical controls. Multiple disease symptoms, two causal organisms, inconsistent testing environments, and genotype by environment interactions are all components that make breeding for leaf spot‐resistant peanuts challenging. To better understand this disease, and make gains in breeding for disease resistance, an accurate and objective phenotyping strategy must be implemented. In this work, data derived from leaf scans, unoccupied aerial vehicle‐captured red, green, blue and multispectral imagery were evaluated as a replacement for the subjective visual rating scale used at present. Standard operating procedures are detailed for all digital methods evaluated in this paper, and all digital phenotypes are fully characterized with descriptive statistics. Feature importance and post hoc proof of concept studies are conducted to further evaluate the new digital methods. Ultimately, “visible atmospherically resistant index” was selected as the most appropriate proxy for visual ratings and should be deployed by researchers and plant breeders in the peanut community for the objective evaluation of leaf spot resistance.
Why it matches plant phenotyping methods落花生葉斑病の客観的表現型評価を目的に、葉スキャンおよびUAVのRGB・マルチスペクトル画像を用いるデジタル手法を評価・標準化しており、病害表現型の取得法が中心である。
abstractan accurate and objective phenotyping strategy must be implemented
Reproduction assets foundThe paper deposits its phenotyping datasets (visual ratings, leaf scans, UAV RGB/multispectral imagery) in Dryad and hosts analysis scripts and supporting information in a public GitHub repository, both explicitly linked by the authors.Dataset · publicUS
Department of Agriculture is an equal opportunity provider
and employer.
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 datasets generated during and/or analyzed during the cur-
rent study are available in the Dryad repository: https://doi.org/10.5061/dryad.rn8pk0pnm.O RC I D
RyanAndres https://orcid.org/0000-0001-8635-4077
JeffreyDunne https://orcid.org/0000-0003-0544-9889
R E F E R E N C E S
Anco, D. J., Thomas, J. S., Jordan, D. L., Shew, B. B., Monfort, W. S.,
Mehl, H. L., Small, I. M., Wright, D. L., Tillman, B. L., Dufault, N.
S., Hagan, A. K., & Campbell, H. L. (2020). Peanut yield losOpen asset ↗Dryad · 10.5061/dryad.rn8pk0pnm.Opdf-raw-page:15 lines:1-82Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
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The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Code / 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 confirmedEurope PMC · checked 14 Sept 2026
The bacterium Xylella fastidiosa (Xf) is a plant pathogen first identified in Europe in 2013, specifically in olive groves in the Apulia region (south-eastern Italy). It is now spreading across the Mediterranean basin and poses a serious threat to the local economy by causing branch desiccation and the rapid death of olive trees, a condition known as olive quick decline syndrome (OQDS). Several studies have investigated the potential of remote sensing (RS) technology to monitor OQDS over time and space; however, accurate and reliable data on OQDS occurrence remain scarce. To enhance the distribution data of Xf-infected trees in the Apulia region, we investigated an infection hotspot of 25 km² area in the province of Brindisi, where records of infections were documented in 2019 and 2020. Three very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees. Through visual interpretation, 2340 trees were identified most likely as either infected or removed due to OQDS. This dataset provides a valuable resource for developing or validating RS techniques for early detection of OQDS. Furthermore, it could support studies aimed to evaluate spectral bands or indices most correlated with infection presence. Finally, the dataset can be integrated with other Xf-infection presence data to support species distribution model studies.
Why it matches plant phenotyping methods衛星画像のセグメンテーションと感染・枯死オリーブ樹のラベル化による、植物病害状態の検出・検証用データセットが研究の中心である。
abstractThree very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees.
Reproduction assets foundThe paper is a Data in Brief article describing a public Figshare dataset (OQDS-Insight) containing WorldView-2 satellite raster imagery (RGB and NDVI GeoTIFFs) and a shapefile of 76,637 olive tree points with OQDS infection labels — directly the paper's phenotyping measurements.Dataset · publicsouth-eastern Italy. The extent (EPSG:32633) is from 706164.541 N to 713395.999 N, and from 4508710.411 E to 4513574.414 E.
Data are stored at the Council for Agricultural Research and Economics, Research Centre for Agriculture and Environment, Italy.
Data accessibility
Repository name: OQDS-Insight
Data identification number: https://doi.org/10.6084/m9.figshare.28191245.v4
Direct URL to data: https://doi.org/10.6084/m9.figshare.28191245.v4
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1. Value of the Data
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The dataset provides a detailed record of OQDS olive groves within an infection hotspot in the province of Brindisi, Apulia region (south-eastern Italy) ( Fig. 1 ).
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It can support rOpen asset ↗figshare · 10.6084/m9.figshare.28191245.v4lines:95-140Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
Large-scale manual measurements of plant architectural traits in tomato growth are laborious and subjective, hindering deeper understanding of temporal variations in gene expression heterogeneity. This study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system. The SegFormer with fusion of multispectral and depth imaging modalities was employed to semantically segment plant organs from the registered RGB-D and multispectral images. Organ point clouds were then generated and clustered into instances. Finally, six key architectural traits, including fruit spacing (FS), inflorescence height (IH), stem thickness (ST), leaf spacing (LS), total leaf area (TLA), and leaf inclination angle (LIA) were extracted and the temporal broad-sense heritability folds were plotted. The root mean square errors (RMSEs) of the estimated FS, IH, ST, and LS were 0.014, 0.043, 0.003, and 0.015 m, respectively. The visualizations of the estimated TLA and LIA matched the actual growth trends. The broad-sense heritability of the extracted traits exhibited different trends across the growth stages: (i) ST, IH, and FS had a gradually increased broad-sense heritability over time, (ii) LS and LIA had a decreasing trend, and (iii) TLA showed fluctuations (i.e. an M-shaped pattern) of the broad-sense heritability throughout the growth period. The developed system and analytical approach are promising tools for accurate and rapid characterization of spatiotemporal changes of tomato plant architecture in controlled environments, laying the foundation for efficient crop breeding and precision production management in the future.
Why it matches plant phenotyping methods植物形態形質を取得するUGV型マルチモーダル画像フェノタイピングシステムと解析手法の開発・定量評価が研究の中心であり、誤差検証も行っているため。
abstractThis study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' trait-extraction pipeline code and example data on a public GitHub repository, matching the allowed URL.Code · publicThe pipeline code and example data related to this project are available as open source on GitHub ( https://github.com/DigBigPigForU/Tomato-architectural-trait-extraction ).Open asset ↗Tomato-architectural-trait-extractionlines:822-958Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsタイトルから、ハイパースペクトル画像と深層学習による植物形質推定の不確実性評価に関する補足資料であり、形質取得・推定手法の検証が中心と判断できる。
titleUncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data
Reproduction assets foundThe supplement (Table S3) enumerates the public hyperspectral/phenotype datasets used to train and evaluate the paper's deep-learning trait retrieval and uncertainty models. These include EcoSIS canopy spectra-trait datasets, Dryad airborne imaging spectroscopy data, GFZ EnMAP preparatory campaign datasets, an FRDR folDataset · publicGravel, A., Laliberté, E., Kalacska, M. (2024).
Foliar Functional Trait Mapping of a mixed
temperate forest using imaging spectroscopy.
Federated Research Data Repository.
https://doi.org/10.20383/103.0922Open asset ↗Federated Research Data Repository · 10.20383/103.0922pdf-page:9 lines:1-39Code / 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 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 6 Sept 2026
Remote sensing holds promise for ecosystem-level monitoring of plant drought stress but is limited by uncertain linkages between physiological stress and remotely sensed metrics of water content. Here, we investigate the stability of relationships between water potential (Ψ) and water content (measured in situ and via repeat airborne VSWIR imaging) over diel, seasonal, and spatial variation in two xeric oak tree species. We also compare these field-based relationships with ones established in laboratory settings that might be used as calibration. Due to confounding physiological processes related to growth, both in situ and remotely sensed metrics lacked consistent relationships with stress when measured across space or through time. Relationships between water content and physiological drought stress measured over the growing season were stronger and more closely related to established laboratory-based drydown methods than those measured across space (i.e., between wet trees and dry trees). These results provide insight into the utility of "space for time" approaches in remote sensing and demonstrate both important limitations and the potential power of high temporal resolution remote sensing for detecting drought stress.
Why it matches plant phenotyping methods樹木の干ばつストレスを対象に、航空機VSWIR画像による含水量推定と水ポテンシャルとの関係を時空間的に検証しており、リモートセンシング手法の妥当性・限界評価が中心です。
abstractwater content (measured in situ and via repeat airborne VSWIR imaging)
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's data and analysis code on Zenodo (DOI 10.5281/zenodo.15110087), and the AVIRIS-NG reflectance data used for the canopy water content analysis is publicly archived at ORNL DAAC (DOI 10.3334/ORNLDAAC/2376). Both are paper-specific, public, and match Code · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:245-270Dataset · publicThe data and code that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.15110087 .Open asset ↗Zenodo · 10.5281/zenodo.15110087lines:467-475Dataset · publicReflectance data was obtained from ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2376 .Open asset ↗ORNL DAAC · 10.3334/ORNLDAAC/2376lines:245-270Code / dataset availability 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 confirmedOpenAlex · arXiv · checked 14 Sept 2026
This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.
Why it matches plant phenotyping methods植物フェノタイピング施設向けに、固定カメラ画像からNeRFで植物の3D点群を再構成する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities.
Reproduction assets foundThe paper explicitly releases its full SC-NeRF dataset (raw 4K videos, frames, COLMAP poses, NeRF checkpoints, and final 10M-point clouds for six plant/produce objects) on Hugging Face, and states that all datasets and the authors' code are available at the project page. Both are paper-specific, public, and actionable.Code · publicd delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines. We provide all datasets and our code, available at https://baskargroup.github.io/SC-NeRF/
Figure 1 : Schematic of the stationary camera imaging system for NeRF-based point cloud reconstruction in high-throughput plant phenotyping. In this setup, each plant is conveyed to a rotating turntable marked against a matte black background. Over a full 30-second rotation, a tripod-mounted stationary camera captures high-resoOpen asset ↗lines:1-53Code / 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
Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions. The most comprehensive parametric model tested, which included SNPs, HSI, and environmental covariates data, consistently achieved the best results, closely followed by machine learning (ML) approaches when considering the same omics data. For example, the most comprehensive model (M9), under the forward prediction cross-validation scheme, predicted the GY of the 2023 growing season using data from 2021 and 2022 for a correlation between predicted and observed values of 0.53. This model demonstrated superior performance compared to less complex models, emphasizing the advantage of integrating numerous data sources and their interactive effects. Furthermore, when comparing the top 25% of the predicted lines versus the corresponding observed lines with the highest GY, the M9 model returned a coincide index (CI) of 55% (i.e., in both sets, 55% of the top 25% values were common), whereas for the highest performing ML model (gradient boosting regression), the CI was of 46%. This study highlights the potential of multi-data source approaches to accelerate the selection of heat-tolerant wheat genotypes.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像を用いた小麦収量形質の推定を、ゲノム・環境データとの統合モデルで検証しており、形質予測性能の比較が研究の中心である。
abstractIntegrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) 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2件を確認しました。Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDglines:343-470Dataset · publicThe datasets used in this study can be found at http://datadryad.org/stash/share/t8Ev6Aptra1z86ELtPNd2A0Bi1glIrwrTS3zrH4VpDg and http://datadryad.org/stash/share/UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpk .Open asset ↗Dryad · UGz_RyppCD‐KCea6z0pR83oU5V2WgGzC09ADOb3kVpklines:343-470Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this work, a unique database of 6726 multispectral images of coffee leaves is presented. These images were captured in JPG format for the RGB photos and in TIF format for the five multispectral bands: blue, green, red, NIR and red edge, providing a detailed view of different wavelengths of the electromagnetic spectrum. Images in TIF format have a color depth of 16 bits per pixel, ensuring good quality. The blue band (Band 1) captures light in the blue region of the spectrum, approximately 450 to 500 nm. The green band (Band 2) records light in the green region, approximately between 500 and 620 nm. The red band (Band 3) captures light in the red region, between 620 and 750 nm. The red-edge band (Band 4) lies between the red band and the NIR, and is sensitive to the transition between green vegetation and non-vegetation, around 840 nm. Finally, the near infrared band (Band 5) captures light in the near infrared region, between 750 and 900 nm. For ease of identification, images are labeled as follows: if the image name ends in 0, it is an RGB image; if it ends in 1, it corresponds to the blue band; if it ends in 2, to the green band; if it ends in 3, to the red band; if it ends in 4, to the red-edge band; and if it ends in 5, to the near-infrared band. The images show coffee leaves with and without lesions caused by the Hemileia vastatrix fungus, known as coffee rust. These samples were collected from Colombian coffee farms and the images were captured under controlled lighting conditions to ensure quality and consistency. This database is an invaluable resource for precision agriculture research and early detection of crop diseases. With these 6726 images, researchers can use advanced image processing and machine learning techniques to identify differences between healthy leaves and those affected by rust. This can lead to the development of effective predictive models, enabling early detection and more efficient management of diseases in coffee plantations, optimizing production and reducing economic losses for farmers.
Why it matches plant phenotyping methodsコーヒー葉の病斑という植物の病害状態を対象としたマルチスペクトル画像データセットであり、再利用可能なフェノタイピング用データセットの提供が中心です。
abstractIn this work, a unique database of 6726 multispectral images of coffee leaves is presented.
Reproduction assets foundThe paper is a data descriptor whose own multispectral coffee leaf image dataset is publicly deposited on Kaggle with an explicit direct URL and DOI, matching an allowed URL.Dataset · publicth of 16 bits per pixel .
Data source location
Institution: Escuela Colombiana de Ingeniería Julio Garavito University
City/Town/Region: Bogotá D.C.
Country: Colombia Latitude: 4.5983° * Longitude: 74.0051°.
Data accessibility
Repository name: Coffe Rust
Data identification number: 10.34740/kaggle/ds/5644659
Direct URL to data: https://www.kaggle.com/ds/5644659
Instructions for accessing these data: Data available free of charge to anyone with access to the Internet and the web server address provided.
Related research article
[ 1 ] Jorge Luis Aroca Trujillo, Alexander Pérez-Ruiz. “Technologies Applied in the Field of Early Detection of Coffee Rust Fungus Diseases: A Review.” Nongye JOpen asset ↗Kaggle · 10.34740/kaggle/ds/5644659lines:1-53Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Plant phenomics has made significant progress recently, with new demand to move from external characterization to internal exploration through data combination. Hyperspectral and metabolomic data, with cause-and-effect relationship, are given priority for integration. However, few efficient integrating methods are available. Here, we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency. To verify its feasibility, two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants. Centered on salt tolerance, data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening, and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening. Target phenotype could be discriminated after five-day salt-treatment, much earlier than phenotypic difference appearance. 20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants, almost tripled that of unintegrated analysis. Accuracy rate, confirmed with salt-tolerance analysis for experimental verification, reached 90 %, which can be optimized to 100 % theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis. Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data. Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects, providing a powerful tool for phenotype selection in new-generation crop breeding.
Why it matches plant phenotyping methods高耐塩性表現型識別のため、ハイパースペクトルデータとメタボロームを統合した二段階フェノタイピング・スクリーニング手法を開発し、実験検証している。
abstracttwo-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.
Reproduction assets foundThe authors explicitly state that all source code (MATLAB implementation of the hyperspectral-metabolome combination pipeline) and the hyperspectral dataset (A17 and FNB mutants) required to reproduce the study are publicly available on GitHub. Both URLs appear in the allowed list and are quoted verbatim in the Data-avCode · publicAll relevant source codes and datasets, implementation in MATLAB R2022a for WINDOWS 11 64-bit operating system, required to reproduce the results reported in this study are available at https://github.com/DPF2024/Targeted-Hyperspectra-and-Metabolome-Combining-Method.gitOpen asset ↗DPF2024/Targeted-Hyperspectra-and-Metabolome-Combining-Methodlines:131-237Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease. Leaves of two durum wheat genotypes were studied under controlled conditions to track the evolution of Lb disease and capture significant spectral and spatial differences until the onset of symptoms. Hyperspectral image acquisitions were purchased with two cameras in visible-near infrared (VNIR) and short-wave infrared (SWIR) spectral ranges on eighteen dates between one day before inoculation and twenty days after inoculation. For each wavelength range studied, a total of 1175 images provided information on 3326 leaves measured throughout the experiment. These data are valuable since they can be used as a basis to monitor disease's development over time, to build leaf classification models according to their infection status per genotype per day, to develop prediction models related to symptoms' appearance, or to test imaging and spectral analysis methods.
Why it matches plant phenotyping methodsコムギ葉の病害状態をハイパースペクトル画像で取得したデータベースを構築し、感染状態分類・症状出現予測や画像解析手法の評価基盤として提供しており、表現型取得法が中心である。
abstractThis article presents a hyperspectral imaging (HSI) database of healthy leaves and leaves infected with Zymoseptoria tritici fungal pathogen responsible for leaf blotch (Lb) disease.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral imaging dataset of healthy and Zymoseptoria tritici-infected durum wheat leaves, deposited on Data INRAE with DOI 10.57745/WVP0FJ. This is the paper's own plant-phenotyping measurement data (VNIR/SWIR hyperspectral images, pixel coordinates, and CSVDataset · publicand HySpex SWIR-384 (Norsk Elektro Optikk, Norway).
Data source location
Institution: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE)
City: Montpellier
Country: France
Data accessibility
Repository name: Data INRAE
Data identification number: doi: 10.57745/WVP0FJ
Direct URL to data: https://doi.org/10.57745/WVP0FJ
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Value of the Data
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This dataset depicts the visual appearance and spectral information related to the onset kinetics of Lb disease symptoms on wheat leaves using hyperspectral images acquired post-inoculation.
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The images captured are valuable to monitor the evolution of the Lb disease on wheat leaves through the developmenOpen asset ↗Data INRAE · 10.57745/WVP0FJlines:1-60Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity and sustaining ecosystem services in cities. However, comprehensive field assessments are resource-intensive, and landscape-level approaches may overlook heterogeneity within urban regions. To address this challenge, we combined remote sensing with field inventories to comprehensively map and analyze urban forest attributes in forest patches across the Minneapolis-St. Paul Metropolitan Area (MSPMA) in a multistep process. First, we developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP). These models enabled accurate predictions of forest attributes, specifically nine metrics of plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, dbh, and canopy density), and structural complexity (variability in canopy height, dbh, and canopy density) with relative errors ranging between 11% and 21%. Second, we applied these machine learning models to predict diversity metrics for 804 additional plots from GEDI and Sentinel-2. Finally, we applied Bayesian multilevel models to the predicted diversity metrics to assess the influence of multiple factors-patch dimensions, landscape attributes, plot position, and jurisdictional agency-on these forest attributes across the 804 predicted plots. The models showed all predictors have some degree of effect on forest attributes, presenting varying explanatory power with R 2 values ranging from 0.071 to 0.405. Overall, plot characteristics (e.g., distance to nearest trail, proximity to forest edge) and jurisdictional agency explained a large portion of the variability across patches, whereas patch and landscape characteristics did not. The relative effect of plot versus management sets of predictors on the marginal ΔR 2 was heterogeneous across metrics and ecological subsections (an ecological classification designation). The multiplicity of determinants influencing urban forests emphasizes the intricate nature of urban ecosystems and highlights nuanced, heterogeneous relationships between urban ecological and anthropogenic factors that determine forest properties. Effectively enhancing biodiversity in urban forests requires assessments, management, and conservation strategies tailored for context-specific characteristics.
Why it matches plant phenotyping methodsGEDI・Sentinel-2と機械学習を統合し、植物の多様性・構造属性を予測する測定手法を開発、誤差評価し、追加プロットへ適用しているため、表現型取得が中心的である。
abstractwe developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP).
Reproduction assets foundThe paper's data availability statement provides three paper-specific public assets: the field vegetation inventory data on EDI, the machine learning ensemble R script on Zenodo, and the Bayesian model summaries on Zenodo.Dataset · publicVegetation data are available (Marcilio‐Silva et al., 2022 ) on the Environmental Data Initiative (EDI) data portal: https://doi.org/10.6073/pasta/166a4b954ecaaabcda75bd51004804a5Open asset ↗Environmental Data Initiative · 10.6073/pasta/166a4b954ecaaabcda75bd51004804a5lines:317-357Code · publicThe R script used for the machine learning model ensemble (Marcilio‐Silva, 2024 ) is available on Zenodo: https://doi.org/10.5281/zenodo.14395998Open asset ↗Zenodo · 10.5281/zenodo.14395998lines:317-357Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System.
Why it matches plant phenotyping methods航空分光画像を用いて植物多様性を予測するルーチンを開発し、地上データとの関連を評価しており、植物状態の推定手法が研究の中心である。
abstractWe harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US.
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 · publicR codes, including functions and examples, are available at Zenodo: https://doi.org/10.5281/zenodo.13983114Open asset ↗Zenodo · 10.5281/zenodo.13983114lines:153-226Code / 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 confirmedEurope PMC · checked 14 Sept 2026
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery. Employing the U-Net architecture with a ResNet-101 backbone, we explore three band combinations-multispectral, multispectral+NDVI, and multispectral+NDRE-to achieve superior segmentation accuracy. Due to the lack of suitable UAV-based datasets for rice disease, we generate our own dataset through disease inoculation techniques in experimental paddy fields. The dataset is increased using data augmentation and patch extraction methods to improve training robustness. Our findings demonstrate that the U-Net model incorporating ResNet-101 backbone trained with multispectral+NDVI data significantly outperforms other band combinations, achieving high accuracy metrics, including mean Intersection over Union (mIoU) of up to 97.20%, mean accuracy of up to 99.42%, mean F1-score of up to 98.56%, mean Precision of 97.97%, and mean Recall of 99.16%. Additionally, this approach efficiently segments healthy rice from other classes, minimizing misclassification and improving disease severity assessment. Therefore, the experiment concludes that the accurate mapping of the disease extent and severity level in the field is reliable to accurately allocating the compensation. The developed methodology has the potential for broader application in diagnosing other rice diseases, such as Blast, Bacterial Panicle Blight, and Sheath Blight, and could significantly enhance agricultural management through accurate damage mapping and yield loss estimation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と深層学習によるイネ病害の症状・重症度推定パイプラインを開発し、精度評価とデータセット構築を行っており、植物表現型取得が中心である。
abstractThis study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery.
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: We have provided the aerial datasets in figshare after getting permission from the landowner. Please see https://doi.org/10.6084/m9.figshare.26955862.v1 .Open asset ↗figshare · 10.6084/m9.figshare.26955862.v1lines:146-158Code / 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 confirmedEurope PMC · checked 13 Sept 2026
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( Solanum lycopersicum var. cerasiforme ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Why it matches plant phenotyping methods植物の生育・ストレス・健全性評価を目的とした、放射補正済みマルチスペクトル画像とオルソモザイクを含む再利用可能なデータセットであり、植物表現型取得基盤が中心です。
abstractThe dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels.
Reproduction assets foundThe paper is itself a data descriptor for a public UAV multispectral cherry tomato phenotyping dataset (calibrated aerial images, manual plant images, orthomosaics, binary masks) deposited in Dryad, with an explicit DOI and direct URL matching an allowed URL.Dataset · publicRepository name: tomatodb
Data identification number: 10.5061/dryad.63xsj3vbd
Direct URL to data: https://datadryad.org/stash/share/Wq_X7QUyGryJ-ZnmgfwRn4MtOCr4VBm_MSnhF40sv_8#readmeOpen asset ↗Dryad · 10.5061/dryad.63xsj3vbdlines:1-42Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The selection and promotion of high-yielding and nitrogen-efficient wheat varieties can reduce nitrogen fertilizer application while ensuring wheat yield and quality and contribute to the sustainable development of agriculture; thus, the mining and localization of nitrogen use efficiency (NUE) genes is particularly important, but the localization of NUE genes requires a large amount of phenotypic data support. In view of this, we propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots, propose a wheat 3D plot segmentation dataset, quantify the plot canopy height via combination with PointNet++, and generate 4 nitrogen utilization-related vegetation indices via index calculations. Six height-related and 24 vegetation-index-related dynamic digital phenotypes were extracted from the digital phenotypes collected at different time points and fitted to generate dynamic curves. We applied height-derived dynamic numerical phenotypes to genome-wide association studies of 160 wheat cultivars (660,000 single-nucleotide polymorphisms) and found that we were able to locate reliable loci associated with height and NUE, some of which were consistent with published studies. Finally, dynamic phenotypes derived from plant indices can also be applied to genome-wide association studies and ultimately locate NUE- and growth-related loci. In conclusion, we believe that our work demonstrates valuable advances in 3D digital dynamic phenotyping for locating genes for NUE in wheat and provides breeders with accurate phenotypic data for the selection and breeding of nitrogen-efficient wheat varieties.
Why it matches plant phenotyping methods航空画像・3D点群・マルチスペクトル画像から小麦区画の草冠高と植生指数を抽出するデジタルフェノタイピング手法を開発・適用しており、表現型取得が研究の中心である。
abstractwe propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' source code, testing data, and supporting datasets (including the W3DPS 3D plot segmentation dataset and phenotyping/GWAS data) at two public Quark pan links under CC BY 4.0. These are paper-specific, publicly actionable assets. Other allowed URLsCode · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Dataset · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://pan.quark.cn/s/afbf9025b19e and https://pan.quark.cn/s/47e91f9d6c9c .Open asset ↗lines:138-156Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of crops across large areas, particularly when deployed on robotic platforms such as unmanned aerial vehicles (UAVs). However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and vineyard trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.
Why it matches plant phenotyping methods植物の健康状態を推定するためのマルチスペクトル・ハイパースペクトル画像と葉の形質測定を組み合わせた評価用データセットが主題であり、植物フェノタイピング手法のベンチマーク資源に該当する。
abstractThis work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages
Reproduction assets foundThe paper is a dataset descriptor; its complete plant-phenotyping measurements (multispectral leaf images, hyperspectral reflectance, chlorophyll, nitrogen, weight/FMC across five drying stages for avocado, olive, and vineyard) are publicly deposited on figshare under DOI 10.6084/M9.FIGSHARE.26950660, along with aMatlåDataset · publicAll the data is available at this repository DOI: 10.6084/M9.FIGSHARE.26950660Open asset ↗figshare · 10.6084/M9.FIGSHARE.26950660pdf-page:15 lines:1-59Code / dataset availability 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 15 Sept 2026
Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400 nm) and plant defense hormone-mediated responses (750 nm) rather than changes in leaf pigments or internal structure (800-900 nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.
Why it matches plant phenotyping methodsトマト葉の病徴状態をハイパースペクトル画像と機械学習で推定し、病害進行段階、前症状検出、異常葉斑との識別を検証することが中心である。
abstractHyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves.
Reproduction assets foundThe paper's raw hyperspectral image data (tomato leaf HSI used for phenotyping/analysis) are publicly deposited on Ag Data Commons. No author analysis code is publicly shared; evaluation metrics are only available upon request.Dataset · publicSpecies at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.
Author contributions
X.Z. designed and conducted the experiment. X.Z. analyzed the data. X.Z., B.V, and S.L. wrote the manuscript.
Data availability
The raw hyperspectral image data have been uploaded to Ag Data Commons: https://data.nal.usda.gov/dataset/early-detection-bacterial-spot-disease-tomato-hyperspectral-imaging . Full evaluation metrics for all models are available upon request.
Declarations
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 instOpen asset ↗Ag Data Commonslines:100-122Code / 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 · checked 7 Sept 2026
Multispectral / hyperspectralRootClassificationRoot system architecture
Background Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.
Why it matches plant phenotyping methods根系のハイパースペクトル画像取得・分類パイプラインを開発し、取得条件、分類法、機械学習手法を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ある。
abstractThis study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale
Reproduction assets foundThe authors explicitly state that the Python scripts for the paper's HSI root-soil classification pipeline are publicly available on GitHub. No phenotype dataset or image deposit is stated.Code · publicThe scripts for data analysis are available from https://github.com/corinef/Automated-root-classification .Open asset ↗corinef/Automated-root-classificationlines:156-180Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 13 Sept 2026
Abstract In recent years, the automation of genotyping has significantly enhanced the efficiency of genome-wide association studies. As a result, phenotyping rather than genotyping is now the rate-limiting step, especially in field experiments. For this reason, there is a strong need to further automate in-field phenotyping. Here we present a GWAS study on 194 field-grown accessions of lettuce ( Lactuca sativa ). These accessions were non-destructively phenotyped at two time points 15 days apart using an unmanned aerial vehicle. Our high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation. We used the mean and other descriptives such as median, quantiles, minimum and maximum to quantify different aspects of color and height variation in lettuce from the drone images. Using this approach, we confirm several previously described QTLs, now in populations grown under field conditions, and identify several new QTLs for plant-height and color.
Why it matches plant phenotyping methodsドローン搭載RGB・マルチスペクトルカメラと高さ推定を統合した圃場フェノタイピング手法を開発・適用し、画像からレタスの色と草丈を定量化しているため、方法が研究の中心です。
abstractOur high throughput phenotyping approach integrates an RGB camera, a multispectral camera to measure the reflectance at 5 wavelengths (blue, green, red, red edge, near-infrared), and precise height estimation.
Reproduction assets foundThe paper explicitly states that analysis scripts are publicly available on GitHub (SnoekLab/Dijkhuizen_etal_2025_Drone) and that extended data (raw data, intermediate steps, figure data, weather data) is deposited at the Utrecht University repository DOI 10.24416/UU01-S5FCM9. Both are paper-specific, public, and verbiCode · publicScripts used for this study are available on github:
https://github.com/SnoekLab/Dijkhuizen_etal_2025_Drone.Open asset ↗SnoekLab/Dijkhuizen_etal_2025_Dronepdf-page:8 lines:1-120Dataset · publicExtended data available on https://doi.org/10.24416/UU01-S5FCM9. This includes all raw
data to reproduce results, all intermittent steps, the data required to generate all figures and
the weather data.Open asset ↗10.24416/UU01-S5FCM9pdf-page:8 lines:1-120Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.
Why it matches plant phenotyping methods植物のクロロフィルおよび糖含量を定量可能なオンチップ・ハイパースペクトル画像センサーを開発しており、センサー技術と植物形質取得への応用が中心的である。
abstractHere we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution.
Reproduction assets foundThe paper explicitly states that all data generated or analysed are available in a public GitHub repository (hyperspectral image/video dataset collected with the HyperspecI sensors) and that demo code is available in another public GitHub repository. Both are paper-specific, public, and actionable.Dataset · publiced the project.
Peer review
Peer review information
Nature thanks Yidong Huang, Yunfeng Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, Open asset ↗bianlab/Hyperspectral-imaging-datasetlines:148-189Code · publiche peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, ZL 2022 1 0764143.4, ZL 2022 1 0764141.5, ZL 2019 1 0441784.4, ZL 2019 1 0482098.1 and ZL 2019 1 1234638.0) and submitted the related patent applications.Open asset ↗bianlab/HyperspecIlines:148-189Code / 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 confirmedOpenAlex · checked 14 Sept 2026
Abstract Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.
Why it matches plant phenotyping methodsUASマルチスペクトル時系列画像から収量・生育期形質を予測する機械学習手法を比較・評価し、米のベンチマークデータセットも提供しており、表現型取得・推定手法が研究の中心である。
abstractprediction models often fail to generalize well from one year to the next or to new environments.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw and processed UAS imagery, extracted features, and agronomic data on Dryad, and the authors' analysis code on GitHub. Both are paper-specific, public, and actionable.Dataset · publicts complied with the
current laws of the United States, the country in which they
were performed.
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
Emily S. Bellis is a full time employee of Avalo, Inc., a crop
improvement company.
DATA AVA I L A B I L I T Y S TAT E M E N T
Raw and processed UAS images are available on Dryad
(https://doi.org/10.5061/dryad.v41ns1s4z) along with
extracted features and agronomic data for the 2021 and 2022
field seasons. Code to reproduce the analyses are available at
https://github.com/FareedFarag/TPPJ-Modeling-Code.O RC I D
FaredFarag https://orcid.org/0000-0002-4659-6781
Trevis D. Huggins https://orcid.org/0000-0002-1937-6687
JeremyD. Edwards https://orcidOpen asset ↗Dryad · 10.5061/dryad.v41ns1s4zpdf-raw-page:16 lines:1-86Code · public61/dryad.v41ns1s4z) along with
approaches for rice trait prediction using UAS imagery as extracted features and agronomic data for the 2021 and 2022
the primary data source. While showcasing the potential of field seasons. Code to reproduce the analyses are available at
various modeling approaches, it also emphasizes the trade- https://github.com/FareedFarag/TPPJ-Modeling-Code.
offs between performance and interpretability for applications
in precision agriculture and plant breeding. Looking for- ORCID
ward, extending the study over multiple years, extending to Fared Farag https://orcid.org/0000-0002-4659-6781
hyperspectral sensors, and exploring additional remotely Trevis D. Huggins https:/Open asset ↗GitHub · FareedFarag/TPPJ-Modeling-Codepdf-layout-page:16 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.
Why it matches plant phenotyping methods森林の機能形質を推定するための大規模ハイパースペクトルLUTデータセットを構築しており、形質取得・推定基盤そのものが研究の中心である。
abstractwe present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral LUT dataset (3.5 million trait/structural combinations) deposited in the Czech National Repository, including the authors' processing codes (merge_images.m, LUT_processing.py) within the deposit. The direct repository URL is given in the text and isDataset · publiceaf pixels.
Data source location
Institutions: Institute of Computer Science, Masaryk University; Global Change Research Institute of the Czech Academy of Sciences
City: Brno
Country: Czech Republic
Data accessibility
Repository name: National Repository
Data identification number: 10.48700/datst.bcnpf-47q73
Direct URL to data: https://data.narodni-repozitar.cz/general/datasets/4y0sy-qh735
1.
Value of the Data
•
Look-Up Tables (LUT) are considered important training datasets for machine learning models to predict leaf traits.
•
To date, only a limited number of LUT datasets have been developed for forest sites, particularly for Central European temperate broadleaf forests. Most of them are lOpen asset ↗National Repository · 10.48700/datst.bcnpf-47q73lines:36-69Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Multispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests. Information from estimated growth curves can be used to infer harvest biomass and to gain insights into the relationship between growth dynamics and forage biomass stability across cuttings and years. In this study, multispectral imaging and several common vegetation indices were used to estimate genetic parameters and model growth of alfalfa cultivars to determine the longitudinal relationship between vegetation indices and forage biomass. Results showed moderate heritability for vegetation indices, with median plot level heritability ranging from 0.11 to 0.64, across multiple cuttings in three trials planted in Ithaca, NY, and Las Cruces, NM. Genetic correlations between the normalized difference vegetation index and forage biomass were moderate to high across trials, cuttings, and the timing of multispectral image capture. To evaluate the relationship between growth parameters and forage biomass stability across cuttings and environmental conditions, random regression modeling approaches were used to estimate the growth parameters of cultivars for each cutting and the variance in growth was compared to the variance in genetic estimates of forage biomass yield across cuttings. These analyses revealed high correspondence between stability in growth parameters and stability of forage yield. The results of this study indicate that vegetation indices are effective at modeling genetic components of biomass accumulation, presenting opportunities for more efficient screening of cultivars and new longitudinal modeling approaches that can provide insights into temporal factors influencing cultivar stability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数を用いてアルファルファのバイオマス蓄積を推定・モデル化し、遺伝パラメータや生育安定性を評価することが研究の中心であるため。
abstractMultispectral imaging by unoccupied aerial vehicles provides a nondestructive, high-throughput approach to measure biomass accumulation over successive alfalfa (Medicago sativa L. subsp. sativa) harvests.
Reproduction assets foundThe paper's authors publicly deposited the R analysis code and input data for the random regression growth-curve modeling and stability analysis in a GitHub repository, explicitly stated in the Data availability section. Phenotype/imagery data themselves are only available upon request (request_only), and Pix4D is a第三方Code · publicAll data is available upon request. R Code and input data are available in the github: https://github.com/rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfa .Open asset ↗rthapa1/FFAR_RandomRegressionModel_growthcurve_modelling_stabilityanalysis_alfalfalines:145-180Code / dataset availability confirmedOpenAlex · bioRxiv · checked 15 Sept 2026
Abstract Globally, vegetation biodiversity is expected to decline as the rate of plant adaptation struggles to keep pace with rising temperatures. To support conservation efforts through remote sensing, we disentangled the nested effects of genetic and environmental influences on reflectance spectra, leveraging spectroscopy to assess plant adaptations to temperature. Specifically, we quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance using clonal replicates propagated from 16 populations and grown across three common gardens spanning a mean annual temperature gradient representing the thermal range of P. fremontii . We used variance partitioning to decompose phenotypic variation expressed in the leaf spectra into genotypic and environmental components to estimate broad-sense heritability. Heritability was strongly expressed in the spectral red edge (∼680-750nm) and shortwave infrared (∼1400-3000nm), though the heritability peak in the red edge was sensitive to extreme temperatures. By comparing distances of group centroids in principal component space, we determined that P. fremontii intraspecific spectral variation was shaped by the interaction between common garden site conditions and source population. Support vector machine models indicated pronounced environmental influence on spectral variation, as P. fremontii source population and garden location were classified at 71.8% and 92.6% accuracy, respectively. These findings emphasize the utility of reflectance data in separating genetic and environmental influences on plant phenotypes, offering a pathway to scale these insights across broader landscapes and aid in the conservation and management of vulnerable ecosystems in a warming climate.
Why it matches plant phenotyping methods葉の反射スペクトルを植物表現型として取得し、遺伝性・環境効果の分離、スペクトル変異の分類、温度適応評価に体系的に利用しており、単なる補助的な測定ではなく主要な解析基盤である。
abstractwe quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available on GitHub at the authors' repository (MegsSeeley/temperature_cottonwood). The phenotype/spectral data files are promised on Figshare only 'upon acceptance', so they are not yet publicly actionable and the Figshare DOI is not in the allowed URL listCode · publicAll authors reviewed
528 several drafts and agreed with the final version.
529 Availability of data: All data files will be made available on the Figshare database upon
530 acceptance of the manuscript at DOI: 10.6084/m9.figshare.25719585.
531 Code availability: Code is available on GitHub and is maintained by Seeley (2025)
532 https://github.com/MegsSeeley/temperature_cottonwood.
533 Conflict of interest: The authors have declared that no competing interests exist.
534
535 References
536 Ahmad, P., & Prasad, M. N. V. (2011). Environmental Adaptations and Stress Tolerance of
537 Plants in the Era of Climate Change. Springer Science & Business Media.
24Open asset ↗MegsSeeley/temperature_cottonwoodpdf-layout-page:24 lines:1-55Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.
Why it matches plant phenotyping methodsUAV・衛星リモートセンシングと機械学習により森林の地上部バイオマス（炭素蓄積量）を推定し、現地測定と精度検証しているため、植物群落レベルの形質推定手法が中心です。
abstractWe used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest
Reproduction assets foundThe paper's Data Availability Statement points to an openly available Zenodo deposit containing the original study data (field carbon stock measurements, UAV-derived datasets, and Sentinel-2 based analysis inputs). No separate author analysis code or trained model repository is mentioned.Dataset · publicData Availability Statement: The original data presented in the study are openly available here:
https://doi.org/10.5281/zenodo.11657557, accessed on 5 June 2024.Open asset ↗zenodo · 10.5281/zenodo.11657557pdf-page:17 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とYOLOv9、二重チャネルNMSを用いてコーヒー植物の検出・セグメンテーション・個体数推定手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
titleA Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral 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。Code · publicWe will publish all the codes and datasets in this study after the article is accepted
https://github.com/legend2588/Coffee-plant-counting.gitOpen asset ↗https://github.com/legend2588/Coffee-plant-counting.gitpdf-page:19 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.
Why it matches plant phenotyping methodsMODIS反射率から作物のFVCを推定するリモートセンシング手法について、NBAR補正の時期効果を比較・検証しており、植物形質推定が研究の中心である。
abstractThe PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product.
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 · publicwhile the GEOV3 FVC data can be downloaded from:
https://land.copernicus.eu/global/products/fcoverOpen asset ↗pdf-page:17 lines:1-59Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract The use of high-altitude remote sensing (RS) data from aerial and satellite platforms presents considerable challenges for agricultural monitoring and crop yield estimation due to the presence of noise caused by atmospheric interference, sensor anomalies, and outlier pixel values. This paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers. Applying this technique to 20,946 Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2003 to 2015 improved the mean peak signal-to-noise ratio (PSNR) to 40.91 dB. Integrating Quartile Clean data with Convolutional Neural Networks (CNN) models with exponential decay learning rate scheduling achieved RMSE improvements up to 5.88% for soybeans and 21.85% for corn, while Long Short-Term Memory (LSTM) models demonstrated RMSE reductions up to 11.52% for soybeans and 29.92% for corn using exponential decay learning rates. To compare the proposed method with state-of-the-art techniques, we introduce the Vision Transformer (ViT) model for crop yield estimation. The ViT model, applied to the same dataset, achieves remarkable performance without explicit pre-processing, with R 2 scores ranging from 0.9752 to 0.9875 for soybean and 0.9540 to 0.9888 for corn yield estimation. The RMSE values range from 7.75086 to 9.76838 for soybean and 26.25265 to 34.20382 for corn, demonstrating the ViT model's robustness. This research contributes by (1) introducing the Quartile Clean Image method for enhancing RS data quality and improving crop yield estimation accuracy, and (2) comparing it with the state-of-the-art ViT model. The results demonstrate the effectiveness of the proposed approach and highlight the potential of the ViT model for crop yield estimation, representing a valuable advancement in processing high-altitude imagery for precision agriculture applications.
Why it matches plant phenotyping methods作物収量という植物形質を遠隔センシング画像から推定する前処理法を開発し、CNN・LSTM・ViTとの比較で性能を検証しており、フェノタイピング手法が中心です。
abstractThis paper introduces a "Quartile Clean Image" pre-processing technique to address these data issues by analyzing quartile pixel values in local neighborhoods to identify and adjust outliers.
Reproduction assets foundThe paper's declarations section explicitly states that the code for data collection, processing, and analysis is openly available on GitHub, and that a sample dataset used in the study is available via a Google Drive link. Both are paper-specific, public, and actionable.Code · publicThe code used in this study is openly available on GitHub at
https://github.com/mananthakkar24/RemoteSensingBlobDetection. This reposi-
tory contains all the necessary code for data collection, processing, and analysis as
described in this paper.Open asset ↗mananthakkar24/RemoteSensingBlobDetectionpdf-page:3 lines:1-48Dataset · publicA sample dataset used in this study is available at:
https://drive.google.com/drive/folders/18Z3hcqRf0nnE5vjqDat99qh3o-Open asset ↗pdf-page:3 lines:1-48Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.
Why it matches plant phenotyping methods衛星スペクトル指数と解析手法によりダイズの生育ステージを推定し、空間解像度や分類性能を評価しており、植物フェノタイピング手法の検証・適用が研究の中心です。
abstractThe aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops.
Reproduction assets foundThe authors state all relevant data (soybean phenology/vegetation index measurements from Sentinel-2 and Amazonia-1) are publicly available in their GitHub repository.Dataset · publicat the difference in spatial resolution of the two sensors evaluated, 10 meters per pixel of Sentinel-2 and 65 meters per pixel of Amazônia-1, is not a determining factor in the correct identification of soybean phenological stages.
Data Availability
All relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2 .
Funding Statement
This study was funded by the College of Food and Agriculture Sciences, King Saud University, RSPD2024R678 (to Mohamed A. El-Tayeb). This study was also funded by a scholarship from CAPES, Coordination for the Improvement of Higher Education Personnel, 88887.677482/2022-00 (to Airton Andrade Open asset ↗FSilva-826/DADOS---AMAZONIA1-CENTINEL2lines:240-262Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small-grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment, which are typically laborious and time consuming. In this work, we developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index. Such tools are an important step toward the creation of automated and efficient phenotyping methods. The data used to generate the results are 3D point clouds consisting of four colour channels—red, green, blue (RGB), and near-infrared (NIR)—collected using a multispectral 3D scanner. Our 3D CNN models for FHB detection achieved 100% accuracy. The influence of the multispectral information on performance was evaluated; the results showed the dominance of the RGB channels over both the NIR (720 nm peak wavelength) and the NIR plus RGB channels combined. Our best 3D CNN models for estimation of total and infected number of spikelets achieved mean absolute errors (MAEs) of 1.13 and 1.56, respectively. Our best 3D CNN models for FHB severity estimation achieved 8.6 MAE. A linear regression analysis between the visual FHB severity assessment and the FHB severity predicted by our 3D CNN showed a significant correlation.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャンと3D CNNを用いて、コムギのFHB症状、穂の小穂数、感染小穂数、病害重症度を自動推定する手法を開発・評価しており、植物表現型取得が中心である。
abstractwe developed automated applications based on three-dimensional (3D) convolutional neural networks (CNNs) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index.
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 confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740–1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R 2 , RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R 2 = 0.56 and R 2 = 0.51 respectively while breakdown had calibration and validation accuracy of R 2 = 0.66 and R 2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R 2 = 0.65 and R 2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.
Why it matches plant phenotyping methods携帯型近赤外分光法と機械学習モデルを用いて、カッサバ育種材料のペースト特性を推定するモデルを開発・検証しており、形質取得法が研究の中心である。
abstractThe use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits.
Reproduction assets foundThe paper's Data Availability statement points to a public GitHub repository containing the SCiO calibration data (spectra and pasting-property reference values) used in this study. The URL matches an allowed URL verbatim. No separate analysis code or trained model deposit is stated.Dataset · publicM.A; methodology, M.A, and W.A; data analyses, M.A and W.A; writing – original draft preparation, M.A; review and editing, P.W, E.M, G.M, E.K, R.E, P.T, S.K, I.R, P.O.O, and H.K, All authors have read and agreed to the published version of the manuscript.
Data availability
The data used in this study are available on GitHub at https://github.com/mikidadio/SCiO-Calibration-data.gi.
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 affiliations.
References
1. Hershberger, J. et al. Low-cost, handheld near-infrared spectroscopy for root dry matter conOpen asset ↗SCiO-Calibration-datalines:341-371Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p
Why it matches plant phenotyping methods光学・SARセンサーデータを統合してライムギの地上部バイオマスを推定し、回帰モデルを訓練・検証しているため、植物形質の取得・推定手法が研究の中心です。
abstractThis study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets.
Reproduction assets foundThe paper's Sentinel-1 SAR processing workflow is publicly shared as a Google Earth Engine JavaScript script with an explicit availability statement and URL. The field AGB measurements and PlanetScope data are not public (available only on reasonable request from the corresponding author).Code · publicData Availability Statement: The Google Earth Engine script to process Sentinel-1 data is available at
[https://code.earthengine.google.com/219fc3c05b8ae8132ae2d758ccba3d1e?noload=true] (accessed
on 12 July 2024).Open asset ↗pdf-page:17 lines:1-59Code / dataset availability confirmedCrossref · Europe PMC · checked 7 Sept 2026
Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.
Why it matches plant phenotyping methodsジャガイモの水欠乏状態をハイパースペクトル画像と深層学習で識別し、性能評価および重要波長の同定を行っており、植物ストレス表現型の取得・抽出手法が中心である。
abstractHyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the pre-processed hyperspectral dataset on Zenodo and the authors' analysis code on GitHub, both with public URLs matching allowed_urls. The SiaPy Zenodo record is a generic open-source library, not a paper-specific asset.Code · publicand code at https://github.com/janezlapajne/manuscripts (accessed on 8 July 2024)Open asset ↗github · janezlapajne/manuscriptslines:104-424Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から穀粒のメラニン着色状態を抽出する解析パイプラインとソフトウェアを開発・適用しており、表現型取得・解析手法が中心である。
abstractThis paper presents a hyperspectral image processing pipeline
Reproduction assets foundThe paper's authors publicly release the hyperspectral image processing pipeline (Python source code) used for the barley grain melanin analysis, and the supplementary material lists the 313 barley accessions with their pigmentation (melanin-containing vs. non-containing) phenotype labels used in the study.Code · publicThe source code is available at: https://github.com/igor2704/Hyperspectral_images.Open asset ↗https://github.com/igor2704/Hyperspectral_imageslines:1-42Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methods高スループット表現型プラットフォームによる時系列の植物形質取得が研究の中心的データ基盤であり、複数形質の縦断測定と予測への応用を評価している。
abstractwe combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare deposit containing all phenotypic data (temporal HTP traits: plant height, LAI, spectral vegetation indices), genomic data, and GWAS results for this sesame study. No author analysis code repository is explicitly deposited.Dataset · publicfor longitudinal traits derived from single time points (green) and random regression (orange) analysis.
Figure S3 . Phenotypic (green) and genetic (orange) correlations between longitudinal traits at each time point and seed‐yield.
Data Availability Statement
All phenotypic data, genomic data, and GWAS results can be found at https://doi.org/10.6084/m9.figshare.24961491 .Open asset ↗figshare · 10.6084/m9.figshare.24961491lines:1055-1061Code / dataset availability confirmedOpenAlex · checked 7 Sept 2026
Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
Why it matches plant phenotyping methodsマルチスペクトル画像とVision Transformerを用いて、トウモロコシ個体の干ばつストレス状態を推定する解析パイプラインを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform.
Reproduction assets foundThe paper's raw maize drought-stress imaging dataset (three trials, downsampled NGB/NIR images) is openly deposited on Zenodo with DOI 10.5281/zenodo.10991581. No author analysis code, trained models, or annotations are stated as publicly available.Dataset · publicData Availability Statement: The original data presented in the study are openly available on
the data sharing platform Zenodo https://zenodo.org/records/10991581 ( accessed on 18 April
2024) with DOI 10.5281/zenodo.10991581. The repository contains raw images before any of the
pre-processing steps mentioned in Section 3.Open asset ↗Zenodo · 10.5281/zenodo.10991581pdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.
Why it matches plant phenotyping methods小麦の感染状態をハイパースペクトル画像から推定する分類手法を開発し、実験室データで学習したモデルを圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractA machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes.
Reproduction assets foundThe paper's hyperspectral lab/field wheat scan datasets are publicly deposited in OSU Scholars Archive (DOI 10.7267/z316q855z). Code is only available upon request, so it does not qualify as a public asset.Dataset · publicntal Monitoring Programs
(CTEMPs); and Collaborative Research; CompSustNet: Expanding the Horizons of Computational Sustain-
ability, respectively).
Availability of data and material The datasets generated during and/or analyzed during the current study are
available in the Oregon State University’s Scholars Archive repository, https://doi.org/10.7267/z316q855z.Code availability Code will be made available upon request.
Declarations
Conflicts of interest/competing interests The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative CommonsAttribution 4.0 International License, which
permits use, sharing, adaptation, distribution aOpen asset ↗Oregon State University’s Scholars Archive · 10.7267/z316q855zpdf-raw-page:17 lines:1-41Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.
Why it matches plant phenotyping methods植物のRGB・マルチ/ハイパースペクトル・熱・深度画像と植物形質ラベルを生成するシミュレーション基盤を開発しており、表現型取得・学習用データ生成が中心的な方法論的貢献である。
abstractwe proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation.
Reproduction assets foundThe paper's phenotyping analysis relies on three public, paper-specific assets: the Helios framework code (used to generate the synthetic annotated images), the MSU-PID bean image dataset, and the strawberry.00 annotated dataset, all with explicit open-availability statements and URLs.Dataset · publicThe Bean data that support the findings of this study are openly available in MSU-PID at https://www.cse.msu.edu/computervision/MVA15-MSU-PID.zipOpen asset ↗MSU-PIDlines:255-283Dataset · publicThe strawberry data that support the findings of this study are openly available in strawberry.00 at https://universe.roboflow.com/skripsie/strawberry.00Open asset ↗strawberry.00lines:255-283Code / dataset availability confirmedCrossref · checked 15 Sept 2026
The PlantEye multispectral scanner is an optoelectrical sensor automatically applied to a mechatronic platform that allows the non-destructive, accurate, and high-throughput detection of morphological and physiological plant parameters. In this study, we describe how the advanced phenotyping platform precisely assesses changes in plant architecture and growth parameters of wild rocket salad (Diplotaxis tenuifolia L. [DC.]) under drought stress conditions. Four different irrigation supply levels from moderate to severe, required to keep 100, 70, 50, and 30% of the water-holding capacity, were adopted. Growth rate and plant architecture were recorded through the digital measure of biomass, leaf area, Canopy Light Penetration Depth, five convex hull traits, plant height, Surface Angle Average, and Voxel Volume Total. Vegetation color assessments included hue, lightness, and saturation. Vegetation and senescence indices were calculated from canopy reflectance in the red (620–645 nm), green (530–540 nm), blue (peak wavelength 460–485 nm), near-infrared (820–850 nm), and 3D laser (940 nm) ranges. The temperature, relative humidity, and solar radiation of the environment were also recorded. Overall, morphological parameters, color, multispectral data, and vegetation indices provided over 7200 data points through daily scans over three weeks of cultivation. Although a general decrease in growth parameters with increasing stress severity was observed, plants were able to maintain the same morpho-physiological performances as the control during the early growth stages, keeping both 70% and 50% of the total water-holding capacity. Among indices, the Normalized Differential Vegetation Index (NDVI) contributed the most to the differentiation between different stress levels during the cultivation cycle. Across the 3 weeks of growth, statistically significant differences were observed for all traits except for the Saturation Average. Comparisons with respect to the control highlighted the strong impact of drought stress on morphological plant traits. This study provided meaningful insights into the health status of wild rocket salad under increasing drought stress.
Why it matches plant phenotyping methodsPlantEye multispectral3Dプラットフォームを用いた植物形態・生理形質の高スループット取得が研究の中心であり、乾燥ストレス実験への実質的なフェノタイピング適用である。
abstractThe PlantEye multispectral scanner is an optoelectrical sensor automatically applied to a mechatronic platform that allows the non-destructive, accurate, and high-throughput detection of morphological and physiological plant parameters.
Reproduction assets foundThe paper deposits its raw phenotyping and climate data on Figshare with explicit open-access availability statements: Data File 1 (climate datalogger) at DOI 10.6084/m9.figshare.25201160 and Data File 2 (PlantEye F500 drought-stress phenotyping, ~7200 data points) at DOI 10.6084/m9.figshare.25201172. No author code orDataset · publice gathered 7200 phenotypic data
points on both control and water-stressed plants from 8 June to 26 June 2023 (Table 2: Data
File 2).
Table 2. Overview of Data Files reporting raw climatic and phenotyping data.
Label Name of Data File Data Repository and DOI Identifier
Data File 1
D. tenuifolia_Trial_Climate
Datalogger
Figshare
(https://doi.org/10.6084/m9.figshare.25201160,
accessed on 6 May 2024)
Data File 2
D_tenuifolia_Water_Stress_
F500Phenotyping
Figshare
(https://doi.org/10.6084/m9.figshare.25201172,
accessed on 6 May 2024)
The applied stresses highlighted substantial changes in the morphology and canopy
of the plant (Figure 2). The Three-Dimensional Leaf Area consistently decreased witOpen asset ↗Figshare · 10.6084/m9.figshare.25201160pdf-raw-page:4 lines:1-58Dataset · publicble 2. Overview of Data Files reporting raw climatic and phenotyping data.
Label Name of Data File Data Repository and DOI Identifier
Data File 1
D. tenuifolia_Trial_Climate
Datalogger
Figshare
(https://doi.org/10.6084/m9.figshare.25201160,
accessed on 6 May 2024)
Data File 2
D_tenuifolia_Water_Stress_
F500Phenotyping
Figshare
(https://doi.org/10.6084/m9.figshare.25201172,
accessed on 6 May 2024)
The applied stresses highlighted substantial changes in the morphology and canopy
of the plant (Figure 2). The Three-Dimensional Leaf Area consistently decreased with the
incremental stress during the 3 weeks of this study. We observed how, in control conditions,
LA3D increased from the first to theOpen asset ↗Figshare · 10.6084/m9.figshare.25201172pdf-raw-page:4 lines:1-58Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Efficiently determining crop water stress is vital for optimising irrigation practices and enhancing agricultural productivity. In this realm, the synergy of deep learning with remote sensing technologies offers a significant opportunity. This study introduces an innovative end-to-end deep learning pipeline for within-field crop water determination. This involves the following: (1) creating an annotated dataset for crop water stress using Landsat 8 imagery, (2) deploying a standalone vision transformer model ViT, and (3) the implementation of a proposed CNN-ViT model. This approach allows for a comparative analysis between the two architectures, ViT and CNN-ViT, in accurately determining crop water stress. The results of our study demonstrate the effectiveness of the CNN-ViT framework compared to the standalone vision transformer model. The CNN-ViT approach exhibits superior performance, highlighting its enhanced accuracy and generalisation capabilities. The findings underscore the significance of an integrated deep learning pipeline combined with remote sensing data in the determination of crop water stress, providing a reliable and scalable tool for real-time monitoring and resource management contributing to sustainable agricultural practices.
Why it matches plant phenotyping methods作物の水ストレスという植物状態を対象に、Landsat画像の注釈付きデータセット作成とCNN-ViT／ViTモデルの比較評価を行っており、植物状態の推定手法が研究の中心である。
abstractcreating an annotated dataset for crop water stress using Landsat 8 imagery
Reproduction assets foundThe paper's ground-truth crop water stress annotations derive from the public SMAPVEX16 Manitoba PALS brightness temperature and soil moisture/VWC dataset (NSIDC), cited in the Data Availability Statement and references. No author analysis code, trained models, or annotated dataset release is stated.Dataset · public/arxiv.org/abs/2209.05700 (accessed on 13 October 2023).
24. Colliander, A.; Misra, S.; Cosh, M. SMAPVEX16 Manitoba PALS Brightness Temperature and Soil Moisture Data, Version 1’
[VSM_20160718, VWC_20160718]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive
Center, 2019. Available online: https://nsidc.org/data/sv16m_pltbsm/versions/1 (accessed on 28 July 2023).
25. Zhou, Z.; Majeed, Y.; Naranjo, G.D.; Gambacorta, E.M. Assessment for crop water stress with infrared thermal imagery in
precision agriculture: A review and future prospects for deep learning applications. Comput. Electron. Agric. 2021, 182, 106019.
[CrossRef]
26. Sarwar, A.; Khan, M. TechnoOpen asset ↗sv16m_pltbsmpdf-raw-page:17 lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The "EscaYard" dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.
Why it matches plant phenotyping methodsブドウの病徴・生産性・房形質を対象とするマルチモーダル画像／UAVデータセットであり、植物フェノタイピングや病害・収量推定アルゴリズムの開発と評価を主目的としているため。
abstractThe "EscaYard" dataset provides a foundation for advancing research in sustainable farming practices, optimising crop health, and improving productivity through precise agricultural technologies.
Reproduction assets foundThe paper is a Data in Brief article describing the EscaYard dataset, publicly deposited on Zenodo with explicit DOI and direct URL. The dataset contains the paper's own phenotyping measurements (geotagged smartphone images, phytosanitary status, grape cluster counts, UAV orthomosaics, 3D point clouds, RTK GNSS trunk-Dataset · publics
City/Town/Region: Tomiño, Pontevedra, Galicia
Country: Spain
Coordinates: Vineyard B7, X: 517183.8, Y: 4645072.8; Vineyard B9, X: 516987.8, Y: 4644823.7 (ETRS89 / UTM zone 29N, EPSG:25829).
Data accessibility
Repository name: Zenodo
Data identification number: https://zenodo.org/doi/10.5281/zenodo.10362567
Direct URL to data: https://zenodo.org/records/10362567
1.
Value of the Data
•
The dataset offers a unique combination of multimodal data, including geotagged smartphone images, UAV orthomosaics, 3D point clouds, and precise geolocation data, enabling a multifaceted analysis of vineyard health and productivity.
•Open asset ↗Zenodo · 10.5281/zenodo.10362567lines:1-49Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から得た縦断的NDVIを植物・圃場プロットの表現型として用い、空間・時系列モデリング手法を提案・評価しており、表現型取得と解析ワークフローが研究の中心です。
abstractour study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery.
Reproduction assets foundThe paper's maize field phenotype datasets (2015, 2017, 2019, 2020 G2F hybrid experiments) are publicly available via G2F DOIs. The genotypic SNP dataset DOI was excluded as molecular omics data; image data are said to be in the supplement but without a public URL.Dataset · publicn Johnson, Seth Murray, Jacob Washburn, Filipe I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected imageOpen asset ↗10.25739/erxg-yn49lines:427-461Dataset · publice I. Matias, Annarita Marrano, and Felipe Sabadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are avaOpen asset ↗10.25739/w560-2114lines:427-461Dataset · publicadin for their help and suggestions on the image processing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this maOpen asset ↗10.25739/t651-yy97lines:427-461Dataset · publicprocessing pipeline and the research more broadly.
Data availability
This study used phenotypic data of hybrid maize ( Z. mays L.) field experiments part of the G2F program planted in 2015 ( https://doi.org/10.25739/erxg-yn49 ), 2017 ( https://doi.org/10.25739/w560-2114 ), 2019 ( https://doi.org/10.25739/t651-yy97 ), and 2020 ( https://doi.org/10.25739/hzzs-a865 ), named 2015_NYH2, 2017_NYH2, 2019_NYH2, and 2020_NYH2, respectively. The genotypic SNP marker data was also from the G2F program ( https://doi.org/10.25739/frmv-wj25 ). The collected image data from 2015, 2017, 2019, and 2020 are available in the Supplemental section of this manuscript.
Supplemental material available at GENEOpen asset ↗10.25739/hzzs-a865lines:427-461Code / 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 · 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 confirmedEurope PMC · checked 7 Sept 2026
This paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health. From a metrological perspective, it is crucial to evaluate the quality of vegetation indices, which are usually obtained by processing multispectral images for measuring vegetation, soil, and environmental parameters. For this reason, it is important to assess how the NVDI measurement is affected by the camera characteristics, light environmental conditions, as well as atmospheric and seasonal/weather conditions. The proposed study investigates the impact of atmospheric conditions on solar irradiation and vegetation reflection captured by a multispectral UAV camera in the red and near-infrared bands and the variation of the nominal wavelengths of the camera in these bands. Specifically, the study examines the influence of atmospheric conditions in three scenarios: dry-clear, humid-hazy, and a combination of both. Furthermore, this investigation takes into account solar irradiance variability and the signal-to-noise ratio (SNR) of the camera. Through Monte Carlo simulations, a sensitivity analysis is carried out against each of the above-mentioned uncertainty sources and their combination. The obtained results demonstrate that the main contributors to the NVDI uncertainty are the atmospheric conditions, the nominal wavelength tolerance of the camera, and the variability of the NDVI values within the considered leaf conditions (dry and fresh).
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物状態を示すNDVIを取得する測定ワークフローについて、不確かさ評価・感度分析を中心に扱っており、測定法の技術的検証が主題である。
abstractThis paper proposes a workflow to assess the uncertainty of the Normalized Difference Vegetation Index (NDVI), a critical index used in precision agriculture to determine plant health.
Reproduction assets foundThe paper's Monte Carlo NDVI uncertainty analysis relies on two public datasets: the ORNL Visible and Near-Infrared Leaf Reflectance Spectra (1992–1993), which provide the dry/fresh leaf reflectance inputs underlying the NDVI variability analysis, and the NASA GES DISC TSIS-1 Level 3 Solar Spectral Irradiance 24-Hour VDataset · public43. Richard E. TSIS SIM Level 3 Solar Spectral Irradiance 24-Hour Means V09. Goddard Earth Sciences Data and Information Services Center (GES DISC); Greenbelt, MD, USA: 2022. [(accessed on 23 April 2024)]. Available online: https://disc.gsfc.nasa.gov/datasets/TSIS_SSI_L3_24HR_12/summary .Open asset ↗GES DISC · TSIS SIM Level 3 Solar Spectral Irradiance 24-Hour Means V09lines:687-687Code / 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 confirmedOpenAlex · checked 14 Sept 2026
Abstract Breeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.
Why it matches plant phenotyping methodsUASによる大規模なスペクトル形質取得を用い、SRIの予測性能を年次交差検証しており、植物フェノタイピング手法の実質的な適用・評価が中心である。
abstractUnoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology
Reproduction assets foundThe paper's data availability statement explicitly deposits all code and data (including UAS-derived SRI/NDVI phenotyping data and genomic prediction analysis) in a public GitHub repository whose URL matches an allowed URL.Code · publicNational Institute of Food and Agriculture, Hatch project
1014919, and the O.A. Vogel Research Endowment at
Washington State University.
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
All code and data used in the study can be found
at https://github.com/AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-prediction.O RC I D
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Appels, R., Eversole, K., Stein, N., Feuillet, C., Keller, B., Rogers, J.,
Pozniak, C. J., Choulet, F., Distelfeld, A., Poland, J., Ronen, G.Open asset ↗AW-Herr/Large-scale-breeding-applications-of-UAS-enabled-genomic-predictionpdf-raw-page:10 lines:1-85Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Asian soybean rust (ASR) is one of the major diseases that causes serious yield loss worldwide, even up to 80%. Early and accurate detection of ASR is critical to reduce economic losses. Hyperspectral imaging, combined with deep learning, has already been proved as a powerful tool to detect crop diseases. However, current deep learning models are limited to extract both spatial and spectral features in hyperspectral images due to the use of fixed geometric structure of the convolutional kernels, leading to the fact that the detection accuracy of current models remains further improvement. In this study, we proposed a deformable convolution and dilated convolution neural network (DC 2 Net) for the ASR detection. The deformable convolution module was used to extract the spatial features, while the dilated convolution module was applied to extract features from the spectral dimension. We also adopted the Shapley value and the channel attention methods to evaluate the importance of each wavelength during decision-making, thereby identifying the most contributing ones. The proposed DC 2 Net can realize early asymptomatic detection of ASR even when visual symptoms have not appeared. The results of the experiment showed that the detection performance of DC 2 Net dominated state-of-the-art methods, reaching an overall accuracy at 96.73%. Meanwhile, the experimental result suggested that the Shapley Additive exPlanations method was able to extract feature wavelengths correctly, thereby helping DC 2 Net achieve reasonable performance with less input data. The research result of this study could provide early warning of ASR outbreak in advance, even at the asymptomatic period.
Why it matches plant phenotyping methodsハイパースペクトル画像から植物病害状態を抽出する深層学習手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractIn this study, we proposed a deformable convolution and dilated convolution neural network (DC 2 Net) for the ASR detection.
Reproduction assets foundThe paper's Data Availability statement provides an explicit public GitHub link to the authors' DC 2 Net analysis code. The hyperspectral dataset itself is only available upon request, so it is not a public asset.Code · publicThe code of our work can be found via the following link: https://github.com/NJAUJerry/DC2Net .Open asset ↗NJAUJerry/DC2Netlines:310-335Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k -nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.
Why it matches plant phenotyping methodsハイパースペクトル画像と機械学習により、ナタネの成熟状態を非破壊的に分類する取得・解析手法を開発し、前処理、波長選択、モデル性能を比較評価しているため、フェノタイピング手法が中心である。
abstracta nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms
Reproduction assets foundThe authors state that the primary script and dataset (spectral reflectance data and classification/feature-wavelength-extraction code) used in this rapeseed hyperspectral maturity classification study are publicly accessible via the provided link, which matches an allowed URL.Dataset · publicAll authors confirm that all raw experimental data are available upon request. The primary script and dataset used during the experimental procedure are accessible via the following link: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:421-450Code / dataset availability confirmedCrossref · OpenAlex · checked 7 Sept 2026
Abstract Optical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectance dataset. We propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval. A standard (nadir) and a multi-angular, 10-band multispectral dataset was collected for maize using a UAV on two different days. Reflectance data was grouped by VZA and VAA (on average 2594 spectra/plot/day for the multi-angular data and 890 spectra/plot/day for nadir flights only, 13 spectra/plot/day for a standard orthomosaic), serving as predictor variables for leaf chlorophyll content (LCC), leaf area index (LAI), green leaf area index (GLAI), and nitrogen balanced index (NBI) classification. Results consistently showed higher accuracy using grouped VZA/VAA reflectance compared to the standard orthomosaic data. Pooling all reflectance values across viewing directions did not yield satisfactory results. Performing multiple flights to obtain a multi-angular dataset did not improve performance over a multi-angular dataset obtained from a single nadir flight, highlighting its sufficiency. Our openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groups, benefiting cross-disciplinary and agriculture scientists in harnessing the potential of multi-angular datasets. Graphical abstract
Why it matches plant phenotyping methodsUAVマルチアングル反射データから植物形質を抽出・分類する方法を提案し、標準オルソモザイクと精度比較しているため、表現型取得手法が中心である。
abstractWe propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval.
Reproduction assets foundThe authors explicitly share their custom Python workflow for extracting multi-angular VZA/VAA reflectance data and reproducing the maize trait classification analysis via a public GitHub repository, referenced multiple times in the article.Code · publicOur openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groupsOpen asset ↗ReneHeim/proj_on_uavlines:1-64Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.
Why it matches plant phenotyping methodsトウモロコシのさび病抵抗性を対象に、UAVマルチスペクトル・熱赤外データから植物状態を評価し、目視評価との比較や再利用可能なデータセットとして提示しており、表現型取得法が中心的です。
abstractFor phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS).
Reproduction assets foundThe paper deposits its own phenotype data (visual scores, UAV multispectral/thermal remote-sensing vegetation indices) and imputed SNP genotypes for three maize DH populations in the CIMMYT Research Data & Software Repository Network, with a direct public URL (hdl:11529/10548898). This is a paper-specific, publicly resDataset · publicData accessibility
Repository name: CIMMYT Research Data & Software Repository Network [2]
Data identification number: 10548898
Direct URL to data:Open asset ↗CIMMYT Research Data & Software Repository Networklines:1-57Dataset · public016/j.fcr.2024.109281.
2. Loladze A., Rodrigues F., Petroli C., Muñoz C., Macia Naranjo S., San Vicente F., Gerard B., Montesinos-López O.A., Crossa J., Martini J. CIMMYT Research Data & Software Repository Network, V1. 2023. Replication data for: use of remote sensing for genome-wide association studies and genomic prediction. https://hdl.handle.net/11529/10548898
Associated Data
Supplementary Materials
Image, application 1
Data Availability Statement
Replication Data for: Use of Remote Sensing for Genome-Wide Association Studies and Genomic Prediction (Original data) (Dataverse) [2] .Open asset ↗CIMMYT Research Data & Software Repository Networklines:219-242Code / 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
Field / plotLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldLeaf traitsPigment / colour / senescenceWater status / transpiration
Mediterranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. Optical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. However, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. Thus, the objective of the SENTHYMED/MEDOAK experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi-platform sensors having different spectral characteristics and spatial resolutions. Seven monthly data collections were performed between April and October 2021 (with a complementary one in June 2023) over two forests in the north of Montpellier, France, comprised of two oak endemic species with different phenological dynamics (evergreen: Quercus ilex and deciduous: Quercus pubescens ) and a variability of canopy cover fractions (from dense to open canopy). These collections were coincident with satellite multispectral Sentinel-2 data and one with airborne hyperspectral AVIRIS-Next Generation data. In addition, satellite hyperspectral PRISMA and DESIS were also available for some dates. All these airborne and satellite data are provided from free online download websites. Eight datasets are presented in this paper from thirteen studied forest plots: (1) overstory and understory inventory, (2) 687 canopy plant area index from Li-COR plant canopy analyzers, (3) 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from ASD spectroradiometers, (4) 92 soil moistures and temperatures from IMKO and Campbell probes, (5) 747 leaf-clip optical data from SPAD and DUALEX sensors, (6) 2594 in-lab leaf directional-hemispherical reflectances and transmittances from ASD spectroradiometer coupled with an integrating sphere, (7) 747 in-lab measured leaf water and dry matter content, and additional leaf traits by inversion of the PROSPECT model and (8) UAV-borne LiDAR 3-D point clouds. These datasets can be useful for multi-scale and multi-temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. Other targeted applications can be forest 3-D modelling, biodiversity assessment, fire risk prevention and globally vegetation monitoring.
Why it matches plant phenotyping methods森林の葉からキャノピーまでの植物形質データとマルチプラットフォーム光学・LiDARデータを体系的に整備し、衛星植生形質プロダクトの較正・検証に用いるデータセット研究であり、形質取得と再利用可能な検証基盤が中心です。
titleMulti-scale datasets for monitoring Mediterranean oak forests from optical remote sensing during the SENTHYMED/MEDOAK experiment in the north of Montpellier (France).
Reproduction assets foundThis Data in Brief article deposits the paper's own SENTHYMED/MEDOAK plant-phenotyping measurements (forest inventory, canopy plant area index, forest/leaf optical properties, soil moisture, leaf-clip sensor data, leaf traits, UAV-borne LiDAR point clouds) in the public SEDOO repository with explicit DOIs and a direct,Dataset · publicat https://eoweb.dlr.de/egp/ (image rasters, .tif for GeoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
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Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UOpen asset ↗SEDOO · 10.6096/8005lines:31-86Dataset · publicoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
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Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.154Open asset ↗SEDOO · 10.6096/8007lines:31-86Dataset · publicTHEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to aOpen asset ↗SEDOO · 10.6096/8006lines:31-86Dataset · publicoduct/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/Open asset ↗SEDOO · 10.6096/8001lines:31-86Dataset · publiceoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the daOpen asset ↗SEDOO · 10.6096/8002lines:31-86Dataset · public//doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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Value of the Data
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These datasets were collected to provide calibratioOpen asset ↗SEDOO · 10.15454/AGBW7Glines:31-86Dataset · publictical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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Value of the Data
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These datasets were collected to provide calibration/validation data for methods aimiOpen asset ↗SEDOO · 10.15454/DMYWPBlines:31-86Dataset · publicoisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
1
Value of the Data
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These datasets were collected to provide calibration/validation data for methods aiming at linking ground observations on Mediterranean forests withOpen asset ↗SEDOOlines:31-86Code / dataset availability confirmedEurope PMC · 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
This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.
Why it matches plant phenotyping methodsトマト葉の病害症状と重症度を、画像・疑似ハイパースペクトル・深層学習で直接推定する方法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractthis study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity
Reproduction assets foundThe paper uses two publicly available tomato leaf disease image datasets (Kaggle tomatoleaf; Google Drive dataset) as phenotyping inputs and provides the authors' analysis code (RGB-to-SHSI conversion, VGG-16 feature extraction, GMM/K-means severity pipeline) via a public Colab notebook listed in the Data Availability.Dataset · publicards in the field. In summary,
the compilation of our diverse dataset and the incorporation of benchmark datasets form the
foundation of this research endeavor, ensuring a thorough and principled evaluation of our proposed
approaches in the context of plant disease assessment [8].
2.1.1. Dataset 1:
This data was collected from "https://www.kaggle.com/datasets/kaus-tubhb999/tomatoleaf:
Access Date: 2023-10-25." This dataset includes diseases for tomato leaves such as "Septoria leaf spot,
tomato healthy, Spider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photosOpen asset ↗kaggle · kaus-tubhb999/tomatoleafpdf-raw-page:7 lines:1-31Dataset · publicider mites Two-spotted spider mite, Early blight, Leaf Mold, Late blight, Tomato
Yellow Leaf Curl Virus, Bacterial spot, Target Spot, and Tomato mosaic virus." This collection has 984
photos in total.
2.1.2. Dataset 2:
Dataset-2 is also a publicly available one which can be downloaded and utilized from the drive
link provided. "https://drive.google.com/file/d/1DVy0LyUUfJciyo7BUFm1sHKSRdTVJgjF/view:
Access Date: 2023-10-25." This dataset is divided into seven classes: yellow curving, tomato mosaic,
Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 29 January 2024 doi:10.20944/preprints202401.1973.v1Open asset ↗pdf-raw-page:7 lines:1-31Code · public.B.; writing— S.K., M.M., B.B., Y.S., and A.B.; writing—review
and editing, M.M, B.B.; supervision, S.K., M.M., B.B. All authors have read and agreed to the published version
of the manuscript.
Funding: This research was partly funded by Zayed University, grant number 12091.
Data Availability Statement: Our code is available at
https://colab.research.google.com/drive/1wMvqsuZNY_lB2INmyWWqSZYm87wVckv0?usp=sharing
Acknowledgments: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the resultsOpen asset ↗pdf-raw-page:19 lines:1-52Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.
Why it matches plant phenotyping methodsドローン・衛星リモートセンシングとNDVI等を用いて除草剤効果を評価し、その手法を有効な評価ツールとして検証しているため、植物状態の取得・評価方法が中心である。
abstractTherefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia.
Reproduction assets foundThe article states 'Data are contained within the article' and provides no author code, model, or dataset deposit. The only paper-specific public asset is the MDPI supplementary file, which contains Figure S1 showing the control subplots affected by Echinochloa spp. (field imagery related to the phenotyping experiment,Supplement · 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/s24030804/s1 . Figure S1. Control subplots affected by Echinochloa spp. (Own elaboration).
Click here for additional data file.
Author ContributionsOpen asset ↗lines:90-101Code / 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
•
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
•
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
•
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 14 Sept 2026
Introduction The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce. Methods We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively. Results and discussion Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R 2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いてレタスの霜害指数という植物状態を推定する高スループット手法を開発・評価しており、フェノタイピング手法が中心です。
abstractWe proposed a high-throughput method to estimate lettuce FDI based on remote sensing.
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 · publicData, models, or codes generated or used in the course of the study are available on GitHub at https://github.com/kwcnmm/predict-FDI .Open asset ↗kwcnmm/predict-FDIlines:908-915Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published1 Jan 2024IEEE Transactions on Geoscience and Remote SensingCited by 1 · OpenAlex ↗
Full-spectrum Sun-induced chlorophyll fluorescence (SIF) offers profound physiological insights into plant functional status compared to single-band SIF. We propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF, aiming to address the limitations of existing methods, such as reliance on reflectance training datasets and the limited spectral range of retrieved SIF spectrum. The core principle of the FSM involves modeling reflectance as a wavelength-dependent function, which can be approximated by successive summations using high-order expansions of the Fourier series. The performance of the FSM was thoroughly evaluated through a combination of simulations and field measurements. The findings illustrate FSM’s capability to achieve high-precision full-spectrum SIF retrieval, with an average relative root-mean-square error (RRMSE) of 2.468% based on synthetic data. Moreover, the corresponding RRMSE values in the O2-A and O2-B bands, at 1.1% and 3.724%, respectively, indicate accuracy comparable to the spectral fitting method (SFM) and advanced FSR (aFSR) methods and superior to the SpecFit method. In the field full-spectrum SIF retrieval, FSM exhibited improved reflectance reconstruction and produced more reasonable results for the diurnal variation of full-spectrum SIF. The diurnal comparison of single-band SIF at both Italian and German sites further highlights the close alignment between FSM-retrieved SIF and the SFM SIF, with$R^{2}$values exceeding 0.96 and a maximum RMSE of 0.118 mW/m2/sr/nm. Conversely, the aFSR method encountered challenges stemming from an under-representation of the training dataset, resulting in the maximum RMSE at the Italian site reaching 0.506 mW/m2/sr/nm, along with a minimum$R^{2}$of 0.809. The FSM demonstrates the promising potential for full-spectrum SIF retrieval, accompanied by fewer limitations.
Why it matches plant phenotyping methods植物キャノピー計測から葉緑素蛍光を抽出する新規手法を開発し、シミュレーションと圃場計測で精度検証・既存法比較を行っており、植物生理状態のフェノタイピング手法が中心である。
abstractWe propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF
Reproduction assets foundThe paper's FloX field spectral measurements (canopy upwelling radiance and apparent reflectance from Grosseto, Italy and Julich, Germany) are explicitly stated to be publicly available on Zenodo, matching the allowed URL. No author analysis code or trained model deposit is mentioned.Dataset · publicp (sparse vegetation), with observation heights of 1.5
121
m and 3 m, respectively. For this study, we used six sets of clear-sky observations
122
conducted on April 7, 16, and 25, 2018, in Italy, and on November 5, 7, and 18, 2020,
123
in Germany. These spectral datasets are already available on the shared online platform
124
(https://zenodo.org/records/7040578). For a more detailed description, please refer to
125
the work by Naethe, Julitta [17].
126
Figure 1 illustrates the diurnal measurements of upwelling radiance and apparent
127
reflectance recorded over the course of six days. The Italian measurements (upper two
128
rows) depict the vigorous growth period of the target vegetatOpen asset ↗zenodo · 7040578pdf-raw-page:6 lines:1-53Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と抽出可能なデジタル形質を含む、育種利用可能な植物表現型データセットとして構築・公開されているため。
abstractThis dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials.
Reproduction assets foundThis Data in Brief article describes its own pulse crop phenotyping dataset (agronomic trait tables and 275 UAV multispectral orthomosaic images), publicly deposited on Zenodo with an explicit DOI listed in the Specification Table under Data accessibility. This is a paper-specific, public, directly actionable dataset.Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.8280431 .Open asset ↗Zenodo · 10.5281/zenodo.8280431lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.
Why it matches plant phenotyping methodsドローン画像とAirMeasurerを用いた作物形態・スペクトル・テクスチャ形質の取得、および曲線フィッティングによる動的表現型抽出が研究の中心であり、実質的な植物フェノタイピング手法の応用・解析である。
abstractPowered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties.
Reproduction assets foundThe authors explicitly deposit their phenotyping analysis code, testing aerial images, trait analysis outputs, and Jupyter notebooks in a public GitHub repository, and separately release the AirMeasurer phenotyping platform used for the drone-based trait analysis. Both are paper-specific, public, and actionable via theCode · publicmade available in this paper. The source code, testing data, and other datasets supporting the results presented in this article are available at https://Github.com/The-Zhou-Lab/Nitrogen-response-traits/releases . Other data and user guides are openly available upon request. The latest AirMeasurer platform can be downloaded via https://github.com/The-Zhou-Lab/UAV/releases ).
Supplementary Materials
Supplementary 1
Figs. S1 to S6
Tables S1 to S14
Notes S1 to S3
Supplementary 2
Data S1 to S9
References
1. Seppelt R, Klotz S, Peiter E, Volk M.
Agriculture and food security under a changing climate: An underestimated challenge. iScience. 2022;25(12):105551.
2. Li S, Tian Y, Wu K, Ye Y, Yu J, ZhaOpen asset ↗The-Zhou-Lab/UAVlines:143-192Code / dataset availability confirmedCrossref · bioRxiv · checked 14 Sept 2026
Abstract Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their high temporal dynamism and variety. It follows that a thorough investigation of the key factors, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), influencing such a relationship is necessary. Thus, this study aims to assess the applicability of SD for plant diversity monitoring at the local scale by testing six different SD metrics while considering the effect of the presence of flowering on the relationship and resampling the original data to assess how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots with 1.5 m ×1.5 m experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at ∼2 cm spatial resolution. Our results show that, in the presence of flowering, the relationship is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models had very low values, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD∼plant diversity in grassland communities are needed to use SD for monitoring purposes.
Why it matches plant phenotyping methodsUAV搭載センサーによるスペクトル情報から植物多様性を推定し、複数のスペクトル多様性指標と空間解像度を比較検証しているため、植物フェノタイピング手法の応用・評価が中心です。
abstractThus, this study aims to assess the applicability of SD for plant diversity monitoring at the local scale by testing six different SD metrics
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 · publicData and scripts are provided at https://github.com/MichelaPerrone/SVH_Benesov.git
under CC-BY license.Open asset ↗MichelaPerrone/SVH_Benesovpdf-page:7 lines:1-43Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Salt stress is considered one of the primary threats to cotton production. Although cotton is found to have reasonable salt tolerance, it is sensitive to salt stress during the seedling stage. This research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning. A prototyping platform that can obtain multicolor fluorescence and multispectral reflectance images synchronously was developed to get different characteristics of each cotton seedling. The experiments revealed that salt stress harmed cotton seedlings with an increase in malondialdehyde and a decrease in chlorophyll content, superoxide dismutase, and catalase after 17 days of salt stress. The Relief algorithm and principal component analysis were introduced to reduce data dimension with the first 9 principal component images (PC1 to PC9) accounting for 95.2% of the original variations. An optimized EfficientNet-B2 (EfficientNet-OB2), purposely used for a fixed resource budget, was established to detect salt stress by optimizing a proportional number of convolution kernels assigned to the first convolution according to the corresponding contributions of PC1 to PC9 images. EfficientNet-OB2 achieved an accuracy of 84.80%, 91.18%, and 95.10% for 5, 10, and 17 days of salt stress, respectively, which outperformed EfficientNet-B2 and EfficientNet-OB4 with higher training speed and fewer parameters. The results demonstrate the potential of combining multicolor fluorescence-multispectral reflectance imaging with the deep learning model EfficientNet-OB2 for salt stress detection of cotton at the seedling stage, which can be further deployed in mobile platforms for high-throughput screening in the field.
Why it matches plant phenotyping methods綿実生の塩ストレス状態を画像から検出する撮像プラットフォームと深層学習手法を開発しており、植物状態の取得・抽出が研究の中心である。
abstractThis research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' EfficientNet-OB2 code and training script on GitHub at the allowed URL. No public phenotype/image dataset is stated.Code · publication and technical support for the project. H.W., B.Z., and D.Y. provided suggestions on the experiment design and discussion sections.
Competing interests: The authors declare that they have no competing interests.
Data Availability
The code and training script of EfficientNet-OB2 has been hosted to GitHub and is available at https://github.com/foddcus/EfficientNetOB .
Supplementary Materials
Supplementary 1
Figs. S1 and S2
Tables S1 and S2
Click here for additional data file.
References
1. Noreen S, Ahmad S, Fatima Z, Zakir I, Iqbal P, Nahar K, Hasanuzzaman M. Abiotic stresses mediated changes in morphophysiology of cotton plant. In: Ahmad S, Hasanuzzaman M, editors. Cotton production andOpen asset ↗foddcus/EfficientNetOBlines:363-403Code / 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 confirmedCrossref · checked 7 Sept 2026
The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path.
Why it matches plant phenotyping methodsハイパースペクトル由来の2種類の正規化差分指標を組み合わせ、作物の成長・老化過程や生育段階を時系列で推定する手法が研究の中心であるため。
abstractBy using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only.
Reproduction assets foundThe paper's analysis is based on the publicly available GHISA EO-1 Hyperion hyperspectral dataset from USGS, explicitly named in the Data Availability Statement. No author analysis code or trained models are deposited.Dataset · publicData Availability Statement: The data are publicly available at https://www.usgs.gov/media/files/
ghisa-usa-eo-1-hyperion-dataset (accessed on 14 November 2023).Open asset ↗ghisa-usa-eo-1-hyperion-datasetpdf-page:13 lines:1-56Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains.
Why it matches plant phenotyping methodsドローンのDEM・マルチスペクトル画像から作物高を推定する手法を開発し、従来法と精度比較・検証しており、植物表現型取得が中心である。
abstractwe introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height.
Reproduction assets foundThe paper's crop-height phenotyping analysis is built on a public cotton UAV multispectral/DEM dataset deposited by Xu et al. on Figshare, which qualifies as a paper-specific, publicly actionable phenotyping input. The authors' own G-DMD code and processed data are only available upon request, so that component is not公Dataset · public47. Xu, R.; Li, C.; Paterson, A.H. UAV Multispectral. Figshare. Dataset. 2018. Available online: https://figshare.com/articles/Open asset ↗Figsharepdf-page:22 lines:1-20Code / 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 7 Sept 2026
The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R 2 ) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R 2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.
Why it matches plant phenotyping methodsUAV画像から個体特徴を自動抽出し、収穫重量という植物形質を予測する手法が研究の中心であるため。
abstractwe propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub with explicit availability language. The UAV imagery (RGB/multispectral orthomosaics and point cloud data) is only available upon reasonable request from the corresponding author, so it does not qualify as a public asset.Code · publicAll code associated with the current study is available at: https://github.com/anaguilarar/CC_Weight_Prediction .Open asset ↗anaguilarar/CC_Weight_Predictionlines:155-233Code / dataset availability confirmedCrossref · checked 14 Sept 2026
A sound understanding of plant growth is critical to maintaining future crop productivity under ongoing climate change. Remotely sensed time series of crop functional traits from optical satellite imagery are an invaluable tool for deriving appropriate management practices that facilitate risk mitigation and increase the resilience of agroecosystems. However, the availability of imagery is limited by atmospheric disturbances that cause large temporal gaps and noise in the trait time series. Therefore, time series reconstruction methods are required for accurate crop growth modelling. Physiological priors, such as the fact that plant growth is mainly controlled by a few environmental covariates, among which air temperature plays a prominent role, represent a promising approach to improve the representation of crop growth. Here, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat. A probabilistic ensemble Kalman filtering data assimilation scheme allows the combination of high temporal resolution air temperature data and satellite imagery, which also allows quantification of uncertainties. The proposed approach requires a smaller number of satellite observations compared to conventional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover, and is considerably less complex than a mechanistic crop growth model. Validation was carried out using in-situ data collected on winter wheat plots in Switzerland in two consecutive years. The validation results suggest that the proposed assimilation of Sentinel-2 GLAI and temperature-response-based growth rates allows the reconstruction of physiologically meaningful GLAI time series. In particular, the systematic underestimation of high in-situ GLAI values (> 5 m^2 m^-2) often prevalent in purely remote sensing driven GLAI time series reconstruction was reduced. Thus, the proposed approach is advantageous compared to state-of-the-art remote sensing approach based on wide-spread logistic functions by means of physiological plausibility, fitting requirements and representation of high in-situ GLAI values. This has great potential to increase the reliability of remotely sensed crop productivity assessment.
Why it matches plant phenotyping methods衛星光学データと生理モデルを統合して作物GLAI時系列を再構築する手法を提案し、圃場データで検証しており、植物形質推定が研究の中心である。
abstractHere, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat.
Reproduction assets foundThe authors explicitly state that code and data to reproduce the entire workflow (DRC fitting, Sentinel-2 GLAI assimilation, and validation) are publicly available on GitHub under GNU GPL v3.0. This is a paper-specific, public, actionable asset. Other URLs in the text are cited references or generic libraries (e.g., NLCode · publicCode and Data Availability
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available at https://github.com/EOA-team/sentinel2_crop_trait_timeseries
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under GNU General Public License v3.0.
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Credit Authorship Contribution Statement
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Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
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dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
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Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.
Why it matches plant phenotyping methods植物病害という植物状態をマルチスペクトル画像とCNN/ViTで推定する手法が研究の中心であり、データ収集、波長比較、モデル性能評価を含むため収録対象。
abstractA multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths.
Reproduction assets foundThe paper's Data Availability Statement and conclusions provide public Google Drive links to the authors' balanced and unbalanced multispectral plant disease image datasets used in this study. No code or model checkpoints are shared.Dataset · publicThe data that support the findings of this study are available in Unbalance multispectral disease dataset ( https://drive.google.com/drive/folders/1Ck9CKfru4SY9xknDSrWHqQM9EtXcjP_l?usp=drive_link , accessed on 15 October 2023.)Open asset ↗lines:122-339Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.
Why it matches plant phenotyping methods植物の栄養状態をハイパースペクトル画像から抽出するCNN手法を開発し、前処理・複数モデルとの比較・異なる生育段階での性能評価を行っており、表現型取得が中心である。
abstractThis study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages.
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 is the description of the selected growth stages based on the BBCH system for coding the phenological growth stages of plants ( Meier et al.Open asset ↗lines:339-346Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation—information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata : wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400–2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて植物の遺伝的変異を評価する測定法を、異なる環境・測定条件で検証・評価しており、植物フェノタイピング手法が中心です。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurements and analysis code are publicly available: processed spectral data, metadata, and code are on the authors' GitHub repository, and the raw spectral measurement dataset is published in SPECCHIO.Code · publicAll processed spectral data, metadata and code are provided at the GitHub repository: https://github.com/licheng1221/How-leaves-reflect-genetic-variation .Open asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationlines:218-235Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice ( Oryza sativa ). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
Why it matches plant phenotyping methods自動ハイパースペクトル画像からイネの生理形質を推定するモデルを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88 ) and carbon to nitrogen ratio (C:N, n=88 ) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository containing the code for the physiological trait prediction models (RReliefF-PLSR, SVM classification of rice hyperspectral data), and a Purdue PURR repository deposit containing the study's datasets (HSI-derived and ground-reference phenCode · publicThe code for each physiological trait prediction model can be accessed through GitHub ( https://github.com/To-Chia/rice_imaging_ms ).Open asset ↗https://github.com/To-Chia/rice_imaging_mslines:384-394Dataset · 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://purr.purdue.edu/publications/4079/ .Open asset ↗https://purr.purdue.edu/publications/4079/lines:442-452Code / dataset availability confirmedCrossref · checked 14 Sept 2026
As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing “seed loss and waste” due to slight shortfalls in eligibility rates. In this study, we applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars. Candidate discriminants for quality evaluation were derived by linear sparse modeling using the seed reflectance spectra as explanatory variables. To systematically proceed with model selection, we defined the sorting condition where the recovery rate of seeds matches the initial eligibility rate ( iP ) as “standard condition”. How much the eligibility rate after sorting ( P ) increases from iP under this condition offers a reasonable criterion for ranking candidate models. Moreover, the model performance under conditions with adjusted discrimination strength was verified using a metric “relative precision” ( rP ) defined as ( P–iP )/(1 –iP ). Because rP , compared to precision (= P ), is less dependent on iP in relation to recall ( R ), i.e., recovery rate of eligible seeds, the rP-R curve and area under the curve also offer useful criteria for spotting better discriminant models. We confirmed that the batches of seeds given higher discriminant scores by the models selected with reference to these criteria were more enriched with eligible seeds. The method presented can be readily implemented in developing a sorting device that enables “last-percent improvement” in eligibility rates of crop seeds.
Why it matches plant phenotyping methods近赤外イメージング分光と機械学習を用いて種子の発芽能力などの品質形質を評価・選別する手法が研究の中心であり、モデル選択基準と性能評価も提示している。
abstractwe applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe software for directly visualizing discriminant scores of seeds within hyperspectral images is provided as S2 FileOpen asset ↗lines:157-171Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.
Why it matches plant phenotyping methods複数の地上・UAS型HTPセンサープラットフォームを比較評価し、スペクトル形質の相関・遺伝率・標準化を検証しており、フェノタイピング手法が研究の中心です。
abstractwe evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor.
Reproduction assets foundThe article's data availability statement points to a public repository deposit (DOI 10.7273/000004802) containing the study's HTP spectral reflectance and grain yield datasets. The WSU weather station URL is a generic external resource, not a paper-specific asset.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://doi.org/10.7273/000004802 .Open asset ↗10.7273/000004802lines:481-522Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
Why it matches plant phenotyping methodsリンゴ果実の欠陥を画像から画素単位で抽出する深層学習手法を開発・評価しており、植物器官の状態（欠陥）取得が中心的な方法論的貢献である。
abstractwe propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs)
Reproduction assets foundThe paper's authors publicly release the analysis code for their apple defect segmentation experiments via a GitHub repository, explicitly stated in the text. The apple image dataset itself is cited prior work (Kleynen et al.) and no separate dataset deposit by these authors is stated.Code · publicwe investigate the feasibility of using RGB images exclusively as input data instead of multi-spectral images. Encouragingly, the results show that the accuracy achieved in this scenario is nearly comparable to the multi-spectral approach. The experiments can be reproduced using the code made available at the following address: https://github.com/cimice15/Quasi_real-time_apple_defect_segmentation (accessed on 8 September 2023).
The paper is organized as follows: Section 2 presents related works, Section 3 presents the database used in our experiments and the method we propose. Section 4 presents evaluation metrics and experimental setups. Finally Section 5 discusses results of the proposed mOpen asset ↗cimice15/Quasi_real-time_apple_defect_segmentationlines:40-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation.
Why it matches plant phenotyping methodsGEDI LiDAR由来の樹冠高不均一性という植物群落形質を推定し、複数のCHM、解像度、指標、森林プロットで検証しており、測定・推定手法が研究の中心です。
abstractWe tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis.
Reproduction assets foundThe paper's phenotyping analysis relies on two freely available GEDI-derived canopy height models (Lang10m and Potapov30m) and local ALS LiDAR data from the Province of Bolzano/Bozen, all with explicit public download URLs matching allowed_urls. No author analysis code or trained models are deposited; the data-availaDataset · publictual species
p i = ratio between the number of individuals for a defined species i and the total number of individuals within each plot.
2.3.
LiDAR data
2.3.1.
GEDI LiDAR data
We estimated the HH using the recently published and freely available LiDAR GEDI CHMs Lang10m ( Lang et al., 2022 , Lang et al., 2022 ) (downloaded here: https://langnico.github.io/globalcanopyheight/ ) and Potapov30m ( Potapov et al., 2021 ) (downloaded here: https://glad.umd.edu/dataset/gedi/ ).
Lang10m was derived fusing the GEDI and Sentinel-2 images through a deep convolutional neural network ( Lang et al., 2022 ). It has spatial resolution of 10 m and is valid for the year 2020. The canopy top height was defined Open asset ↗globalcanopyheight · Lang10mlines:48-67Dataset · publicindividuals within each plot.
2.3.
LiDAR data
2.3.1.
GEDI LiDAR data
We estimated the HH using the recently published and freely available LiDAR GEDI CHMs Lang10m ( Lang et al., 2022 , Lang et al., 2022 ) (downloaded here: https://langnico.github.io/globalcanopyheight/ ) and Potapov30m ( Potapov et al., 2021 ) (downloaded here: https://glad.umd.edu/dataset/gedi/ ).
Lang10m was derived fusing the GEDI and Sentinel-2 images through a deep convolutional neural network ( Lang et al., 2022 ). It has spatial resolution of 10 m and is valid for the year 2020. The canopy top height was defined as the relative height at which 98% of the energy was returned (RH98). For the modelling GEDI observaOpen asset ↗Potapov30mlines:48-67Dataset · public= −4.8, RMSE = 9.6 m; MAE = 7.4 m).
2.3.2.
Local ALS LiDAR data
In order to validate the GEDI CHMs and to calculate the canopy cover we used local Airborne Laser Scanning (ALS) LiDAR data. For the Italian study area, we derived the CHM from an ALS campaign completed in 2006 by the Province of Bolzano/Bozen (free available here: http://geocatalogo.retecivica.bz.it/geokatalog/ ). For the German study area were used the LiDAR data derived from an ALS campaign carried out in 2010 (for the assessment of the DTM) and 2018 (for the assesment of DSM). For both study sites, the CHMs, calcuated as the difference between the DSM (derived from the point cloud using the R packege “lidR” through the functOpen asset ↗lines:68-110Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation – information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて遺伝的変異を評価する分光計測法を、複数環境・遺伝子型で検証し、測定不確実性や適用上の考慮点も評価しており、植物表現型取得法が研究の中心である。
titleEvaluating potential of leaf reflectance spectra to monitor plant genetic variation
Reproduction assets foundThe paper's leaf reflectance spectral measurement data are published in SPECCHIO, and all processed spectral data, metadata, and analysis code (e.g., RawDataProcess.R, Plots PCA.R, Plots Models.R) are provided in the authors' public GitHub repository.Code · publicilability of data and code
All plant lines are available from the Max Planck Institute for Chemical Ecology. The spectral measurement data
underlying the results presented in this paper are available as a published dataset [71]. All processed spectral data,
metadata and code for this study are provided at the GitHub repository:
https://github.com/licheng1221/How-leaves-reflect-genetic-variationOpen asset ↗https://github.com/licheng1221/How-leaves-reflect-genetic-variationpdf-raw-page:32 lines:1-45Code / 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
745
Ethics approval and consent to participate
746
Not applicable.
747
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Consent for publication
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Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
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Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. wOpen asset ↗zenodo · 10.5281/zenodo.7807989pdf-raw-page:30 lines:1-62Dataset · publicl
744
Declarations
745
Ethics approval and consent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
7Open asset ↗zenodo · 10.5281/zenodo.8164473pdf-raw-page:30 lines:1-62Dataset · publiconsent to participate
746
Not applicable.
747
748
Consent for publication
749
Not applicable.
750
751
Availability of data and materials
752
The datasets generated and analyzed during the current study are available in the zenodo repository
753
(https://doi.org/10.5281/zenodo.7807989, https://doi.org/10.5281/zenodo.8164473,
754
https://doi.org/10.5281/zenodo.8033640)
755
756
Competing interests
757
The authors declare that this study received funding from BASF. The funder had the following
758
involvement in the study: collaboratively conceived the original screening and research plans. J.V.,
759
and W.B. were employed by BASF Corporation, USA.
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Funding
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This work was supported bOpen asset ↗zenodo · 10.5281/zenodo.8033640pdf-raw-page:30 lines:1-62Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Aims Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles. Methods The transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles. Results The technique was able to predict pH live and in situ within ± 0.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10 μm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties. Conclusion Smart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes.
Why it matches plant phenotyping methods植物根圏のpHを生体根周辺で測定・3D再構成するセンサー基盤を開発し、精度検証まで行っており、植物状態の取得方法が研究の中心である。
abstractwe have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles.
Reproduction assets foundThe paper's Data availability statement explicitly releases the authors' software for predicting pH from light-sheet image data (the machine-learning phenotyping analysis) on the authors' public GitHub repository SENSOIL. No separate phenotype/trait dataset or image deposit is stated; supplementary material is only a 'Code · public102 Plant Soil (2024) 500:91–104
1 3
Vol:. (1234567890)
Data availability Software developped for predicting pH
from image data is available at https://github.com/LionelDu-puy/SENSOIL/tree/main/pH_Release.Declarations
Competing interest There is no competing interest.
Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License, which per-
mits use, sharing, adaptation, distribution and reproduction in
any medium or format, as long as you give appropriate credit
to the original author(s) and the source,Open asset ↗SENSOILpdf-raw-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a 'spectral-spatial' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective 'spectral-spatial' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.
Why it matches plant phenotyping methods果実の病害・損傷・低温障害などの状態をハイパースペクトル画像から検出する手法を開発・評価しており、植物器官の状態推定が研究の中心です。
abstractIn this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed.
Reproduction assets foundThe paper's SSAD code implementation and learned models are publicly available on GitHub. The fruit hyperspectral datasets are paper-specific but only available on request from the corresponding author.Code · publicThe code implementation and learned models of SSAD are available at https://github.com/YisenLiu-Intelligent-Sensing/SSAD accessed on 18 May 2022.Open asset ↗YisenLiu-Intelligent-Sensing/SSADlines:57-72Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.
Why it matches plant phenotyping methodsカッサバ病害の症状・病態を対象に、スペクトルと画像を収集した再利用可能なデータセットを構築し、収集プロトコル、専門家による病害スコア、地上真値を記述しているため、植物表現型取得が中心である。
abstractwe present a novel dataset composed of spectral data and images of cassava crops with and without diseases.
Reproduction assets foundThe paper is a Data in Brief article describing a publicly deposited cassava spectral/leaf-image dataset with biochemical and expert-score labels, hosted on Harvard Dataverse with an explicit DOI and direct URL. This is the paper's own phenotyping data (spectra, leaf images, RT-PCR ground truth, expert scores), so it'sDataset · publicected for 19 and 15 consecutive weeks respectively.
Data source location
The dataset is in two major groups: screenhouse and open field experiment, collected for 19 and 15 consecutive weeks respectively.
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/R0KL7R
Direct URL to data:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/R0KL7R
Related research article
Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, and Ernest Mwebaze. 2020. Early detection of plant diseases using spectral data. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems (APPIS 2020). AssociatioOpen asset ↗Harvard Dataverse · doi:10.7910/DVN/R0KL7Rlines:1-54Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.
Why it matches plant phenotyping methodsRGB・LiDAR・VNIRリモートセンシングによる作物形質抽出について、行選択とプロットトリミングを反復性・収量予測で評価しており、フェノタイピング手法の技術評価が中心である。
abstractThe objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data.
Reproduction assets foundThe paper states that remote sensing data, yield data, and the authors' R analysis code are publicly deposited in the Purdue University Research Repository under DOI 10.4231/PF9S-4G38. This is a paper-specific, publicly actionable asset covering both the phenotyping measurements (RGB/LiDAR/VNIR remote sensing traits, 4Dataset · publicRemote sensing data, yield data, and R code used for this study are available at the Purdue University Research Repository (10.4231/PF9S-4G38).Open asset ↗Purdue University Research Repository · 10.4231/PF9S-4G38lines:404-413Code / dataset availability confirmedCrossref · checked 8 Sept 2026
AppleArabidopsisLaboratory / benchtopMultispectral / hyperspectralLeafTissueClassificationObject detectionWater status / transpiration
Abstract Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana , and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Why it matches plant phenotyping methods植物組織培養におけるハイパーヒドリシティという植物状態を、分光計測と機械学習で自動検出・分類する手法を開発し、別のハイパースペクトル画像取得で妥当性検証しているため。
abstractThe objective of this study was to characterize optical features to allow an automated detection of HH.
Reproduction assets foundThe paper's RGB image dataset of hyperhydric in vitro explants (used for CNN-based HH detection) is publicly available on Roboflow, explicitly stated in the Data availability section and cited as Bethge (2023). Spectral datasets and trained spectral classifier are only available on request.Dataset · publicRGB image dataset analysed during the current study available in the Bethge ( 2023 ) repository, [ https://universe.roboflow.com/hains/hh-detection-in-vitro/dataset/8 ].Open asset ↗Roboflow · hh-detection-in-vitrolines:203-234Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Field / plotMultispectral / hyperspectralCalibration / preprocessing
Active radiometric reflectance is useful to determine plant characteristics in field conditions. However, the physics of silicone diode-based sensing are temperature sensitive, where a change in temperature affects photoconductive resistance. High-throughput plant phenotyping (HTPP) is a modern approach using sensors often mounted to proximal based platforms for spatiotemporal measurements of field grown plants. Yet HTPP systems and their sensors are subject to the temperature extremes where plants are grown, and this may affect overall performance and accuracy. The purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers. Sensor performance was measured at 1.2 m using large titanium-dioxide white painted field normalization reference panels and the expected detector unity values as well as sensor body temperatures were recorded. The white panel reference measurements illustrated that individual filtered sensor detectors subjected to the same thermal change can behave differently. Across 361 observations of all filtered detectors before and after field collections where temperature changed by more than one degree, values changed an average of 0.24% per 1 °C. Recommendations based on years of sensor control data and plant field phenotyping agricultural research are provided to support ACS-470 researchers by using white panel normalization and sensor temperature stabilization.
Why it matches plant phenotyping methods植物フェノタイピング用の近接アクティブ反射センサーについて、温度影響を評価し、正規化・安定化を含む運用方法を提案する技術研究であり、測定法が中心的です。
abstractThe purpose of this study was to characterize the only customizable proximal active reflectance sensor available for HTPP research, including a 10 °C increase in temperature during sensor warmup and in field conditions, and to suggest an operational use approach for researchers.
Reproduction assets foundThe paper's Data Availability Statement points to public USDA Ag Data Commons datasets containing the authors' proximal-sensing/phenotyping raw data (high-throughput phenotyping data from the proximal sensing cart and the Bronson Files field datasets), which directly underpin this paper's ACS-470 NDVI/temperature phenyDataset · publicRelated raw datasets are available from the USDA Ag Data Commons. https://data.nal.usda.gov/dataset/high-throughput-phenotyping-data-proximal-sensing-cartOpen asset ↗USDA Ag Data Commonslines:100-326Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
It is valuable to develop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data for diverse soil backgrounds without any ground calibration. To achieve this objective, 2 strategies were investigated to improve our existing random forest regression (RFR) model, which was trained with simulations from a radiative transfer model (PROSAIL). The 2 strategies consisted of (a) broadening the reflectance domain of soil background to generate training data and (b) finding an appropriate set of indicators (band reflectance and/or vegetation indices) as inputs of the RFR model. The RFR models were tested in diverse soils representing varying soil types in Australia. Simulation analysis indicated that adopting both strategies resulted in a generic model that can provide accurate estimation for wheat LAI and is resistant to changes in soil background. From validation on 2 years of field trials, this model achieved high prediction accuracy for LAI over the entire crop cycle (LAI up to 7 m 2 m -2 ) (root mean square error (RMSE): 0.23 to 0.89 m 2 m -2 ), including for sparse canopy (LAI less than 0.3 m 2 m -2 ) grown on different soil types (RMSE: 0.02 to 0.25 m 2 m -2 ). The model reliably captured the seasonal pattern of LAI dynamics for different treatments in terms of genotypes, plant densities, and water-nitrogen managements (correlation coefficient: 0.82 to 0.98). With appropriate adaptations, this framework can be adjusted to any type of sensors to estimate various traits for various species (including but not limited to LAI of wheat) in associated disciplines, e.g., crop breeding, precision agriculture, etc.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータから小麦LAIを推定する汎用モデルを開発し、異なる土壌・圃場試験で検証しており、植物形質取得手法が中心である。
abstractdevelop a generic model that can accurately estimate the leaf area index (LAI) of wheat from unmanned aerial vehicle-based multispectral data
Reproduction assets foundThe paper's Data Availability statement points to public source code and data at UQ eSpace (DOI 10.48610/ac9642c), covering the RFR model code and supporting data. Additionally, the BASE soil reflectance dataset used to generate test soil backgrounds is publicly available on Zenodo (record 6265730).Code · publicOther data and source code supporting this work are available at UQ eSpace, and a unique DOI (https://doi.org/10.48610/ac9642c) is provided for public access.Open asset ↗UQ eSpace · 10.48610/ac9642clines:298-372Dataset · publicThe BASE soil reflectance data are available online ( https://zenodo.org/record/6265730 ).Open asset ↗Zenodo · 6265730lines:176-179Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
Why it matches plant phenotyping methodsトマト感染株の病害状態をハイパースペクトル測定と機械学習で非破壊・早期推定する方法を開発しており、表現型取得・判定手法が中心である。
abstractan approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented.
Reproduction assets foundThe paper's Data availability statement explicitly provides the authors' hyperspectral tomato disease measurements (the paper-specific phenotyping dataset) at a public link.Dataset · publicData availability
The hyperspectral measurements presented in this study are available at https://bit.ly/3W7VroF .Open asset ↗lines:175-230Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
Why it matches plant phenotyping methods植物群集の3次元構造とマルチスペクトル情報を取得する自動フェノタイピングシステムをフィールド用に改変・実証しており、植物表現型取得法が研究の中心である。
abstractWe introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies
Reproduction assets foundThe paper's Data availability statement points to a public repository DOI (10.17616/R32P9Q, a re3data registry DOI) for the datasets presented in this study, which include the DWCP scan-derived morphological/physiological parameters, manual trait measurements, and vegetation data. No author analysis code or trained模型的公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: http://doi.org/10.17616/R32P9Q.Open asset ↗10.17616/R32P9Qpdf-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
Why it matches plant phenotyping methodsハイパースペクトル画像からイネ種子の温度処理状態を分類する深層学習手法を開発・検証し、校正・前処理・分類用GUIも提供しており、種子表現型の取得・抽出方法が中心である。
abstractA framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented.
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 codes for the DL framework for hyperspectral seed image calibration, preprocessing, segmentation, and classification are available at: https://gitfront.io/r/vido6/vC64GLsxCDZx/classificationRice/ , accessed on 23 March 2023.Open asset ↗classificationRicelines:95-200Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum . In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R 2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.
Why it matches plant phenotyping methods小麦粒のFHB損傷・重症度をハイパースペクトル画像と機械学習で分類・定量する手法が研究の中心であり、Mask R-CNNによる抽出と精度評価も含むため、植物病害表現型の方法研究として適格。
abstractwe explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels.
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 · publicData Availability Statement: The codes and the data are available at Li lab GitHub repository at
https://github.com/LiLabAtVT/WheatHyperSpectral (accessed on 1 March 2023).Open asset ↗LiLabAtVT/WheatHyperSpectralpdf-page:11 lines:1-60Code / 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 14 Sept 2026
In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R 2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.
Why it matches plant phenotyping methodsVIS-NIR-SWIRハイパースペクトロスコピーとAIによるレタスの色素表現型推定・分類が研究の中心であり、植物形質取得手法の開発・評価に該当する。
abstractthe use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties
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/plants12061333/s1 . Table S1. Descriptive analysis parameters of lettuce varieties. Pigment of leaves expressed by leaf area (mg m −2 ), mass (mg g −1 ), and volume (mL L −1 ) ( n = 132); Table S2. STEPW and VIPs by wavelengths selected according to classified algorithm-based ANOVA and information gain ratio ( p < 0.001) by band range spectroscopy from reflectance leaves.Open asset ↗lines:80-115Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.
Why it matches plant phenotyping methods綿花の生育段階・遷移を衛星観測指標と環境データから推定する手法を開発し、地上観測で評価している。さらに再利用可能なデータセットも提供するため、植物フェノタイピング手法が中心である。
abstractwe propose a new approach for the within-season phenology estimation for cotton at the field level.
Reproduction assets foundThe paper's ground-observation phenology dataset (1,285 field observations with photos, labels, field geometries) is publicly released on GitHub, and the data are additionally deposited on Zenodo. No author analysis code is explicitly shared.Dataset · publicData relevant to this paper are available from Zenodo at DOI: 10.5281/zenodo.7646864 ( https://doi.org/10.5281/zenodo.7646864 ).Open asset ↗zenodo · 10.5281/zenodo.7646864lines:153-165Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
Why it matches plant phenotyping methods植物の熱画像・マルチスペクトル画像を用いた表現型取得と解析のためのオープンソース計算パイプラインを開発しており、画像補正・共登録・解析が中心的な方法論的貢献である。
abstractThis study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors.
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2件を確認しました。Code · publicAll codes were written in MATLAB to produce a library package which is available at https://github.com/SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipeline.git (accessed on 12 November 2022).Open asset ↗SmartSense-iHub/Thermal-and-Multispectral-Image-Analysis-Processing-Pipelinelines:35-43Dataset · publicThe data is freely shared in google drive and can be accessed from the following link. https://drive.google.com/file/d/1VSqRu5CUZhyd3MF23kdRjqrtRke7sbJU/view?usp=share_link .Open asset ↗lines:91-241Code / dataset availability confirmedarXiv · checked 14 Sept 2026
Multispectral / hyperspectralWhole plant / canopy / plot / field
The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Airborne hyperspectral imaging has shown promise for measuring plant diversity remotely, but to operationalise these efforts over large regions we need to advance satellite-based alternatives. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR) for species richness prediction. The best prediction results were $r=0.76$ and $\text{RMSE}=5.89$ for the Southern Tablelands region, and $r=0.68$ and $\text{RMSE}=5.95$ for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness.
Why it matches plant phenotyping methodsDESISハイパースペクトルデータから植物種数を推定する特徴抽出・回帰手法を比較し、交差検証で性能評価しており、植物フェノタイピング手法が中心である。
abstractThis study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia.
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 · publicFor on-ground measures of vascular plant species richness, we obtained plant community survey data from the NSW BioNet Vegetation Information System database [ Government, 2019 ] .Open asset ↗NSW BioNet Vegetation Information Systemlines:75-98Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
Why it matches plant phenotyping methodsSentinel/GEDI等のリモートセンシング画像から樹冠高を推定する深層学習手法を開発し、外部データで検証しているため、植物形質取得法が中心である。
abstractwe developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map
Reproduction assets foundThe paper's primary phenotyping-relevant input is the GEDI L2A canopy height dataset (526,449 footprints over the Landes forest, 2020), explicitly downloaded from NASA's EarthDataSearch. This is a public, paper-specific sensor dataset directly used for the study's canopy height measurements and model training. No code,Dataset · publicwater bodies (Beck et al., 2020). Indeed, these
surfaces mirror the transmitted waveforms that have a pulse width of ~ 15 ns which
corresponds to a ~ 2.25 m wide waveform (Dubayah et al., 2020).
In total, 526,449 footprints from the GEDIv002 L2A product (Dubayah et al., 2021) were
downloaded from NASA’s EarthDataSearch website
(https://search.earthdata.nasa.gov/search) for this study, covering the entire area of interest
for 2020. Due to atmospheric perturbations, some waveforms could not be used to give
information on the vertical forest structure. Therefore, several filtering criteria were applied to
remove unusable waveforms: (1) When the quality_flag provided in the GEDI data was set toOpen asset ↗GEDIv002 L2Apdf-raw-page:6 lines:1-45Code / 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
Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and 'n' rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.
Why it matches plant phenotyping methodsオリーブ果実を対象とした圃場ハイパースペクトル画像データセットであり、画像取得、正規化、アルゴリズムによるスペクトル特徴抽出、データ保存が中心的に記述されているため、植物フェノタイピング手法・データセットとして収録する。
abstractThis study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September.
Reproduction assets foundThe paper is a data descriptor whose hyperspectral olive dataset (raw/normalised HSIs and processed spectral signatures) is publicly deposited in Mendeley Data with DOI and direct URL given in the article.Dataset · publicolive field in a city on the north-west side of Seville in the south of Spain.
• City/Town/Region: Espartinas, Seville province
• Country: Spain
• Latitude and longitude: 37.394327, -6.121881
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/8xvhcsdvst.1
Direct URL to data: https://data.mendeley.com/datasets/8xvhcsdvst/1
Value of the Data
•
In smart agro applications, there are technological approaches that use artificial intelligence or traditional statistical methods such as ANOVA or PLS [1] , [2] , [3] , which require the use of data. In this regard, data are essential for both artificial intelligence and stochastic approaches. There Open asset ↗Mendeley Data · 10.17632/8xvhcsdvst.1lines:1-52Code / 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 confirmedOpenAlex · checked 15 Sept 2026
Abstract. The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot.ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species.iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.
Why it matches plant phenotyping methodsUAV画像・点群から森林の3D構造や個体樹木の高さ・樹冠径を扱う再利用可能なデータセットを提供し、抽出結果の検証や機械学習に用いるため、植物表現型データ基盤が中心です。
abstractThe SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes.
Reproduction assets foundThe paper is a data description paper for the SiDroForest collection; all four datasets (UAV-SfM point clouds/orthomosaics, individually labeled trees, synthetic tree-crown images, Sentinel-2 labeled patches) are published on PANGAEA with explicit public download availability.Dataset · publice future users time when attempting to classify
vegetation of central Siberian and eastern Siberian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et aOpen asset ↗PANGAEA · 10.1594/PANGAEA.933263lines:557-585Dataset · publicerian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. EverOpen asset ↗PANGAEA · 10.1594/PANGAEA.932821lines:557-585Dataset · publice PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biolOpen asset ↗PANGAEA · 10.1594/PANGAEA.932795lines:557-585Dataset · publicand orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biological and ecological applications is therefore quite rare and an important addition for scientific studiOpen asset ↗PANGAEA · 10.1594/PANGAEA.933268lines:557-585Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
Why it matches plant phenotyping methodsピーナッツ種子の品種分類とカビ状態検出を目的に、ハイパースペクトル画像と前処理・機械学習モデルを中心的に開発・比較しており、植物の状態を推定するフェノタイピング手法に該当する。
abstracthyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds.
Reproduction assets foundThe paper's data availability statement explicitly deposits the original study contributions (peanut seed hyperspectral data) in a public GitHub repository, which is listed among the allowed URLs.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://github.com/wuqingsongwj/Peanut-seed .Open asset ↗wuqingsongwj/Peanut-seedlines:589-618Code / 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 · checked 15 Sept 2026
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R 2 , root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R 2 , RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R 2 , RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.
Why it matches plant phenotyping methods消費者向けUAVマルチスペクトル画像を用いて小麦の収量および窒素利用効率を推定・検証する方法が研究の中心であり、植物形質の取得と予測性能を評価している。
abstractThis study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat
Reproduction assets foundThe article's data availability statement points to a public figshare deposit containing the paper's Supplementary Material and Appendix (including e.g. Supplementary Table S1 with VARI-threshold background-removal accuracy results). No author analysis code, raw imagery, or trained models are explicitly deposited; DJI/Supplement · publicapplications should be thoroughly explored.
Data availability statement
The original contributions presented in the study are included in the article/
Supplementary Materials
. Further inquiries can be directed to the corresponding author. The Supplementary material and Appendix document for this article can be found online at: https://figshare.com/s/fa258c55dd6bd9fc69b9 named as Supplementary Material.zip.
Author contributions
XL, JL, and YZ designed and developed the research idea. YZ, XC, and XT conducted the field data collection. JL and YZ performed the data analysis. JL wrote the manuscript. JL, YZ, XT, XC, and XL contributed to the results and data interpretation, discussion, and reviOpen asset ↗figsharelines:861-886Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus ( Pseudocercospora fijiensis ) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.
Why it matches plant phenotyping methodsバナナ葉の病徴をハイパースペクトル画像と複数の機械学習モデルで早期検出し、モデル性能を評価する手法研究であり、植物病害状態の取得・推定が中心です。
abstractWe evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging.
Reproduction assets foundThe paper's hyperspectral banana-leaf training/validation datasets and the authors' analysis source code (PLS-PLR, NPLS-DA, SVM, MLP) are explicitly stated as publicly available on the authors' GitHub repository.Code · publicThe source programs are available at the following link: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:34-65Dataset · publicThe two datasets used in this study (i.e., the training dataset and validation dataset are available at: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:234-247Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Over the years, researchers have applied various deep learning techniques to automatically recognise plant diseases from both raster and spectral images. The primary focus of the existing studies is developing individual species-specific or disease-specific models, where the former recognises diseases of single crop type and the latter recognises single diseases of single or multiple crop types. Building one global model to recognise diseases of multiple crops has also been widely explored, where a class is treated as a crop-disease combination. While training individual species-specific or disease-specific deep models is labour-intensive, embracing a vast number of crop species and inherent diseases present on this planet makes the model cumbersome. In order to address this problem, a more intuitive and feasible family-based plant disease characterisation approach with botanical reasoning is proposed in this study. This approach demonstrates the feasibility of six state-of-the-art deep neural networks through a set of extensive experiments incorporating six key strategies. The results on a newly built family-based plant disease dataset confirm that the proposed novel approach is convincing to be applied in a plant family-based disease recognition problem. Further, this study creates future opportunities for more intuitive plant disease data collection and benchmark classification model development.
Why it matches plant phenotyping methods植物画像から病害を認識・特徴付ける深層学習手法を提案し、データセット構築と複数モデルによる検証を行っており、病害状態の表現型取得・分類が中心である。
abstracta more intuitive and feasible family-based plant disease characterisation approach with botanical reasoning is proposed in this study.
Reproduction assets foundThe paper's plant disease image analysis is built from the publicly available PlantVillage dataset, which the authors explicitly state is accessible via a Mendeley Data URL in the Data availability statement. This is the image dataset used for the paper's phenotyping measurements. The referenced GitHub repositories (mlDataset · publicThe data used in this research is publicly available and can be accessed via: https://data.mendeley.com/datasets/tywbtsjrjv/1Open asset ↗data.mendeley.com/datasets/tywbtsjrjv · tywbtsjrjv/1pdf-page:17 lines:1-27Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Why it matches plant phenotyping methodsUAVおよびSentinel-2マルチスペクトル画像から草丈、LAI、バイオマス、葉窒素などの植物形質を推定し、参照データとの相関比較・技術評価を行っているため、植物フェノタイピング手法の検証・応用が中心です。
abstractprospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated
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 Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs14174426/s1, Table S1: Summary statistics of the plant trait
variables measured at 20 sample points in field A; Table S2: Summary statistics of the plant trait
variables measured at 20 sample points in field G; Table S3: Absolute correlation results of plant
maximum height and mean height in field AOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.
Why it matches plant phenotyping methodsブドウ葉の病徴・病害面積をハイパースペクトル画像から自動推定し、SVMの検証と病害重症度評価を行う方法中心の研究である。
abstractdeveloped an automatic detection tool based on a Support Vector Machine (SVM) classifier
Reproduction assets foundThe paper's hyperspectral image dataset of downy mildew on grapevine leaves is explicitly stated as publicly available on Recherche Data Gouv with a DOI (10.57745/AV1ETI), matching an allowed URL. No author analysis code repository is deposited (only generic library citations), so only the dataset qualifies.Dataset · publicThe hyperspectral images used in this work came from a database publicly available [ 40 ] at https://doi.org/10.57745/AV1ETI , accessed on 19 July 2022.Open asset ↗10.57745/AV1ETIlines:135-137Code / 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 8 Sept 2026
Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.
Why it matches plant phenotyping methods大豆の地上部バイオマスを推定するための時系列マルチスペクトル画像と植生指数を中心に、予測精度および撮像時期を評価しているため、植物表現型計測手法の実質的な適用・検証に該当する。
abstractMultispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the datasets generated and analyzed (phenotype/vegetation-index data and analysis materials) for this soybean multispectral imaging study. Supplemental files are only docx summaries, not datasets themselves.Dataset · publicThe datasets generated and analyzed in the present study are available from the ‘Sakuraikengo/TSMS_supple’ repository in the GitHub, https://github.com/Sakuraikengo/TSMS_supple.Open asset ↗Sakuraikengo/TSMS_supplehtml-lines:718-899Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.
Why it matches plant phenotyping methods森林キャノピーの影分率という植物群落の状態を推定する反射シミュレータを開発し、UAV測定およびSentinel-2反射率との比較で検証しており、植物状態の取得・推定手法が中心である。
abstractwe developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation.
Reproduction assets foundThe paper's CSRS reflectance/shadow simulation code is explicitly stated to be publicly available on the authors' GitHub repository. The ECOSTRESS Spectral Library is a generic external spectral database, not a paper-specific asset.Code · publicng—review and editing, W.T.; visualization, T.F.;
supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The simulation code of CSRS is available from https://github.com/Takumi-Fuji6936/CSRS.git (accessed on 29 June 2022).
Conflicts of Interest: The authors declare no conflict of interest.
References
1. FAO. Assessment, Global Forest Resources 2020. Available online: https://www.fao.org/3/CA8753EN/CA8753EN.pdf (accessed
on 10 November 2021).
2. Beer, C.; Reichstein, M.; Tomelleri, E.; Ciais, P.; Jung, M.; CarvalhaisOpen asset ↗Takumi-Fuji6936/CSRSpdf-raw-page:13 lines:1-50Code / 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 14 Sept 2026
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・NDVIという植物キャノピー形質の推定を、APSIM制約のためのエミュレーションおよびベイズ最適化ワークフローとして技術的に評価しており、形質取得・抽出法が中心的です。
abstractWe leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest.
Reproduction assets foundThe paper uses a publicly available maize yield dataset from Beck's Hybrids covering 332 locations (2014-2019) with management, soil, and weather information as site-level inputs for APSIM simulations and yield validation. No author analysis code, trained models, or data deposit is disclosed (Data Availability and AcknDataset · public4 of 25
Figure 1. 2 Figures side by side
million ha from 2014-2019 [31]. To perform APSIM simulations at a series of randomly 143
selected locations, site-level information was acquired from the publicly available maize 144
yield dataset maintained by Beck’s Hybrids (https://www.beckshybrids.com/Research/ 145
Yield-Data). The dataset included information on management operations (i.e., planting 146
date, harvesting date, plant population, row spacing, and previous crop planted for residue 147
type), soil, and weather for 332 locations from 2014 to 2019 (Figure 2(a)). Information on 148
soil texture and soil organic carbon (SOC)Open asset ↗pdf-raw-page:4 lines:1-19Code / 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 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 confirmedCrossref · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / field
Abstract Monitoring the rapid and extensive changes in plant species distributions occurring worldwide requires large-scale, continuous and repeated biodiversity assessments. Imaging spectrometers are at the core of novel spaceborne sensor fleets designed for this task, but the degree to which they can capture plant species composition and diversity across ecosystems has yet to be determined. Here we use imaging spectroscopy and vegetation data collected by the National Ecological Observatory Network (NEON) to show that at the landscape level, spectral beta-diversity—calculated directly from spectral images—captures changes in plant species composition across all major biomes in the United States ranging from arctic tundra to tropical forests. At the local level, however, the relationship between spectral alpha- and plant alpha-diversity was positive only at sites with high canopy density and large plant-to-pixel size. Our study demonstrates that changes in plant species composition and diversity can be effectively and reliably assessed with imaging spectroscopy across terrestrial ecosystems at the beta-diversity scale—the spatial scale of spaceborne missions—paving the way for close-to-real-time biodiversity monitoring at the planetary level.
Why it matches plant phenotyping methods画像分光から算出したスペクトル多様性を植物種組成・多様性に対して検証し、広域での植物状態評価手法として中核的に扱っているため。
abstractspectral beta-diversity—calculated directly from spectral images—captures changes in plant species composition across all major biomes in the United States
Reproduction assets foundThe paper's analysis code is publicly available in two author GitHub repositories (specdiv and NEON_crown_area, both Zenodo-archived), and all phenotyping measurements (NEON spectral imagery and plant inventory data) are publicly available from NEON's data portal.Code · publicVaughn for their contribution to the peer review of this work. Peer reviewer reports are available.
Data availability
All data used in this analysis are available from NEON: 10.48443/qeae-3×15, 10.48443/4e85-cr14, 10.48443/abge-r811, 10.48443/e3qn-xw47, 10.48443/h2rb-pj34.
Code availability
The R code is available on GitHub at https://github.com/elaliberte/specdiv (10.5281/zenodo.6385476) and https://github.com/annakat/NEON_crown_area (10.5281/zenodo.6383923).
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 affiliations.
Supplementary informaOpen asset ↗elaliberte/specdiv · 10.5281/zenodo.6385476lines:59-95Code · publicr reviewer reports are available.
Data availability
All data used in this analysis are available from NEON: 10.48443/qeae-3×15, 10.48443/4e85-cr14, 10.48443/abge-r811, 10.48443/e3qn-xw47, 10.48443/h2rb-pj34.
Code availability
The R code is available on GitHub at https://github.com/elaliberte/specdiv (10.5281/zenodo.6385476) and https://github.com/annakat/NEON_crown_area (10.5281/zenodo.6383923).
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 affiliations.
Supplementary information
The online version contains supplementary material available at 1Open asset ↗annakat/NEON_crown_area · 10.5281/zenodo.6383923lines:59-95Code / 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
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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
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lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
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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 confirmedCrossref · checked 8 Sept 2026
Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available.
Why it matches plant phenotyping methods衛星データによる作物収量推定について、7種類の圃場測定法を比較し、収量推定モデルの適合度・精度への影響を検証している。収量という植物形質の取得・推定法が研究の中心である。
abstractTo evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the “true yield” measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019.
Reproduction assets foundThe paper's field-measured maize yield dataset (seven sampling methods plus full-field harvest for 196 Ethiopian fields) is openly available via a Zenodo DOI in the Data Availability Statement. No code availability is stated.Dataset · publicData Availability Statement: The data presented in this study are openly available here:
https://doi.org/10.5281/zenodo.6471977.Open asset ↗zenodo · 10.5281/zenodo.6471977pdf-page:11 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. RESULTS: In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. CONCLUSIONS: This study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Why it matches plant phenotyping methodsイネ葉のハイパースペクトル画像と深層学習により病斑割合・病害重症度を推定し、抵抗性品種選抜へ応用する手法が研究の中心である。
abstractinformative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their LPnet deep learning analysis code on GitHub, explicitly stated in both the Software tools and Availability sections. The hyperspectral phenotype data itself is only available on request, so it is listed separately as request_only.Code · publicRelevant algorithm code is available on the GitHub address ( https://github.com/jinnuozhang/LPnet ).Open asset ↗jinnuozhang/LPnetlines:121-133Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Sorghum, a genetically diverse C 4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation ( V cmax ), phosphoenolpyruvate (PEP) carboxylation ( V pmax ), and electron transport ( J max ), quantified using a C 4 photosynthesis model, were evaluated in two field-grown training sets ( n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax , V pmax , J max , SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 ( n = 875 plots including 650 genotypes) and 2020 ( n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max . Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se . J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
Why it matches plant phenotyping methodsトラクター搭載ハイパースペクトルセンシングとPLSRにより光合成関連形質を推定し、独立試験でモデル性能を評価している。形質取得法の開発・検証と大規模スクリーニングへの応用が中心である。
abstractPartial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing.
Reproduction assets foundThe authors state that all phenotypic data used to develop the PLSR models (ground truth Vcmax, Vpmax, Jmax, SLN, LMA and associated hyperspectral measurements) is publicly available via a UQ eSpace DOI. Genotypic marker data is only available upon request and is not a phenotyping asset. No author analysis code or URLsDataset · publicAll phenotypic data used to develop the models presented in this manuscript is available here: https://doi.org/10.48610/acbe0df .Open asset ↗10.48610/acbe0dflines:488-522Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.
Why it matches plant phenotyping methodsTLS、HMLS、UAS-SfM、ALSなど複数のセンシング手法で林分の梯子燃料密度を推定し、手法間比較・相関評価と火災燃焼重症度予測を行っており、植物群落形態の計測手法が中心である。
abstractHere, we compared remote sensing and field-based approaches to estimate ladder fuel density.
Reproduction assets foundThe authors deposited the study's ladder fuel density and related measurements in the USDA FS Research Data Archive (DOI 10.2737/RDS-2021-0101). The paper also uses publicly available Sonoma County ALS LiDAR data (sonomavegmap.org) as a remote sensing input for its ladder fuel analysis. No author analysis code or modelDataset · 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/10.2737/RDS-2021-0101 , FS Data Research Data Archive.Open asset ↗FS Data Research Data Archive · 10.2737/RDS-2021-0101lines:614-643Dataset · publicAirborne laser scanner (ALS) data were downloaded from existing data collected in 2013 for Sonoma County (QL1/2013). The imagery was collected using Leica ALS50 and ALS70 sensors at 5054 m altitude on a Beechcraft Airliner twin turboprop aircraft. These sensors have 1064 nm (NIR) lasers. The maximum RMSE for the georeferencing of this data was 0.2 cm due to the use of 9,685 ground control points ( Watershed Sciences, 2016 ). Data can be found at http://sonomavegmap.org/data-downloads/ .Open asset ↗lines:342-349Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
Why it matches plant phenotyping methodsマルチスペクトル画像と多変量解析により、アルファルファ種子の活力・生存性・発芽率を非破壊推定する手法が研究の中心であるため。
abstractAs a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation.
Reproduction assets foundThe authors provide a public Google Drive supplement containing the paper's own multispectral imaging data: mean reflectance at 19 wavelengths for all seeds (Table S1), morphological feature data for all seeds (Table S2), and multispectral images of the alfalfa seed samples (Figures S1–S6). These directly reproduce theDataset · publicThe following are available online at https://drive.google.com/file/d/13CXchEm81qnbIZCXLqdvupDPib7BS8FM/view?usp=sharing , Table S1: Mean reflectance of 19 wavelengths in all seeds, Table S2: Data of morphological feature in all seeds. Figure S1: Multispectral image of seeds harvested in 2004. Figure S2: Multispectral image of seeds harvested in 2008. Figure S3: Multispectral image of seeds harvested in 2019. Figure S4: Multispectral image of seeOpen asset ↗lines:79-239Code / 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 confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Cassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation on a susceptible and a tolerant cultivar. Application of the device has the potential to increase farmers’ access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses.
Why it matches plant phenotyping methods植物のウイルス感染状態（病徴）を対象に、携帯型マルチスペクトル画像装置と空間・スペクトル機械学習を開発し、早期検出性能を示した研究であり、表現型取得法が中心です。
abstractWe have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time.
Reproduction assets foundThe article's Data availability statement explicitly deposits the paper's own multispectral imaging dataset (Cassava-TME204-UCBSV trials) on Zenodo. No author analysis code or trained models are stated as available.Dataset · publicThe MSI dataset of these three trials (Cassava-TME204-UCBSV) are available at https://doi.org/10.5281/zenodo.4636968 .Open asset ↗zenodo · 10.5281/zenodo.4636968lines:203-262Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Multispectral imaging is at the forefront of contactless surface analysis. Standard multispectral imaging systems use sophisticated software, cameras and light filtering optics. This paper discloses the building of a customizable and cost-effective multispectral imaging and analysis system. It integrates a web camera, light emitting diodes (LEDs) lighting, a semisphere for even lightening, an open-source Arduino™ development board and a free Python application to automatically obtain and visually analyze multispectral images. The device is hereafter called MEDUSA and its optical performance was tested for repeated Imaging consistency, visible and near infrared band sensitivity and lighting evenness. Four proof of concept tests were run in order to understand the advantageous use of this system, as compared to a simple visual score of diverse samples. Each of three qualitative tests used sets of 12 LED band spectral images to analyze ink changes in a counterfeit bill, surface bruises on Hass avocado fruits and transient changes in petri dish grown bacterial colonies. A fourth test used single band imaging in a set of standard laboratory analyzed plant samples, to quantitatively relate a red band light reflectance to its nitrogen content. These tests indicate that MEDUSA made images may yield qualitative and quantitative spectral information unseen to the naked eye, suggesting potential use in currency counterfeit tests, food quality analyses, microbial phenotyping and agricultural plant chemistry. MEDUSA can be freely reproduced and customized from this research, making it a powerful and affordable analytical tool to analyze a wide range of subtle chemical properties in samples at industrial and science fields.
Why it matches plant phenotyping methods植物試料の化学的状態を非接触画像から推定する低コスト multispectral imaging システムを開発し、光学性能を検証している。植物サンプルの反射率と窒素含量の定量関係も評価され、植物表現型取得への応用が明示されている。
abstractThis paper discloses the building of a customizable and cost-effective multispectral imaging and analysis system.
Reproduction assets foundThe paper discloses the MEDUSA multispectral imaging system with author-provided analysis/control code (Python GUI with PCA image analysis, Arduino firmware) publicly deposited on OSF. No standalone plant-phenotyping dataset (e.g., the nitrogen reflectance plant samples) is explicitly deposited; only the software/hardwCode · publicences
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Environmental and agricultural sciences
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Counterfeit detection
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General surface analytical chemistry
Hardware type
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Imaging tools
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Field measurements and sensors
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Electrical engineering and computer science
Open-Source License
GNU General Public License (GPL)
Cost of Hardware
Less than 300USD
Source File Repository
https://osf.io/zhp4m/
Hardware in context
Multispectral imaging is at the forefront of dry and non-invasive spatial analytical chemistry. It is used in fields as diverse as human health [9] , food quality [8] , plant science [22] soil chemistry [18] , counterfeit [7] and art analyses [15] . Despite its proven general value, wide access to these systOpen asset ↗OSFlines:1-59Code / 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 · Crossref · Europe PMC · checked 8 Sept 2026
The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から画像特徴を抽出して二次作物形質を構築し、育種選抜性能を検証することが研究の中心であるため、植物フェノタイピング手法研究に該当する。
abstractA UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons.
Reproduction assets foundThe paper explicitly states that the LASSO model code and the UAV imagery datasets are publicly available in the authors' GitHub repository. The raw data availability statement only offers data on request, but the code/imagery asset has an explicit public URL.Code · publicThe code for the LASSO model and the imagery datasets can be found at: https://github.com/Heyphil/Soybean-variety-selection.git .Open asset ↗https://github.com/Heyphil/Soybean-variety-selection.gitlines:320-328Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
Why it matches plant phenotyping methods屋内ハイスループット表現型解析、画像処理、機械学習を用いて作物形質を抽出・予測し、プラットフォーム性能も評価しているため、方法が研究の中心である。
abstractA pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits
Reproduction assets foundThe paper's full LemnaTec HTP image dataset (RGB, NIR, FLUOR, HYP images of 50 B. napus founder lines) is openly available at the authors' P2IRC USask repository, directly reproducing this paper's phenotyping measurements. The genomevis tool concerns SNP/genotype visualization, not phenotyping, and no analysis code is,Dataset · publicThe full image dataset is openly available at https://p2irc-data-dev.usask.ca/dataset/10.1109.SciDataManager.2020.7284788 (Dataset name: P2IRC Flagship 1 Data).Open asset ↗P2IRC Flagship 1 Data · 10.1109.SciDataManager.2020.7284788lines:323-329Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field‐based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat (Triticum aestivum L.) lines planted in full plots and grid‐spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unoccupied aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from ‐0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from ‐0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plants, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early‐generation germplasm. Efficient use of early‐generation, affordable HTP data could improve overall genetic gain in plant breeding.
Why it matches plant phenotyping methods単一個体向けに圃場HTPパイプラインを拡張し、UAVによるNDVI取得・抽出と全区画収量との関係を評価しており、表現型取得法の応用・検証が中心です。
abstractwe extended field‐based HTP pipelines to evaluate and extract data from spaced single plants
Reproduction assets foundThe paper's data availability statement explicitly deposits phenotypic data, raw images, and analysis scripts in a public Zenodo repository (DOI 10.5281/zenodo.6515042), which directly reproduces this paper's plant-phenotyping measurements and computational analysis. The NCBI BioProject (PRJNA764168) contains DNA/genoyDataset · publicilized in this work should
be applicable to a range of different crops and plant breeding
programs and allow the development of crops that can meet
the world’s food, fiber, and fuel needs.
DATA AVA I L A B I L I T Y S TAT E M E N T
Phenotypic data, including raw images and analysis scripts
are available in the Zenodo Repository https://doi.org/10.5281/zenodo.6515042. DNA sequence data from genotypes
used in this study is available in NCBI Sequence Read
Archive (SRA) (https://www.ncbi.nlm.nih.gov/bioproject/) as
BioProject accession number PRJNA764168.
AC K N OW L E D G M E N T S
We thank Shuangye Wu and Ethan Faryna for assistance
in genotyping. This publication is supported by the EArly-Open asset ↗Zenodo · 10.5281/zenodo.6515042pdf-raw-page:12 lines:1-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sensitivity of forest mortality to drought in carbon-dense tropical forests remains fraught with uncertainty, while extreme droughts are predicted to be more frequent and intense. Here, the potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations for 64 site-year combinations in Costa Rican tropical dry forests during the 2015 ENSO drought. Temporal autocorrelation, within the optimal moving window of 24 months, demonstrated robust predictive power for in situ mortality (leave-one-out cross-validation R 2 = 0.54), which allows for estimates of annual biomass mortality patterns at 30 m resolution. Subsequent spatial analysis showed substantial fine-scale heterogeneity of forest mortality patterns, largely driven by drought intensity and ecosystem properties related to plant water use such as forest deciduousness and topography. Highly deciduous forest patches demonstrated much lower mortality sensitivity to drought stress than less deciduous forest patches after elevation was controlled. Our results highlight the potential of high-resolution remote sensing to "fingerprint" forest mortality and the significant role of ecosystem heterogeneity in forest biomass resistance to drought.
Why it matches plant phenotyping methodsLandsat EVIの時間自己相関から森林バイオマス死亡率を推定し、現地センサスで検証する手法が研究の中心であるため、植物状態のリモートセンシング型フェノタイピングに該当する。
abstractthe potential of temporal autocorrelation of high-frequency variability in Landsat Enhanced Vegetation Index (EVI), an indicator of ecosystem resilience, to predict spatial and temporal variations of forest biomass mortality is evaluated against in situ census observations
Reproduction assets foundThe paper's data availability statement points to a public Figshare repository archiving the data supporting the study's forest mortality and EVI resilience results.Dataset · publices, D.H.W. and X.T.X.
drafted the paper, Y.L.L. and G.G.K. helped with method develop-
ment in detecting reduced ecosystem resilience, and all authors con-
tributed to the interpretation of the results and to the text.
DATA AVAILABILITY STATEMENT
The data supporting the results of this study are archived in a public
repository (https://doi.org/10.6084/m9.figsh
are.17207741).
ORCIDOpen asset ↗pdf-raw-page:11 lines:1-98Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran's I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.
Why it matches plant phenotyping methods衛星画像からトウモロコシの生育段階（植物状態）を抽出・分類する画像解析および機械学習手法が研究の中心であり、特徴量比較と分類精度評価も実施している。
abstractThe purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops ( Zea mays L.) in Sentinel 2 satellite images through supervised classification models.
Reproduction assets foundThe paper's supplement (Table S1) publicly lists the 41 Sentinel-2 satellite images analyzed for maize phenology classification, hosted on MDPI. No author analysis code or trained models are stated as publicly available; MATLAB scripts are described but no deposit/URL is given.Dataset · publicThe following are available online at https://www.mdpi.com/article/10.3390/s22010094/s1 , Table S1: The sentinel 2 satellite images analyzed in this research work.Open asset ↗MDPI · 10.3390/s22010094/s1lines:453-471Code / 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 14 Sept 2026
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.
Why it matches plant phenotyping methods種子のハイパースペクトル画像取得、セグメンテーション、スペクトル解析を一体化したプラットフォームとソフトウェアを開発しており、植物形質取得法が中心である。
abstractwe have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe software and data for testing is accessible in Github: https://github.com/tgaochn/HyperSeed (accessed on 3 December 2021).Open asset ↗tgaochn/HyperSeedlines:168-188Code / 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 confirmedCrossref · checked 9 Sept 2026
Abstract Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic‐functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation. We examined the relationships between the image‐derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.
Why it matches plant phenotyping methods航空機イメージング分光法により植物バイオマス、緑色被覆、機能形質、群集組成を抽出し、地下プロセスとの関係を評価しており、植物形質取得が研究の中心的手法である。
abstractImaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic‐functional community composition of vegetation.
Reproduction assets foundThe paper's Open Research statement deposits its data and novel code (Cavender-Bares et al. 2021) on DRUM (University of Minnesota) under a Creative Commons license, and Cedar Creek LTER data are available at the Cedar Creek data repository. These are paper-specific, public, actionable assets covering the phenotyping/遥Dataset · publicData and novel code (Cavender‐Bares et al. 2021 ) are accessible through a Creative Commons license for non‐commercial use on DRUM, the Data Repository of the University of Minnesota, at https://conservancy.umn.edu/handle/11299/220311Open asset ↗DRUM · 11299/220311lines:294-333Dataset · publicand at the Cedar Creek Ecosystem Science Reserve Long‐Term Ecological Research data repository: https://www.cedarcreek.umn.edu/research/dataOpen asset ↗Cedar Creek Ecosystem Science Reserve Long‐Term Ecological Research data repositorylines:294-333Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSfMにより、植物体ごとの病害重症度と生体植生量を定量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。
abstractestablish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a publicly accessible dataset at the author's website (zixuanqiu.com), matching an allowed URL. The study's UAV multispectral imagery, vegetation index data, and 11,400 sample-point annotations for arecanut yellow leaf disease are the paper-specific phenotypcDataset · publicData Availability Statement: Data available in a publicly accessible repository that does not issue
DOIs Publicly available datasets were analyzed in this study. This data can be found here:
http://www.zixuanqiu.com/nd.jsp?id=39#_np=110_649 (accessed on 20 October 2021).Open asset ↗zixuanqiu.compdf-page:19 lines:1-58Code / 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 confirmedEurope PMC · checked 9 Sept 2026
Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.
Why it matches plant phenotyping methods航空分光法と熱スキャンを用いて植物病原体感染を非視覚的な生理・スペクトル形質として検出し、複数病原体・宿主で精度を検証しており、表現型取得手法が研究の中心である。
abstractSpectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread.
Reproduction assets foundThe paper's data availability and code availability statements point to a public GitHub repository (HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications) with a Zenodo DOI (10.5281/zenodo.5535095) containing the study's spectral trait datasets and analysis code. The large airborne hyperspectral imagecDataset · publicThe data used in this study 74 are available at the repository https://github.com/HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications and can be cited as https://doi.org/10.5281/zenodo.5535095Open asset ↗HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications · 10.5281/zenodo.5535095lines:133-192Code · publicThe codes used for this study 74 are available at the repository https://github.com/HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications and can be cited as https://doi.org/10.5281/zenodo.5535095Open asset ↗HyperSens/HyperSens-Divergent-spectral-responses-Nature-Communications · 10.5281/zenodo.5535095lines:133-192Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて、近縁植物の種・集団構造・雑種を高精度に識別し、遺伝的多様性を推定する測定・解析手法が研究の中心であるため。
abstractClassification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy.
Reproduction assets foundThe paper's leaf reflectance spectra (the core phenotyping measurements) are publicly deposited on figshare, and the authors' R code for spectral analysis is on GitHub, both stated in the Data availability section. The NCBI BioProject is genomic data and excluded.Code · publicThe R code for spectral analysis is available at https://github.com/LanceStasinski/Dryas2 .Open asset ↗GitHub · LanceStasinski/Dryas2lines:155-197Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からNDVIを時系列抽出する高スループット植物表現型計測が研究の中心的基盤であり、取得データと解析ワークフローを大規模トウモロコシ集団に実質的に適用している。
abstractUnmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost.
Reproduction assets foundThe authors explicitly state that the data and code used in this maize UAV-NDVI phenotyping study are deposited in the Dryad Digital Repository, providing a public DOI. This is a paper-specific, publicly actionable asset containing the NDVI phenotype data and analysis code.Dataset · publicrces; Writing-review & editing. Kevin P. Price:
Conceptualization; Data curation; Methodology; Resources;
Writing-review & editing. Jianming Yu: Conceptualization;
Resources; Supervision; Writing-review & editing.
DATA A N D C O D E AVA I L A B I L I T Y
Data and code used in this study are uploaded in Dryad Digital
Repository: https://doi.org/10.5061/dryad.44j0zpcf0.C O N F L I C T O F I N T E R E S T
The authors declare no conflict of interest.
O RC I D
Jinyu Wang https://orcid.org/0000-0003-2880-5612
XianranLi https://orcid.org/0000-0002-4252-6911
Tingting Guo https://orcid.org/0000-0002-6647-6998
MatthewJ. Dzievit https://orcid.org/0000-0002-1437-1027
Xiaoqing Yu https://orcid.org/0000-Open asset ↗10.5061/dryad.44j0zpcf0pdf-raw-page:15 lines:1-84Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
A primary selection target for wheat ( Triticum aestivum ) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0-0.6), while the correlation between grain yield and secondary traits ranged from -0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58-0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.
Why it matches plant phenotyping methods小麦育種試験で近接センシングによりNDVI・群落温度を取得し、統計モデルで収量を予測するワークフローを5年間検証しており、形質取得と予測手法が研究の中心である。
abstractThe objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh.
Reproduction assets foundThe paper's data availability statement explicitly deposits all phenotypic data (NDVI, CT, agronomic traits) and analysis code in the Dryad Digital Repository with a public DOI, making it a paper-specific, publicly actionable asset.Dataset · publicAll phenotypic data and code for analysis have been placed in the Dryad Digital Repository available at: https://doi.org/10.5061/dryad.vdncjsxrz .Open asset ↗Dryad Digital Repository · 10.5061/dryad.vdncjsxrzlines:724-739Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Spectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs. In disease resistance phenotyping, spectral signatures can be analysed to derive infection severity scores and to screen breeding lines. Hyperspectra of winter wheat spikes were acquired in a Fusarium head blight phenotyping trial at the milk- and wax-ripening phenological phases. Disease severity ratings were simultaneously performed by an expert on a 9-point visual scale. Ordinal support vector machine models were then trained to assign hill plots to the individual severity levels. The predictive models' performance was evaluated for data collection timing, spectral pre-processing and permitted rating-error tolerance. The models trained to spectra acquired at the milk-ripening phase were sufficiently accurate to reliably distinguish between low, medium and high symptom severity; with accuracy approaching 100% for two-point error tolerance. However, deterioration in prediction quality was noted for the wax-ripening campaign, presumably due to spike-drying. After aggregation of the spectra using the median function no gain could be associated with further pre-processing. Modest performance improvements obtained with two schemes do not justify the additional data acquisition costs involved, but standard normal variate could be advantageous for some scenarios with mean-aggregated spectra. In addition to phenotyping, the results are discussed in relation to large-scale farming applications. Elevated infection risk detection prior to anthesis is recommended for fungicide treatment, considering the pathogen biology. The study is accompanied by a publicly-available dataset and the computational scripts employed to obtain the results.
Why it matches plant phenotyping methodsスペクトル測定と機械学習によりコムギ赤かび病の感染重症度を推定し、収集時期・前処理・誤差許容度を評価しているため、植物表現型取得法の検証・応用が中心です。
abstractSpectroscopic methods can contribute to addressing the field phenotyping bottleneck problem in crop breeding programs.
Reproduction assets foundThe authors deposited the paper's spectral phenotyping dataset (hyperspectra of winter wheat spikes, visual symptom scores) together with the computational analysis scripts and a GNU Guix environment specification in a public Zenodo repository, explicitly excluding only the unused hyperspectral image cubes.Dataset · publicl., 2018), with the scheme, plant health deterioration is associated with less
_
pre-registration form (Zelazny et al., 2020) hosted by the pronounced features, except for the longest wavelengths,
Center of Open Science. The dataset is available from a Zen- where the relationship is reversed. This pre-processing
odo repository (https://doi.org/10.5281/zenodo.4536881), accentuated the effect of the infection on the left shoulder
excluding the hyperspectral data cubes because of their of the NIR plateau. All of these patterns occurred also after
excessive size and the fact that they were not analysed. transforming mean-aggregated spectra (Supplement S2).
The analysis was coded in the R languOpen asset ↗Zenodo · 10.5281/zenodo.4536881pdf-layout-page:6 lines:1-49Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R2 = 0.92–0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
Why it matches plant phenotyping methods大豆地上部バイオマスを時系列のRGB・マルチスペクトル画像から予測するハイスループット表現型解析法を開発し、予測精度も評価しているため、表現型取得・推定法が中心的です。
abstractOur main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time
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 ) previously reported in the literature to correlate with crop biomass ( Babar et al.Open asset ↗lines:305-313Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
Why it matches plant phenotyping methods葉の画像およびハイパースペクトル反射を用いて、病斑被覆率に基づくダイズ葉の病害状態を分類する手法を開発・評価しており、植物表現型取得と解析が中心である。
abstractimage processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data
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 · publicS4 Data. Hyperspectral data used in this study.Open asset ↗lines:335-377Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.
Why it matches plant phenotyping methods植物フェノタイピング用のマルチモーダル・時系列データセットを開発し、複数のコセグメンテーション手法をベンチマークする研究であり、画像からの植物抽出・表現型解析手法が中心です。
abstractThis paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions.
Reproduction assets foundThe paper's CosegPP plant image dataset (Buckwheat/Sunflower, multi-modal, with ground-truth masks) is publicly deposited on Zenodo per the Data Availability statement. The GitHub repos mentioned (MIG, Subdiscover, DeepCO3) are cited third-party prior-work code, not authors' paper-specific analysis code.Dataset · publicData Availability: All relevant data underlying this study are available at https://doi.org/10.5281/zenodo.5117176 .Open asset ↗zenodo · 10.5281/zenodo.5117176lines:138-150Code / 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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SC0012704 to Brookhaven National Laboratory. We thankOpen asset ↗EcoSIS · 10.21232/UTK8zaW4.665pdf-raw-page:36 lines:1-44Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Cassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation. Application of the device has the potential to increase farmers' access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses.
Why it matches plant phenotyping methods携帯型マルチスペクトル撮像装置と機械学習を開発し、カンショのウイルス感染状態を植物画像から早期推定する方法が中心である。
abstractWe have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time.
Reproduction assets foundThe paper's Data Availability section states the multispectral imaging dataset from the three Cassava-TME204-UCBSV trials is publicly deposited on Zenodo, matching the allowed URL exactly.Dataset · publicThe MSI dataset of these three trials (Cassava-TME204-UCBSV) are available at https://doi.org/10.5281/zenodo.4636968Open asset ↗zenodo · 10.5281/zenodo.4636968pdf-page:14 lines:1-50Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Field / plotRGB / grayscaleMultispectral / hyperspectralThermalWhole plant / canopy / plot / field
A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions. The TERRA-REF program deployed a suite of high-resolution, cutting edge technology sensors on a gantry system with the aim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolution multiple times per week. The system contains co-located sensors including a stereo-pair RGB camera, a thermal imager, a laser scanner to capture 3D structure, and two hyperspectral cameras covering wavelengths of 300-2500nm. This sensor data is provided alongside over sixty types of traditional plant phenotype measurements that can be used to train new machine learning models. Associated weather and environmental measurements, information about agronomic management and experimental design, and the genomic sequences of hundreds of plant varieties have been collected and are available alongside the sensor and plant phenotype data. Over the course of four years and ten growing seasons, the TERRA-REF system generated over 1 PB of sensor data and almost 45 million files. The subset that has been released to the public domain accounts for two seasons and about half of the total data volume. This provides an unprecedented opportunity for investigations far beyond the core biological scope of the project. The focus of this paper is to provide the Computer Vision and Machine Learning communities an overview of the available data and some potential applications of this one of a kind data.
Why it matches plant phenotyping methods植物の高解像度マルチセンサーデータと植物表現型データを含む公開ベンチマーク／データセットを紹介し、コンピュータビジョンでの利用を主目的とするため、フェノタイピング手法・基盤として中心的です。
abstractgenerate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions
Reproduction assets foundThe paper describes the TERRA-REF public domain release of plant phenotyping sensor data (RGB, thermal, laser scanner, hyperspectral, PSII) plus derived phenotypes, and explicitly points to public code repositories for the processing pipeline (terraref GitHub, PhytoOracle, AgPipeline) and a data access portal. All are,Dataset · publicprocessing, reviewing, curating, describing, and hosting the data.
Instead, we focused on an initial public release and plan to make new datasets available based on need.
Access to unpublished data can be requested from the authors, and as data are curated they will be added to subsequent versions of the public domain release ( https://terraref.org/data/access-data ).
In addition to hosting an archival copy of data on Dryad [ 16 ] , the
documentation includes instructions for browsing and accessing these
data through a variety of online portals. These portals provide access
to web user interfaces as well as databases, APIs, and R and Python
clients. In some cases it will be easier to acceOpen asset ↗lines:234-317Code · publicapproach described by Li et al . [ 18 ] .
Herritt et al . [ 14 , 13 ] demonstrate and provide software used in analysis of a sequence of images that capture plant fluorescence response to a pulse of light.
Most of the algorithms used to generate data products have not been published as papers but are made available on GitHub ( https://github.com/terraref ); code
used to release the data publication in 2020 is available on Zenodo [ 25 , 15 , 10 , 6 , 4 , 19 , 8 , 7 , 5 , 9 , 17 ] .
Pipeline development continues to support ongoing use of the field scanner as well as more general applications in plant sensing pipelines.
Recent advances have improved pipeline scalability and modulOpen asset ↗terrareflines:193-233Code · publiclant sensing pipelines.
Recent advances have improved pipeline scalability and modularity by adopting workflow tools and making use of heterogeneous computing environments.
The TERRA-REF computing pipeline has been adapted and extended for continuing use with the Field Scanner with the new name ”PhytoOracle” and is available at https://github.com/LyonsLab/PhytoOracle . Related work generalizing the pipeline for other phenomics applications has been released under the name ”AgPipeline” https://github.com/agpipeline with applications to aerial imaging described by Schnaufer et al . [ 22 ] .
All of these software are made available with permissive open source licenses on GitHub to enable accesOpen asset ↗PhytoOraclelines:193-233Code · publicnvironments.
The TERRA-REF computing pipeline has been adapted and extended for continuing use with the Field Scanner with the new name ”PhytoOracle” and is available at https://github.com/LyonsLab/PhytoOracle . Related work generalizing the pipeline for other phenomics applications has been released under the name ”AgPipeline” https://github.com/agpipeline with applications to aerial imaging described by Schnaufer et al . [ 22 ] .
All of these software are made available with permissive open source licenses on GitHub to enable access and community development.
Figure 4: Summary of public sensor datasets from Seasons 4 and 6. Each dot represents the dates for which a particular daOpen asset ↗agpipelinelines:193-233Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.
Why it matches plant phenotyping methodsイネの地上部バイオマスという植物形質を、航空マルチスペクトル画像から抽出・推定する特徴抽出手法を開発し、比較検証しているため、フェノタイピング手法が中心です。
abstractThis work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery.
Reproduction assets foundThe paper's multispectral UAV imagery and ground-truth biomass measurements are deposited on OSF (via a view-only link, which is nonetheless publicly reachable with the URL), and the crop-monitoring protocol is on protocols.io. No author analysis code repository is stated.Dataset · publicDatasets supporting the experimental results presented in Figure 4 , Figure 5 and Figure 6 are available at the Open Science Framework: https://osf.io/cde6h/?view_only=1c4e5e03b9a34d3b96736ad8ab1b2774 folder Raw Data—MDPI Sensors.Open asset ↗Open Science Frameworklines:197-212Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Abstract Background Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A , which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.
Why it matches plant phenotyping methods高スループット光学フェノタイピングシステムの開発と、画像から植物の外部・内部形質を抽出する解析パイプラインが研究の中心であるため含める。
abstractHere we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days
Reproduction assets foundThe paper publicly deposits its maize RGB/HSI/CT images, i-trait phenotypic data, and genotype data on Figshare, and the authors' CT/HSI/RGB image-analysis pipeline code on GitHub and Zenodo, plus figures/supplemental files on Figshare.Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-programOpen asset ↗github · fenghuifh2006/Maize-RGB-CT-HSI-programlines:185-218Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-program and https://doi.org/10.5281/zenodo.4690730Open asset ↗zenodo · 10.5281/zenodo.4690730lines:185-218Code / 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 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 confirmedCrossref · checked 14 Sept 2026
Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物のFVCという形態・生育形質を推定するモデルを開発し、複数作物・圃場実験で精度と頑健性を検証しており、表現型取得手法が研究の中心である。
abstractThis study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' PROSAIL-GP model code and all datasets (UAV-derived canopy reflectance/FVC measurements) in a public GitHub repository, plus detailed protocols on protocols.io. The PROSAIL model itself is a generic prior tool and is excluded.Code · publicle.
Conflict of interest
The authors declare no conflict of interest.
Data availability
Data supporting this work,such as details and source code of the PROSAIL
model used in this study,are openly available at http://teledetection.ipgp.jussieu.fr/prosail/.The code of the PROSAIL-GP model and all of the
datasets are available at https://github.com/WanLiangZJU/Crop-FVC-retrieval. The detailed protocols can be found at protocols.io (https://
dx.doi.org/10.17504/protocols.io.btmynk7w).
References
Aballa A, Cen H, Wan L, Mehmood K, He Y. 2020. Nutrient status diag-
nosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE
Transactions on Industrial Informatics 17, 4379–4389.
BacOpen asset ↗WanLiangZJU/Crop-FVC-retrievalpdf-raw-page:15 lines:1-89Code / dataset availability confirmedOpenAlex · checked 8 Sept 2026
Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Why it matches plant phenotyping methodsUAV時系列画像から作物形質を抽出し、成長動態と収穫期バイオマスを予測するHTP手法を、サンプリング頻度や予測性能とともに技術的に評価しており、フェノタイピング手法が中心である。
abstractUnmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops
Reproduction assets foundThe paper's UAV-derived sorghum phenotyping datasets (imagery features, AGB measurements) are deposited in the Illinois Databank with a public DOI listed in the Data Availability Statement. No author analysis code or trained models are explicitly shared.Dataset · publicData Availability Statement: The datasets used and analyzed during the current study are available
from the corresponding author via Illinois Databank at https://doi.org/10.13012/B2IDB-5649852_V2.Open asset ↗Illinois Databank · B2IDB-5649852_V2pdf-page:14 lines:1-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data.
Why it matches plant phenotyping methods米収量という植物・作物群の形質を対象に、Sentinel-2データ用の3D CNNを開発し、データベース構築と既存手法との性能比較・検証を行っており、収量推定手法が中心である。
abstractwe build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data
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 · publicData Availability Statement: The codes related to this work will be released for reproducible research
at https://github.com/rufernan/RicePAL (accessed on 3 April 2021).Open asset ↗rufernan/RicePALpdf-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とスペクトル植生指数を用いて作物バイオマスおよびN状態を推定し、その感度を2作期で検証しており、植物形質取得・検証が実質的な構成要素である。
abstractThe data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits.
Reproduction assets foundThe article includes an explicit data availability statement depositing the plant and spectral data supporting the study's phenotyping measurements in the public ETH Research Collection repository, making it a paper-specific, publicly actionable asset. Supplementary XLSX files also exist but the repository deposit is aDataset · publicThe plant and spectral data that support the findings of this study, as well as the supplementary material, are available in the online repository with the identifier, https://doi.org/10.3929/ethz-b-000380508 . At https://www.research-collection.ethz.ch/handle/20.500.11850/380508 last accessed [09/06/2020].Open asset ↗10.3929/ethz-b-000380508lines:160-271Code / 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 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:1046-1107