RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Background and Aims Drought reduces wheat yields, yet field-scale quantification of root water uptake (RWU) remains challenging because below-ground processes are difficult to monitor. This study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought. Methods Time-lapse ERT (44 surveys, ≥ 3 week⁻ 1 ) was combined with TDR sensor measurements of soil water content (n = 278 paired ρ–θ observations) to convert resistivity measurements into depth-resolved RWU estimates across 0.1–1.0 m depth. Five petrophysical models were evaluated using date-grouped fivefold cross-validation, with the depth-aware Random Forest performing best. Three wheat genotypes with contrasting root architectures were monitored under terminal drought (142 mm available water). ERT-derived RWU were analysed alongside stomatal conductance, chlorophyll fluorescence, and grain yield. Results ERT resolved RWU strategies among genotypes. WM-203 exhibited aggressive, coordinated multi-layer water extraction across the soil profile (r = 0.80–0.98), whereas WM-140 showed a delayed uptake strategy characterized by early deep-layer dominance followed by mid- and deep-profile engagement, and IPLR-760 displayed inconsistent uptake with mid-profile hydraulic decoupling. Genotypic RWU rankings were consistent with stomatal conductance and grain yield, spanning from 7.0 t ha⁻ 1 in WM-203 to 1.5 t ha⁻ 1 in IPLR-760 despite comparable total water extraction. Conclusion ERT-based quantification of RWU provides a robust, non-invasive approach for resolving genotype-specific water-use strategies under field conditions. The framework enables characterization of water-use coordination patterns and offers a tool for phenotyping drought-resilient wheat genotypes.
Why it matches plant phenotyping methodsERT・TDR・Random Forestを統合し、圃場コムギの根系水吸収を定量化する方法を開発・検証し、乾燥耐性遺伝子型の表現型評価に用いているため、フェノタイピング手法が中心である。
abstractThis study developed a non-invasive hydrogeophysical framework integrating Electrical Resistivity Tomography (ERT), TDR-based soil monitoring, and depth-aware Random Forest calibration to quantify depth-resolved RWU and evaluate genotype-specific water-use strategies under terminal drought.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and supporting data for this ERT-based root water uptake study are publicly available on the authors' GitHub repository, which is listed in allowed_urls. This qualifies as a paper-specific public code/data asset for the phenotyping analysis.Code · publicsity of Jerusalem. This research was supported by the Chief
Scientist of the Israeli Ministry of Agriculture and Food Secu-
rity (grant no. 12–01-0056) and the Israeli Council for Higher
Education (Project: Future Crops for Carbon Farming).
Data availability The code and supporting data for this study
are publicly available at:
https://github.com/emmaiyke/ERT_RWU_Wheat_Project
Additional datasets are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Open Access This article isOpen asset ↗ERT_RWU_Wheat_Project · emmaiyke/ERT_RWU_Wheat_Projectpdf-raw-page:22 lines:1-95Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
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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constraints.
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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 confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
Existing conversational plant-phenotyping platforms are difficult for plant scientists to use and lack the reliability scientific research demands: failed analyses are reported as valid measurements rather than flagged as missing, statistical tests run without checking assumptions, predictions carry no uncertainty estimate, and specialised hardware limits accessibility. We present PhenoIntel, a lifecycle-aligned multi-agent web platform that turns the full machine-learning workflow into a reliable, user-friendly phenotyping system. Nine specialised agents divide the analysis into stages, from image collection through model selection, inference, and reporting, rather than handing the whole task to one AI manager. Independent checks separate these stages, and every agent reads from and writes to one shared, fixed-structure record, so an inconsistent output from one stage is caught before it reaches the next. Uncertainty is matched to each model family, conformal prediction, detection-confidence spread, or Monte Carlo Dropout, rather than applied uniformly, and quality thresholds adapt to crop and task instead of one global cutoff. When no suitable model exists, PhenoIntel can propose, validate, and integrate a new one on its own. The model repository spans ten trained models across five crops and four imaging modalities. Classification models reach Macro F1 of 0.78-0.996; object-detection models reach 0.96 mAP@50 with a 54% reduction in counting error over an unoptimised baseline; and a temporal model reaches held-out Macro F1 of 0.7050. PhenoIntel runs in a browser on standard hardware, requiring no GPU, and a 1,200-test automated suite confirms complete pipeline execution. Every result carries calibrated uncertainty, validated statistics, and FAIR-compliant provenance, a combination existing conversational phenotyping tools do not offer.
Why it matches plant phenotyping methods植物フェノタイピングの画像収集から推論・報告までを扱うウェブプラットフォームを開発し、複数モデル、精度、不確実性、検証スイートを評価しており、方法が研究の中心である。
abstractWe present PhenoIntel, a lifecycle-aligned multi-agent web platform that turns the full machine-learning workflow into a reliable, user-friendly phenotyping system.
Reproduction assets found論文固有の解析コードとモデル資産を公開するGitHubリポジトリを本文中の根拠とともに確認しました。Code · publiccode, model checkpoints, and the 1,200-test automated suite referenced
throughout this paper are maintained in a version-controlled
repository, available at
https://github.com/Naren1704/PhenoIntel-InternshipOpen asset ↗Naren1704/PhenoIntel-Internshiplines:2047-2163Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract. In the face of escalating global food demand and increasing climate variability, precise and granular crop yield monitoring is indispensable for maintaining regional agricultural stability. However, current deep learning approaches for yield estimation are severely constrained by their heavy reliance on massive in situ labeled data, which limits their application in data-scarce regions. Furthermore, these models often overlook the essential temporal evolution logic of yield formation and lack a systematic discussion regarding the contribution patterns of different feature dimensions, resulting in a black-box nature of the underlying model mechanisms. To address these challenges, this study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning. The framework's core strength lies in a physiologically complete simulation database, using the WOFOST model to exhaustively cover 30 years of climate variability and habitat combinations across Northeast China (1.24 × 106 km2). A Gated Recurrent Unit (GRU) network was then introduced for end-to-end modeling, accurately capturing the energy accumulation trajectory from vegetative to reproductive growth. Validation against 458 independent ground points (2022–2024) demonstrated robust generalization with an R2 of 0.69, an RMSE of 1.21 t ha−1, and an RRMSE of 13.73 %, despite using no ground data for training. Our analysis revealed that integrating photosynthetic intensity (LAImean), duration (LAD) and peak features (LAImax) across growth stages is critical for accuracy, while omitting early-stage features significantly impairs the model's ability to capture cumulative growth effects. Furthermore, the model successfully captured the spatiotemporal yield anomalies caused by the 2023 typhoon and flooding events. Ultimately, this study generated a 10 m resolution maize yield dataset (2019–2024) for Northeast China. The dataset exhibits consistent interannual stability, with the RRMSE ranging from 7.98 % to 12.92 % and the R2 remaining above 0.44 at the city level. By deeply coupling mechanistic simulation with data mining, this dataset provides detailed support for optimizing agricultural production and guiding farming practices. The Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).
Why it matches plant phenotyping methodsトウモロコシ収量という植物・作物群落の形質を推定する計算フレームワークを開発し、独立地点で性能検証したうえで再利用可能な10 m解像度データセットを生成しており、単なる農業実験の routine measurement ではない。
abstractthis study proposes a field-label-free training framework for maize yield estimation that couples mechanistic model with deep learning.
Reproduction assets foundThe paper's core output, the NortheastChinaMaizeYield10m maize yield dataset (2019–2024) with accompanying uncertainty layers, is openly deposited on Zenodo with an explicit availability statement and DOI. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe Northeast China Maize Yield 10 m dataset is openly available at https://doi.org/10.5281/zenodo.19547014 (Hu et al., 2026).Open asset ↗Zenodo · 10.5281/zenodo.19547014lines:158-191Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
Reliable and objective phenotyping is essential for plant breeding programs to characterize genetic variation and accelerate crop improvement. Conventional disease assessment relies on expert visual scoring, which is labor-intensive, subjective, and prone to inter- and intra-rater variability. Although image-based phenotyping methods have been proposed, many require manual intervention, specialized imaging setups, or single time-point measurements, limiting their ability to capture disease progression over time. Here, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images. The pipeline performs semi-automated leaf and automated pustule segmentation from images acquired in situ , enabling objective disease severity estimation with minimal user intervention and without requiring solid backgrounds or manual leaf manipulation or detachment. By extracting temporal traits, including disease severity trajectories and standardized area under the disease progress curve, the method provides a comprehensive characterization of disease development throughout infection. Association between automated and expert assessments was moderate for stripe rust ( R 2 = 0.58) and strong for leaf rust ( R 2 = 0.85), while expert inter-rater reliability was moderate for both diseases (ICC = 0.675 and 0.800, respectively). The proposed approach establishes a scalable and reproducible framework for longitudinal disease phenotyping in controlled environments, with broad applications in disease resistance screening and crop breeding.
Why it matches plant phenotyping methods画像時系列から植物病害の進展と重症度を抽出する半自動・自動解析パイプラインを開発し、専門家評価との比較で検証しており、表現型取得手法が研究の中心です。
abstractHere, we present a pipeline for longitudinal plant disease phenotyping that quantifies wheat stripe rust and leaf rust progression from time-series images.
Reproduction assets foundThe paper's Code and Data Availability section explicitly states that software and datasets (the phenotyping pipeline and imaging datasets) are publicly available at the authors' GitHub repository and project website, both of which are in the allowed URL list.Code · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗USask-BINFO/greenskeye_analysislines:195-225Dataset · publicSoftware and datasets are available at:
https://github.com/USask-BINFO/greenskeye_analysis and https://greenskeye.usask.ca/speedbreeding/ .Open asset ↗lines:195-225Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Published18 Aug 2026Journal of King Saud University - Computer and Information SciencesCited by 0 · OpenAlex ↗
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R 2 =0.98; MAE=3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = −0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from −2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130292 (CTSI=4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI=0.53) and 90 for Andizhan-60 (CTSI=-2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.
Why it matches plant phenotyping methodsUAV画像と基盤モデルを用いて綿花の開絮を検出・定量し、時系列表現型指標を構築・検証する方法が研究の中心であるため。
abstractwe developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX.
Reproduction assets foundThe paper publicly releases its cotton boll-opening UAV image dataset (3638 patches, 94,774 YOLO-format bounding-box annotations) on GitHub, directly supporting the paper's phenotyping analysis. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicentary information for evaluating cross-scale detection performance and characterizing macroscopic spatial patterns. The 5 m imagery acquired on 18 Sept 2024 is used exclusively for cross-year generalization assessment. All cropped images and the corresponding YOLO-format annotation files have been publicly released on GitHub ( https://github.com/mianchen0529/cotton-boll-dataset/tree/main ) to facilitate further research on cotton phenotyping, agricultural remote sensing, and intelligent analytics.
2.3.
Model construction
2.3.1.
Overall architecture of the DINO-BollGX network
The proposed DINO-BollGX network consists of four stages: image preprocessing, feature extraction, object prediction,Open asset ↗mianchen0529/cotton-boll-datasetlines:63-74Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
leaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants from different accessions grown under different water and nitrogen conditions. From these time series, leaf-level growth dynamics were reconstructed and used to calculate key developmental traits, including phyllochron and relative expansion rates. These measurements were further integrated into the GreenLab model to infer plant-scale parameters such as radiation use efficiency (RUE) and leaf demand parameters. Statistical analyses revealed significant genotype, treatment, and interaction effects on several traits, with stable genotype rankings but contrasted sensitivities to environmental conditions. While whole-rosette growth captured strong and consistent responses, individual leaf contributions to global growth remained limited, highlighting the integrative nature of rosette-scale dynamics. Model-derived parameters provided additional insight into plant strategies, showing that genotypes differ in both the duration and timing of leaf demand, with generally stable patterns across environments. In parallel, QTL mapping was performed using leaf-level measurements and phyllochron traits. This analysis identified shared and trait-specific genetic loci, including loci associated with leaf emergence dynamics that were not detected using traditional rosette-scale integrative traits. Overall, this study demonstrates that combining deep learning-based image analysis with mechanistic modeling enables large-scale and quantitative characterization of plant development, providing biologically interpretable traits and new insights into their genetic and environmental determinism. It highlights the potential of combining high-throughput image-based phenotyping, deep learning, and mechanistic modeling to generate biologically interpretable traits and to assess genetic and environmental influences on these traits.
Why it matches plant phenotyping methods深層学習による葉の自動セグメンテーション・追跡を開発的に適用し、時系列画像から葉レベルおよび植物体レベルの発達形質を定量化しているため、表現型取得・抽出が研究の中心である。
abstractleaf development was tracked dynamically from high-throughput phenotyping image series using the SAM-2 deep learning model, enabling automated segmentation and tracking of individual leaves across hundreds of plants
Reproduction assets foundThe paper explicitly states that the authors' leaf segmentation software (AraLeaf_segmentation), the GreenLab Arabidopsis model (AraGreenlab), and the statistical analysis code (AraParameters_statistical_analyses) are open-source and publicly hosted on forge.inrae.fr, and that the data and results used in the paper areCode · publicThe leaf segmentation software (AraLeaf_segmentation) and the GreenLab model of Arabidopsis thaliana (AraGreenlab) are open-source software, and distributed under the GNU GPL v3 licence. The two software packages, and the code to run statistical analyses (AraParameters_statistical_analyses) are available at:https://forge.inrae.fr/phenoscope-public/plant_phenomics_suppmat.Open asset ↗phenoscope-public/plant_phenomics_suppmathtml-lines:419-444Code / 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 confirmedOpenAlex · checked 14 Sept 2026
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Accurate identification of individual plants from unmanned aerial vehicle (UAV) imagery is essential for high-throughput phenotyping and data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction. The pipeline integrates UAV image processing, user-guided annotation of selected undistorted images, convolutional neural network–based object-detection training, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. This workflow preserves native image geometry during detection while maintaining coordinate traceability from source imagery to georeferenced outputs. Across five independent training runs using early-season maize imagery, MatchPlant achieved source-image-level detection performance of AP@0.5 = 90.3 ± 1.1% and mAP@0.5:0.95 = 43.4 ± 2.9%. Orthomosaic-level evaluation after forward projection showed AP@0.5 = 89.7 ± 0.8% and recall = 93.7 ± 1.2%, demonstrating the workflow’s ability to transfer plant detections into georeferenced outputs. Plant-level traits, including plant height derived from canopy height models and NDVI derived from vegetation index rasters, showed strong agreement with manual annotations ( r = 0.87–0.97). Detection outputs were reused across time points with minimal additional annotation, supporting temporal phenotyping during early growth. The framework was validated using maize imagery from a single site and growing season, where plant separation remained clear. By combining modular design, reproducibility, and coordinate traceability, MatchPlant provides an open-source workflow for UAV-based plant-level analysis, with broader applications requiring validation across additional crops, sensors, growth stages, GSDs, and field conditions.
Why it matches plant phenotyping methodsUAV画像から個体検出と植物形質（草高・NDVI）を抽出する、オープンソースの再利用可能なワークフローを開発・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 study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction.
Reproduction assets foundThe paper's MatchPlant analysis pipeline is publicly available on GitHub, and the maize case-study training dataset and pre-trained model are publicly available on Zenodo; both are paper-specific, public, and actionable.Dataset · publicThe public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Model / weights · publicThe training dataset and pre-trained model used in the maize case study presented in Section 3 are also publicly available via Zenodo ( Sangjan et al., 2025a ) at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:70-82Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024) over the core winter rapeseed production region of the Middle and Lower Yangtze River Plain in China. The data originate from systematic observations at 50 national-level agrometeorological stations across six provinces: Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan. The dataset provides complete records of the specific dates for each phenology stage from sowing to maturity, including eight key phenology periods: Sowing (SO), Emergence (EM), Five-leaf (FV), Bud Formation (BF), Stem Elongation (SE), Flowering (FL), Green Ripening (GR), and Maturity (MA), along with the calculated durations of six distinct growth lengths. We implemented a multi-level quality control protocol encompassing internal logical checks, statistical outlier detection, climatological validation, time series homogenization, and expert arbitration. This protocol effectively constrained data uncertainty and corrected non-climatic discontinuities. Univariate linear regression was further employed to quantify the decadal change trends of each phenology period and growth length, supplemented by Kernel Density Estimation (KDE) to characterize their probability distribution features. The final dataset is presented as structured tables (in xlsx format) and high-resolution diagnostic plots (including trend and density plots), with a total volume of approximately 470 MB, systematically organized by province and station. This dataset fills a critical gap in long-term, standardized rapeseed phenology data for the region. The integrated analysis of phenology dates, growth stage durations, and their trends across the entire network provides an indispensable, high-quality empirical foundation. It is designed to support in-depth investigations into the nonlinear response mechanisms of overwintering crops to climate warming, improve crop model parameterization and validation, and inform regional adaptive management strategies.
Why it matches plant phenotyping methods44年間のナタネの生育段階日を標準化・品質管理して公開するデータセット研究であり、植物状態（フェノロジー）の測定データ整備が中心です。
abstractThis study compiles and releases the first standardized rapeseed phenology observation dataset spanning forty-four years (1981-2024)
Reproduction assets foundThe paper's rapeseed phenology dataset (1981–2024, 50 stations) is openly deposited in Science Data Bank under DOI 10.57760/sciencedb.34086, containing structured xlsx tables and diagnostic plots. No custom code was created per the authors.Dataset · publicThe dataset described in this work has been deposited in the Science Data Bank (ScienceDB) under
accession code https://doi.org/10.57760/sciencedb.34086 [27].Open asset ↗Science Data Bank · 10.57760/sciencedb.34086pdf-page:12 lines:1-68Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Early crop establishment strongly influences plant performance and yield, making seedling emergence an important trait in crop phenotyping, breeding, and stress physiology studies. However, emergence monitoring is still commonly performed manually and typically records only the final emergence percentage, limiting the analysis to other dynamic observations. Automated image-based approaches are promising but remain challenging due to the small size of plant structures, heterogeneous soil backgrounds, and variability across imaging systems. Here, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction. The system integrates instance segmentation, object-detection–based data reduction, and temporal deep learning to estimate the emergence time of individual seedlings from RGB image sequences. The pipeline then automatically reconstructs emergence curves and extracts associated traits, including final emergence percentage, EC50, and emergence synchronicity. SPROUT was developed and evaluated using barley and wheat datasets acquired with different RGB cameras under controlled growth-chamber conditions. In the development and retraining settings, the best-performing TCN model achieved 90.0% per-well accuracy with a ± 2h tolerance, supporting accurate emergence curve reconstruction. In an independent inference-only dataset, the model still captured approximate emergence dynamics, although accuracy decreased to 59.3%, indicating that SPROUT is best used as a modular pipeline that can be retrained or fine-tuned for new crop, camera, or experimental domains. A cadmium-stress case study in two contrasting wheat genotypes showed that SPROUT-derived traits captured genotype-specific establishment strategies associated with growth and metabolic responses.
Why it matches plant phenotyping methodsRGB時系列画像から出芽動態を推定し、出芽率・EC50・同時性などの形質を抽出するパイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present SPROUT (AI- based S eedling PR edicti O n and trait extraction U sing RGB T ime-series), a low-cost RGB imaging pipeline for automated prediction of crop emergence dynamics and trait extraction.
Reproduction assets foundThe paper's SPROUT emergence-prediction pipeline code is publicly available on GitHub with explicit availability language, and raw images plus morphology/metabolic data are deposited on Zenodo (10.5281/zenodo.18889863). The GitHub URL is in allowed_urls; the Zenodo DOI is not, so only the code asset is listed as an ad-Code · publiccan be found online at https://doi.org/10.1016/j.compag.2026.112184.Data availability
The raw images and raw data for the morphology and metabolic
profiling on the case study are available in ZENODO (10.5281/zen
odo.18889863), and the code for the machine learning pipeline and
emergence curve analysis are available on GitHub (https://github.com/kit-pef-czu-cz/sprout-emergence-prediction).References
Albarenque, S., Basso, B., Davidson, O., Maestrini, B., Melchiori, R., 2023. Plant
emergence and maize (Zea mays L.) yield across multiple farmers’ fields. Field Crops
Res. 302. https://doi.org/10.1016/j.fcr.2023.109090.Arsovski, A.A., Galstyan, A., Guseman, J.M., Nemhauser, J.L., 2012.
PhotomorpOpen asset ↗kit-pef-czu-cz/sprout-emergence-predictionpdf-raw-page:13 lines:78-112Code / 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 confirmedEurope PMC · checked 5 Sept 2026
Spatio-Temporal Fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, covering the entire soybean growing season from late June to early November. Near-daily Planet SuperDove imagery with 3 m resolution was used to temporally enhance UAV images, which were acquired at 0.05 m resolution but only at weekly to monthly intervals. Through the downscaling process, the UAV data were converted into a daily dataset with a target spatial resolution of 0.5 m. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms- Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (Fit-FC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)-within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) trajectories, from which the NDVI-based Vegetation Growth Metrics (VGM)85 and the EVI-based VGMmax were derived. The validation results indicated that ESTARFM achieved the highest NDVI performance among the evaluated algorithms, with a Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697. CACAO post-processing further improved these results, with CA-ESTARFM achieving an RMSE of 0.108 and a UIQI of 0.740, corresponding to a 4.4% reduction in RMSE and a 6.2% improvement in UIQI relative to the baseline ESTARFM. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using NDVI-based VGM85 and EVI-based VGMmax showed that CA-ESTARFM remained consistent with simple linear interpolation of UAV observations while retaining finer spatial structure and reducing localized noise in the derived growth metrics. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methodsUAV・衛星画像の時空間融合とCACAO処理により、NDVI/EVIおよび植生成長指標を抽出するワークフローを開発・比較検証しており、植物状態の取得手法が中心である。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Reproduction assets foundThe paper's STF/CACAO analysis code is openly available on Zenodo. The underlying Planet/UAV imagery data are only available from the corresponding author upon request, so they qualify as request_only.Code · publicThe code supporting this study is openly available at Zenodo
(https://doi.org/10.5281/zenodo.20923823).Open asset ↗Zenodo · 10.5281/zenodo.20923823pdf-page:20 lines:1-70Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wild-relative introgression broadens wheat diversity, as exemplified by the Multiple Synthetic Derivatives (MSD) population, a unique hexaploid wheat resource capturing extensive genetic diversity from Aegilops tauschii. However, root system architecture (RSA), a key determinant of resource acquisition and stress adaptation, remains poorly characterized in this population. Here, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress. Using this framework we evaluated MSD417 as a representative genotype against its recurrent parent, Norin 61 (N61). Under control conditions, MSD417 displayed greater total root length, root system width, and convex hull area than N61 (p < 0.001), indicating enhanced early root vigor. MSD417 also exhibited larger second pair seminal root angle (p < 0.001) and length (p < 0.01) across both conditions, suggesting enhanced horizontal root exploration while maintaining similar rooting depth to N61 (p = 0.981). Heat stress reduced overall root growth and narrowed genotypic differences, limiting RSA expression. Microscopic observations revealed a lower coleorhiza height-to-width ratio in MSD417. These findings demonstrate the effectiveness of the two-dimensional platform for early-stage RSA phenotyping and highlight Aegilops tauschii-derived germplasm as a source of favorable root traits in wheat breeding.
Why it matches plant phenotyping methods二次元画像による根系構造フェノタイピング基盤を構築し、連続撮像で根形質を追跡する方法が研究の中心であるため含める。
abstractHere, we established a practical two-dimensional root phenotyping framework that enables continuous imaging to track RSA traits and their responses to heat stress.
Reproduction assets foundThe paper's data availability statement deposits the paper-specific phenotyping inputs publicly on Zenodo: root images of wheat N61 and MSD417 (the two genotypes measured for RSA traits) and microscopic coleorhiza images. These are public, paper-specific image datasets directly underlying the study's measurements. No作者Dataset · publical development in arid regions.
ORCID
Sultan Md Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., DiOpen asset ↗Zenodo · 10.5281/zenodo.18080159pdf-raw-page:14 lines:1-49Dataset · publicMd Monwarul Islam http://orcid.org/0009-0002-7219-2104
Izzat Sidahmed Ali Tahir http://orcid.org/0000-0002-1711-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy,Open asset ↗Zenodo · 10.5281/zenodo.18079748pdf-raw-page:14 lines:1-49Dataset · public-6961
Kinya Akashi http://orcid.org/0000-0002-9991-5766
Data availability statement
The root images of wheat N61 and MSD417 are deposited in the Zenodo data repository under https://doi.org/10.5281/zenodo.18080159 and https://doi.org/10.5281/zenodo.18079748, respectively. The microscopic images of
coleorhiza are deposited under https://doi.org/10.5281/zenodo.18091131. The other original contributions presented
in the study are included in the article and/or supplementary material.
References
Alahmad, S., El Hassouni, K., Bassi, F. M., Dinglasan, E., Youssef, C., Quarry, G., Aksoy, A., Mazzucotelli, E., Juhász, A.,
Able, J. A., Christopher, J., Voss-Fels, K. P., & Hickey, L. T. (2019). A majOpen asset ↗Zenodo · 10.5281/zenodo.18091131pdf-raw-page:14 lines:1-49Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · Crossref · checked 15 Sept 2026
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
R ! .
Because the fitted plane passes through the centroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Code / 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 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 14 Sept 2026
Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll measurements, it is destructive and temporally limited, whereas portable optical meters such as the CCM-300 enable rapid, non-destructive measurement of the chlorophyll fluorescence ratio (CFR) but require species- and season-specific calibration. This study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom. Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. RF regression achieved the highest predictive performance within the calibration dataset, although substantial uncertainty remained at the leaf level; a simple linear model was therefore adopted for cross-year projection due to its stability under extrapolation. Applying this calibration to daily 2022 CFR measurements generated a continuous "virtual acetone" trajectory, enabling qualitative comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines; however, senescence, defined as the initiation of sustained post-peak decline, occurred earlier during the warmer and drier 2022 season. Mixed-effects modelling identified positive effects of temperature and wind speed on CFR in 2022, while generalised additive modelling of the 2023 dataset revealed a non-linear seasonal decline under comparatively mild conditions. Because cross-year projections rely on a low-fit linear calibration, interannual differences are interpreted primarily in terms of relative seasonal trajectory shape and timing rather than absolute chlorophyll magnitude.
Why it matches plant phenotyping methodsCCM-300による葉クロロフィル測定を破壊的測定と比較し、校正モデルの開発・性能評価と季節軌跡の再構築を行っており、植物表現型取得法が研究の中心である。
abstractThis study evaluates the performance of CCM-300 measurements and reconstructs seasonal chlorophyll dynamics in field maple (Acer campestre) across two contrasting summers in the United Kingdom.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicData Availability: Data used in the study can be accessed via https://zenodo.org/records/17475985.Open asset ↗zenodo · 17475985pdf-page:11 lines:1-44Code / dataset availability 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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under BioProject PRJNA1452908 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1452908)
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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 · bioRxiv · checked 5 Sept 2026
Abstract Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.
Why it matches plant phenotyping methods長期マルチセンサー画像による水ストレス応答の表現型取得と、AIによるフェーズ分類・指標抽出が研究の中心であり、3D-QTLVisも開発している。
abstractHigh-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations
Reproduction assets foundThe paper's authors publicly release their GWAS Interaction model R scripts and the 3D-QTLVis Shiny visualization tool on GitHub; no public phenotype dataset or trained model deposit is stated (phenotypic data only as summary statistics in supplements).Code · publicCode used for running the GWAS interaction model in R and the 3D-QTLVis tool are available at https://github.com/Walshj73/3D-QTLVis .Open asset ↗Walshj73/3D-QTLVislines:216-267Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafGrowth / time-series analysisGrowth / development / phenology
Disentangling genotype × environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology and environment. Here, we integrated time-resolved measurements of apical development, final leaf number (FLN), and expression of the flowering-time genes VRN1, VRN2 and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range of developmental sensitivities. By combining controlled-environment phenotyping with concurrent gene-expression profiling, we show that environmentally driven variation in FLN is coherently explained by shifts in the timing of key apical transitions and associated VRN gene-expression dynamics. These integrated datasets were used to parameterise and interrogate the Cereal Anthesis Molecular Phenology (CAMP) model, enabling direct comparison between observed foliar gene-expression time courses and modelled gene activity. While overall developmental responses were well captured by the model, systematic differences between observed and modelled gene-expression patterns highlight the importance of distinguishing foliar expression from apical regulatory activity, as well as differences in temporal scaling. Building on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Why it matches plant phenotyping methodsFLN応答に基づくフェノタイピングプロトコルを提示し、温度・光周期処理下で遺伝的に解釈可能な発育表現型を取得する方法が中心的に扱われている。
abstractBuilding on this framework, we present a phenotyping protocol based on FLN responses to defined temperature and photoperiod treatments that delivers unconfounded developmental phenotypes explicitly linked to underlying genetic regulation.
Reproduction assets foundThe paper's CAMP model code and the analysis scripts producing its figures are explicitly stated as publicly available on the authors' GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicwere also validated and the best-performing sets selected. A
348 description of each of the primers used in this study is given in the supplementary material
349 (Table SA1).
350 2.9 Verification of CAMP predictions
351 2.9.1 Model set-up and operation.
352 The CAMP model was coded into a Python script which is available at
353 https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
354 description of the code and parameterisation scheme is given in the supplementary material.
355 The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
356 derive the Vrn expression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicpression parameters needed for CAMP. Each of the treatments was
357 simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
358 Vrn gene expression could be compared with those observed. The script running the CAMP
359 code and producing the graphs displayed in this paper can be viewed at
360 https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.
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UNOFFICIALOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:14 lines:1-49Code · publicnd testing of the model in
690 broader contexts. EW contributed substantially to the improvement of model concepts and the
691 manuscript and all authors provided final checking.
692 8. Data Availability
693 All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
694 publicly available at https://github.com/HamishBrownPFR/CAMP/
695 9. References
696 Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
697 response of wheat vernalization to environmental variables indicates that vernalization is not
698 a response to cold temperature. Journal of Experimental Botany 63: 847–857.
699 Baumont M, Parent B, Manceau L, Brown HE,Open asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-layout-page:31 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
Modern plant phenotyping faces the challenge of interpreting complex, high-dimensional data. Traditional analytical tools often fail to capture the non-linear, hierarchical, and temporal relationships that define plant responses under multifactorial conditions. We present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space. Unlike conventional Euclidean approaches, HTDA-Mapper preserves the hierarchical structure of phenotypic traits, improves cluster resolution, and reveals hidden growth trajectories across treatments and time, offering a powerful means to explore latent phenoms. The pipeline supports both quantitative data and images. When integrated with unsupervised contrastive learning, HTDA-Mapper identifies similarities and differences in raw image data without requiring manual labelling or post hoc processing. We applied this framework to a high-throughput phenotyping (HTP) dataset of over 27,000 images of Arabidopsis thaliana seedlings exposed to varying nutrient levels and priming agents at different concentrations over seven days. Using cubical complexes, HTDA-Mapper mapped relationships between treatment variables, compound concentrations, and phenotypic outcomes. Furthermore, it reliably detected compound-specific effects, uncovered dynamic trait–environment interactions, revealed phenotypic trajectories not captured by conventional methods, and facilitated biologically meaningful interpretation of the complex dataset. By preserving the geometry and temporal evolution of plant development, HTDA-Mapper sets a new standard for HTP analysis. Beyond phenomics, it is a versatile tool for other omics, such as transcriptomics and metabolomics, where structured, high-dimensional data is prevalent. HTDA-Mapper can accelerate data-driven crop improvement by uncovering effective compounds, robust genotypes, and adaptive growth strategies that enhance plant resilience.
Why it matches plant phenotyping methods植物フェノミクスの高次元画像・形質データを解析するHTDA-Mapperアルゴリズムを開発し、27,000枚超の植物画像データで適用・評価しているため、解析手法が中心的である。
abstractWe present the Hyperbolic Topological Data Analysis Mapper (HTDA-Mapper), a novel algorithm designed to overcome these limitations by embedding data in Poincaré ball space.
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 · publicUpon acceptance, the codes and all material used in this research will be freely available at HYPERLINK: https://github.com/JZdrazilX/MML and data at ZENODO: 10.5281/zenodo.17952279.Open asset ↗JZdrazilX/MMLhtml-lines:222-260Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi-based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.
Why it matches plant phenotyping methods植物の生理的ストレスと成長軌跡を、視覚的植物指数およびIoTセンサーデータから推定するGNNベースの監視・解析手法が研究の中心であり、植物表現型取得と予測に該当する。
abstractThe OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure.
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle lettuce growth dataset used for evaluation, matching an allowed URL. No author code or model checkpoints are disclosed.Dataset · publicThe datasets used and/or analyzed during the current study are available in the Kaggle repository, https://www.kaggle.com/datasets/jurijsruko/lettuce/data.Open asset ↗Kaggle · jurijsruko/lettucehtml-lines:469-500Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding cellular growth dynamics in plants requires precise, long-term imaging of developing tissues. Cauline leaves are produced during the transition from vegetative to reproductive development and provide a useful system for studying how laminar organs diversify in form and function. While other laminar organs, such as rosette leaves and sepals, have been extensively studied, early cauline leaf development remains technically challenging to capture due to their concealed position, curved morphology, and the presence of dense trichomes. Here, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana . This method enables reproducible, high-resolution imaging of cauline leaves, supporting robust quantitative analysis of growth across developmental stages at cellular scale resolution. Key features • Fine dissection method for exposing initiating cauline leaves in Arabidopsis thaliana . • Long-term confocal live imaging of cauline leaf development at cellular resolution. • Optimized imaging parameters for high-fidelity 2.5D segmentation and growth analysis in MorphoGraphX.
Why it matches plant phenotyping methodsカウリン葉の成長を細胞レベルで定量化するための解剖、共焦点イメージング、2.5Dセグメンテーション、画像解析パイプラインが中心的に開発・提示されている。
abstractHere, we provide a complete pipeline for the dissection, confocal imaging, 2.5D segmentation, and image analysis of initiating cauline leaves in Arabidopsis thaliana .
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 · public2. MorphoGraphX 2.0.1 ( https://morphographx.org/software/ ) (access date, 2026-02-26) [10–11]
3. All codes have been deposited to OSF: https://osf.io/uth78/ (access date, 2026-02-26)
Procedure
A. Plant growth
1. Sow the seeds in pots filled with moist, room-temperature soil. Add a layer of water to the bottom of the tray and cover with a lid to maintain high humidity.
Note: Space seeds sufficiently to avoid contact between the developing plants and to prevent leaf damage; typicallyOpen asset ↗OSFlines:109-143Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Quantifying the kinetics of net CO2 assimilation (A) and stomatal conductance (gs) under fluctuating light typically relies on gas exchange measurements, which are slow and thus unsuited for high-throughput phenotyping. As a result, faster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale. However, first the relationship between non-steady-state parameters must be examined in greater detail. In this study, we aimed to determine whether variations in non-steady-state values of chlorophyll fluorescence and leaf temperature reflect differences in key gas exchange traits under fluctuating light conditions. Here, the correlations between the times required for a change in non-steady-state A, gs, operating efficiency of PSII (ΦPSII), and leaf temperature (Tleaf) during stepwise changes in light intensity were evaluated across nine plant species. Both steady-state and non-steady-state photosynthetic traits varied significantly among species. Overall, we found significant positive correlations between non-steady-state A and ΦPSII for time to 50% and 90% of final steady-state values (t50; r2 = 0.70) and (t90; r2 = 0.33). The t90 of gs and that of Tleaf were also significantly correlated after both increases (r2 = 0.45) and decreases (r2 = 0.61) in light intensity. Our findings suggest that the times required for a change in ΦPSII (particularly t50) and Tleaf (particularly t90) can be used as indicators of dynamic A and gs, respectively, facilitating faster phenotyping of the complex processes of photosynthesis and stomatal conductance kinetics in the future.
Why it matches plant phenotyping methods非定常クロロフィル蛍光と葉温を用いて光合成・気孔コンダクタンス動態を推定する高速フェノタイピング手法を評価しており、相関検証が研究の中心である。
abstractfaster, non-invasive phenotyping methods are needed to further evaluate these traits at a larger scale.
Reproduction assets foundThe paper's primary gas exchange, chlorophyll fluorescence, and leaf temperature phenotyping data are explicitly deposited in the WUR data repository (DOI 10.17887/WUR01-TMWYJN), stated in the Data availability section. No author analysis code repository is stated; the agricolae R package is a generic library, not a论文-Dataset · publicThe primary data and associated metadata are publicly available through the WUR data repository at https://doi.org/10.17887/WUR01-TMWYJN .Open asset ↗WUR data repository · 10.17887/WUR01-TMWYJNlines:406-446Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Climate change threatens global Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production, a cool-season crop essential for Asian markets. With optimal growth at 18-20°C and severe disruption above 25°C, developing heat-resilient varieties is critical. This study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes. Seedlings were subjected to heat stress (setpoint 40/35°C day/night; measured 35.7/31.5°C day/night air temperature) or controls (setpoint 25/20°C day/night; measured 25.0/17.7°C day/night air temperature) for 14 days, with continuous non-destructive monitoring of 14 morphological and spectral parameters using PlantEye F600 multispectral 3D scanner. Principal component analysis of temporal phenotyping data explained 62-68% of variance, enabling quantitative assessment of phenotypic stability through Euclidean distance measurements in PC space. Temporal analysis revealed crop-specific response patterns with maximum treatment separation at 3 days after treatment (DAT) (ΔC=3.27), reflecting Chinese cabbage’s rapid heat sensitivity as a cool-season crop, followed by progressive acclimation by 14 DAT (ΔC=1.41). Early responses (3-5 DAT) were dominated by morphological parameters, transitioning to physiological adjustments (10-14 DAT) characterized by spectral indices. Under heat stress, plants prioritized evaporative cooling through increased transpiration (four-fold increase) over carbon assimilation. A critical finding was the disproportionately greater reduction in root biomass relative to shoot biomass under to heat stress, with root biomass declining 38-47% versus 20% in shoots. Strong correlations (r>0.8) between 3D imaging parameters and destructive biomass measurements validated the non-destructive approach’s reliability. Notably, image-based root surface area analysis correlated strongly with actual root biomass (R 2 =0.698, p<0.001), enabling practical assessment of root area without conventional destructive processing. Based on integration of phenotypic stability (Euclidean distances in PC space) and biomass production under heat stress, this approach identified four distinct heat tolerance strategies: stable-productive genotypes (ideal breeding targets combining phenotypic stability with high heat-stress biomass production), stable-conservative genotypes (phenotypic stability with lower production), plastic-productive genotypes (substantial phenotypic changes yet high biomass production), and plastic-sensitive genotypes (phenotypically unstable and poor biomass production). This validated framework accelerates heat-tolerant Chinese cabbage breeding through efficient high-throughput phenotyping, enabling targeted genotype selection for diverse production environments facing climate warming.
Why it matches plant phenotyping methods3Dマルチスペクトルスキャナによる非破壊・時系列表現型取得と、その解析・検証が研究の中心であり、熱ストレス下の形態・生理形質を定量化する実質的なハイスループット表現型解析研究である。
abstractThis study integrated high-throughput 3D multispectral phenotyping with multivariate analysis to characterize temporal heat stress responses in 18 Chinese cabbage genotypes.
Reproduction assets foundThe paper states its collected phenotyping data are available in the supplementary material hosted with the article (open access under CC BY-NC-ND), making the paper-specific phenotype dataset publicly actionable via the article DOI. The analysis code, however, is only available from the corresponding author uponReasonDataset · publichrough field phenotyping.) between RDA and the World Vegetable Center (WorldVeg)” and by the long-term strategic donors to the WorldVeg: Taiwan, the United States, Australia, the United Kingdom, Germany, Thailand, South Korea, Philippines, and Japan.
Footnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100221 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article.
Multimedia component 1
Data availability
The data collected and used in this study are available in the supplementary material. The code used for analysis can be obtained from the corresponding author upon reasonable request.
ReferenceOpen asset ↗lines:486-514Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Crop phenology is a critical determinant for yield prediction and germplasm evaluation. However, precise phenological monitoring in large-scale rice breeding trials faces significant challenges due to the inherent phenological asynchrony among hundreds of cultivars and the trade-off between spatial resolution and temporal continuity in unmanned aerial vehicle (UAV) remote sensing. To address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement. We introduce a Missing Aware Gated Fusion (MAGF) mechanism to dynamically integrate multi-resolution features on non-aligned timelines, enabling robust modeling under irregular sampling conditions. Validated on a massive dataset covering approximately 500 rice cultivars and over 100,000 images across 2023 and 2024 growing seasons, the proposed method significantly outperformed single-temporal-scale baselines despite multiple growth stages coexisting within the same dates. The integration of multi-spatial-scale fusion with LSTM temporal modeling yielded superior performance considering efficiency, achieving an Overall Accuracy (OA) and F1-score of 0.873, with a Kappa coefficient of 0.84. A hybrid sampling strategy (daily MR image combined with weekly HR image) demonstrates that weekly flight time can be reduced from 28 h to approximately 6 h while maintaining high accuracy. Notably, even when HR acquisition was reduced to a once every 14 days frequency, the fusion performance remained significantly superior to that of daily MR monitoring alone. The model exhibited strong generalization capabilities. When directly applying the model trained on 2024 data to the 2023 dataset, it maintained an OA of 0.774 and an F1-score of 0.738 under a 3-day error tolerance, with recall for the maturity stage consistently exceeding 0.96. This framework offers a flexible, scalable, and cost-effective solution for high-throughput phenotyping in precision breeding.
Why it matches plant phenotyping methodsUAVリモートセンシング画像と深層学習によるイネの生育ステージ（フェノロジー）推定手法を開発・検証し、大規模育種データで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。
abstractTo address these issues, this study proposes a multi-scale temporal deep learning framework that integrates high-frequency medium-resolution (MR) images as temporal anchor with sparse high-resolution (HR) images as spatial enhancement.
Reproduction assets foundThe article explicitly states that the authors' source code and test samples for the rice phenology identification framework are publicly available on GitHub, matching an allowed URL. No public dataset deposit is stated; additional data is only on request.Code · publicThe source code and test samples used in this study are publicly available at: https://github.com/gfjiyue/Rice-phenology-identification-by-UAV . Additional data can be made available upon reasonable request.Open asset ↗https://github.com/gfjiyue/Rice-phenology-identification-by-UAVlines:601-709Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. Methods: In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The recorded time-series data were processed using a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Results: Across multiple input time horizons, we found that a 30-minute look-back window strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Importantly, the framework detects transitions from healthy to stressed states in recordings that were not included in the training set. Conclusion: Overall, we provide a decision-support tool for farmers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.
Why it matches plant phenotyping methodsトマトの電気生理シグナルから水ストレス状態を推定するセンシング・機械学習パイプラインを開発し、未学習データで性能検証しているため、植物フェノタイピング手法が中心である。
abstractDirect physiological sensing offers the potential to detect stress responses before visible symptoms appear.
Reproduction assets foundThe paper's electrophysiological time-series and soil moisture measurements from the water-stress tomato experiment are explicitly stated to be publicly available online via a Zenodo deposit (Buss et al. 2026a), referenced in both the Methods and Data availability sections.Dataset · publicAll recorded and
processed data are available online (Buss et al. 2026a).Open asset ↗pdf-page:5 lines:1-37Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Code / dataset availability confirmedOpenAlex · Crossref · checked 5 Sept 2026
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Growth chamberRootMorphology / geometry measurementSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Abstract Root system architecture and root hairs highly influence plant resource uptake, yet their simultaneous quantification at the whole-plant scale remains challenging due to the conflicting requirements of high-resolution imaging and non-destructive, repeated measurements. Here, we present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth. The system produces integrated outputs highlighting both whole-root architecture and the spatial distribution of surrounding root hair area from single images. Validation of the image analysis algorithm showed good segmentation performance, with average Matthews Correlation Coefficient values of 0.68 for root area and 0.65 for root hair area. To demonstrate its experimental applicability, the system was used to assess root growth and root hair responses under controlled environmental conditions, combining three irrigation regimes (2, 4, and 6 irrigation events per day) with three dry bulk density levels (1.4, 1.5, and 1.6 g cm⁻³). In addition to whole-system metrics, the approach enables analysis of root hair expansion at individual root tips. This methodology provides a rapid, scalable, and training-free method for integrated analysis of root architecture and root hairs under controlled physical conditions similar to soil, facilitating studies of root–soil interactions that require both spatial resolution and temporal continuity.
Why it matches plant phenotyping methods根系画像解析システムとRベースの画像分析ワークフローを開発し、根系構造と根毛面積の定量化およびアルゴリズム性能検証を中心に扱っているため、植物フェノタイピング手法として適格です。
abstractwe present a plant growth and imaging system based on A3 sized flatbed rhizotrons, combined with a non–machine-learning image analysis workflow implemented in R, that enables repeated, in situ quantification of cereal root system architecture and root hair area over three weeks of growth.
Reproduction assets foundThe paper's authors state that the RootHairFinder C++ file, R script, and example rhizotron data will be available via a GitHub repository and Zenodo upload, and the preprint's supplementary files already include RootHairFinder.cpp and example rhizotron images (Supplimentaryfile4.tif, Supplimentaryfile5.tif). This is aCode · publicThe RootHairFinder cpp file, R script and example data files for rhizotron analysis will be made available through github https://github.com/TracyValentine/RootHairFinder and https://zenodo.org/uploads/19288922Open asset ↗TracyValentine/RootHairFinderlines:236-263Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.
Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.
Reproduction assets foundThe paper's MRI phenotyping data (~160 GB raw, 1.4 TB processed) is only available upon request, but the authors' processing pipeline (scripts and parameters) is publicly deposited on Zenodo with an explicit URL.Code · publicThe processing pipeline (including scripts and parameters required to reproduce the processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369 .Open asset ↗Zenodo · 10.5281/zenodo.17944369lines:370-484Code / 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 14 Sept 2026
A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency.
Why it matches plant phenotyping methods自動化ハイスループット画像解析により根系形態・成長形質を抽出する表現型取得が研究の主要手段であり、根系構造の実質的な応用解析に該当する。
abstractWe evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled 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。Supplement · publicSupplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants15060935/s1 , Figure S1: Repeatability of image-derived shoot (a) and root traits (b) of the tested 65 maize inbred lines over time.Open asset ↗lines:68-215Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Nominal hours post fertilization (hpf) are widely used to index zebrafish embryogenesis, yet under condition shifts—such as temperature change, genetic perturbation, or environmental stress—nominal time can decouple from true developmental progression. In such settings, biologically meaningful variation is better described as a systematic change in developmental tempo rather than a simple temporal offset. Here we introduce an embryo-resolved framework that treats developmental tempo as the primary quantity of interest in brightfield time-lapse imaging. We present EmbryoTempoFormer (ETF), a clip-based CNN–Transformer that predicts developmental progression from short time-lapse clips and is trained with a within-embryo temporal-difference consistency regularizer to promote temporally coherent trajectories. Crucially, we couple model predictions with an embryo-level inference and statistical workflow: temporally correlated clip-level outputs are aggregated into interpretable embryo-level tempo and stability readouts, and cross-condition effects are quantified using embryo-bootstrap confidence intervals with embryos—rather than frames or clips—as independent units, avoiding pseudo-replication. Using temperature perturbation as a representative domain shift, we robustly quantify condition-induced changes in global developmental dynamics and show that developmental delay predominantly manifests as reduced developmental tempo. This framework enables statistically principled, high-throughput phenotyping for perturbation screens, drug assays, and environmental stress studies. HIGHLIGHTS Clip-based CNN–Transformer predicts developmental time from brightfield time-lapse microscopy. Within-embryo temporal-difference consistency improves trajectory self-consistency. Embryo-level anchored tempo slopes enable interpretable cross-condition comparisons. Reproducible pipeline via code, scripts, and a Zenodo bundle with embryo-level inference Graphical abstract
Why it matches plant phenotyping methodsゼブラフィッシュ胚の発生進行・テンポをタイムラプス画像から推定するCNN–Transformerと、胚単位の統計的推定ワークフローを開発しており、表現型取得・抽出手法が中心である。ただし植物ではなく動物対象のため、この植物フェノタイピング索引では除外すべき内容。
abstractHere we introduce an embryo-resolved framework that treats developmental tempo as the primary quantity of interest in brightfield time-lapse imaging.
Reproduction assets foundThe paper analyzes public zebrafish brightfield time-lapse data (BioImage Archive S-BIAD531) and provides a public GitHub code repository plus a Zenodo reproducibility bundle containing processed arrays, model checkpoints, dataset splits, and checksums. All three are paper-specific, public, and actionable.Code · publicCode repository: https://github.com/LijiayuDeng/s-biad531-embryo-tempoformerOpen asset ↗https://github.com/LijiayuDeng/s-biad531-embryo-tempoformerpdf-page:25 lines:1-52Code / 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
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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 5 Sept 2026
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Code / 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
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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.
•
It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高スループット画像フェノタイピングによる大規模データセットを提示し、画像取得基盤と作物成長動態の解析を中心に扱っているため、方法論文として適格です。
titleFIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper's canopy cover analysis code is publicly available on the authors' ETH GitLab repository. The FIP 1.0 soybean image/trait dataset itself is deposited in the ETH Research Collection and Hugging Face, but those URLs are not among the allowed URLs, so only the code asset qualifies.Code · publicCode availability
The code is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover. Users with similar data can use the implemented workflow to get canopy cover from their experiments.Open asset ↗gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycoverhtml-lines:207-226Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
LiDAR / point cloudWhole plant / canopy / plot / fieldSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
To monitor the growth and structural changes of crop organs, dynamic plant phenotyping based on time-series point clouds has become a cutting-edge research topic. However, existing organ tracking methods based on crop time-series point clouds either rely on complete organ instance segmentation results or lack real-time performance in capturing spatiotemporal correlations among organs. To address these limitations, we propose 3D-OGT: a framework capable of performing continuous organ tracking throughout the entire growth sequence with only the minimal segmentation information. The 3D-OGT framework can automatically propagate organ labels from the previous moment's crop point cloud to the subsequent point cloud, while completing organ segmentation and tracking on multiple crop growth sequences. The framework can recognize and track new organs, mature organs, and even suddenly disappeared organs. Experimental results on a spatiotemporal point cloud dataset demonstrate that 3D-OGT achieves satisfactory organ tracking performance, with an average organ tracking accuracy (TrackAcc) reaching 88.10%, which is superior to three other mainstream methods participating in the comparison.
Why it matches plant phenotyping methods作物器官の3D点群から成長を追跡・分割する手法を開発し、データセット上で他手法と比較検証しており、植物表現型取得が中心です。
abstractwe propose 3D-OGT: a framework capable of performing continuous organ tracking throughout the entire growth sequence with only the minimal segmentation information.
Reproduction assets foundThe paper's data and analysis code are publicly released in the authors' GitHub repository, explicitly stated in the Data availability section. The dataset itself is the public Pheno4D spatiotemporal point cloud dataset, but the paper-specific asset is the authors' code/data repository.Code · publicOur data and code are available at: https://github.com/zingersu/3D-organ-growth-tracking-with-minimum-segmentation.Open asset ↗zingersu/3D-organ-growth-tracking-with-minimum-segmentationhtml-lines:292-314Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Time-resolved phenotyping of disease symptoms enables dissection of resistance mechanisms and improves diagnosis, but acquiring phenotypic data at satisfactory scale remains challenging. Advances in imaging and image processing have improved measurement precision, robustness, and throughput, but further improvements are needed for practical application. We present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms. All images are geometrically aligned with a median precision of 0.16 mm (≈5 pixels). The dataset includes transformation matrices, symptom segmentation masks, metadata on treatments, weather, crop phenology, and disease occurrence, and a lightweight Python toolkit for loading, aligning, inspecting, and editing image sequences. These resources enable detailed investigation of leaf-level disease dynamics such as lesion, pustule, and fruiting body emergence rates, lesion growth, and dynamic interactions of disease development with spatial and environmental contexts. They offer a broad basis for developing improved methods for image alignment and symptom detection, segmentation, and tracking, possibly by tackling these connected challenges within a single end-to-end framework.
Why it matches plant phenotyping methods葉の病徴を対象とした高解像度時系列画像データセットで、幾何位置合わせ、病徴セグメンテーション、追跡用ツールを提供しており、植物病害表現型の取得・解析基盤が中心である。
abstractWe present a data set comprising 12,520 high-resolution (~0.03 mm/pixel) RGB images representing 1,032 time series of wheat leaves with developing disease symptoms.
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 provide a lightweight Python toolkit to facilitate loading, inspection, and curation of the image sequences and their associated processing products in the associated Git repository (https://github.com/and-jonas/sympathique-wheat).Open asset ↗github.com/and-jonas/sympathique-wheathtml-lines:317-337Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l -glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.
Why it matches plant phenotyping methods植物全体の蛍光シグナルをリアルタイム取得する低コスト・オープンな画像プラットフォームを開発し、成体植物で検証しているため、植物表現型取得法が研究の中心です。
abstractHere, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage.
Reproduction assets foundThe authors publicly deposit raw imaging data and MAPPI analysis code on Zenodo, host the MAPPI acquisition/analysis code on GitHub, and release the napari-roi-registration image registration plugin on GitHub. All are paper-specific, public, and actionable.Dataset · publicThe raw data for the images presented in the manuscript and the code to run the MAPPI system are available on Zenodo ( https://doi.org/10.5281/zenodo.15845576 ).Open asset ↗Zenodo · 10.5281/zenodo.15845576lines:170-466Code · publicThe code to run the MAPPI system is also available on the dedicated GitHub repository ( https://github.com/micropolimi/MAPPI ) along with the code used to analyze the data.Open asset ↗GitHub · micropolimi/MAPPIlines:170-466Code · publicThe software is open-source and available on GitHub ( https://github.com/GiorgiaTortora/napari-roi-registration ) and the napari-hub ( www.napari-hub.org/plugins/napari-roi-registration ).Open asset ↗GitHub · GiorgiaTortora/napari-roi-registrationlines:156-169Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / 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 confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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Foundation.
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Data Availability
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Code and data associated with this manuscript are available on GitHub
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(https://github.com/danforthcenter/teff-manuscript).477
478
.
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 version posted January 7, 2026.
;
https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
3
Plant Phenomics Page 26 of 29Open asset ↗zenodo · 10.5281/zenodo.17944369pdf-layout-page:26 lines:1-14Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
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Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Published1 Jan 2026The Plant journal : for cell and molecular biologyCited by 4 · OpenAlex ↗
Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hamper automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers-so long as the cell contours can be identified-are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialists, it is expected to promote understanding of what happens at the sub- and cellular level with high-throughput outcomes.
Why it matches plant phenotyping methods植物細胞のライブ画像から細胞形状・先端位置・成長方向・成長動態を定量化する解析ソフトウェアを開発しており、植物表現型取得・抽出が研究の中心である。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public authors' code repositories (KymoTip analysis tool and SAM2 segmentation code) and a figshare deposit of the raw imaging data used for the tip-growth phenotyping measurements.Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTipOpen asset ↗blues0910/KymoTiphtml-lines:159-244Code · publicthe code for SAM2 segmentation is available at https://github.com/YusukeKimata‐Moo/SAM2‐segmentation/Open asset ↗YusukeKimata‐Moo/SAM2‐segmentationhtml-lines:159-244Dataset · publicThe raw data used in this paper are available on figshare: https://doi.org/10.6084/m9.figshare.30847580Open asset ↗figshare · 10.6084/m9.figshare.30847580html-lines:159-244Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal pores, formed by guard cells, govern the critical trade-off between carbon assimilation and water loss in plants. Their dynamic responses to environmental stresses, such as stomatal oscillations and drought “stress memory” (hysteresis), have lacked a unified mechanistic explanation. While abscisic acid (ABA) is believed to play key roles in water stress responses, no model has linked its core regulatory kinetics to these complex stomatal behaviors. Here, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation. We demonstrate that this framework predicts accurate, genotype-specific stomatal regulation across wildtype, ABA-insensitive mutant ( ost1-3 ), and ABA-synthesis mutant ( aao3-2 ) in Arabidopsis thaliana ( At ) and that non-linear feedbacks in ABA autoregulation can drive both stomatal oscillations and hysteresis. This work unifies genetic, signaling, and membrane processes with leaf-scale physiological dynamics, providing a new predictive foundation for understanding and modulating plant management of water use and water stress.
Why it matches plant phenotyping methods葉の水理とABA制御を統合した予測モデルを開発し、遺伝子型別の気孔コンダクタンス制御を検証しており、植物生理表現型の取得・予測手法が中心である。
abstractHere, we introduce a coupled hydropassive-hydroactive (HP-HA) model that integrates leaf hydraulics with the biokinetics of guard cell-autonomous ABA regulation and plasma membrane-mediated osmoregulation.
Reproduction assets foundThe paper's Code Availability section explicitly archives all MATLAB code used to generate the study's stomatal conductance modeling results in a Zenodo repository (DOI 10.5281/zenodo.17888362) and on GitHub (desai-sahil/sys-bio-gs), both listed as allowed URLs. This is author analysis code directly reproducing the hydCode · publicn analysis are provided in SI sections S5. Comprehensive tables listing all model parameters,
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their sources, and the methodology for parameter fitting are provided in SI section S7.
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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Acknowledgements
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. BeldiOpen asset ↗Zenodo · 10.5281/zenodo.17888362pdf-raw-page:9 lines:1-74Code · publicnd the methodology for parameter fitting are provided in SI section S7.
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All MATLAB code used to generate the results in this study is permanently archived in a Zenodo
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repository at: https://doi.org/10.5281/zenodo.17888362. The most current version of the code is also
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available on GitHub at: https://github.com/desai-sahil/sys-bio-gs.git. Refer to SI section S7.C for details
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on steps to run the code to reproduce the results in main text.
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Acknowledgements
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We thank F. E. Rockwell, V. Bacheva, S. Sen, I. Gabay, E. Wu, J. Belding, and P. Jain for insightful
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discussions. This work was supported by the Center for Research on Programmable Open asset ↗GitHub · desai-sahil/sys-bio-gspdf-raw-page:9 lines:1-74Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract High‐throughput and noninvasive phenotyping methods are promising technology for improving efficiency in plant research and breeding. In this study, we evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice ( Oryza sativa ). The DPS was used to estimate plant height, biomass, color, leaf morphology, and tiller angle in four rice varieties (Koshihikari, Nipponbare, PL9, and Tachiaoba). The results show high accuracy and correlation between manually measured and DPS‐derived traits. Notably, the 3D volume analysis can quantify biomass accumulation and growth dynamics and revealed distinct differences among varieties. The strong correlation between the green‐red normalized difference index (a red‐green‐blue‐based index) and soil plant analysis development also demonstrated the viability of the system in monitoring leaf color without using a multispectral instrument. The analysis also captured growth patterns over time, including canopy development and senescence, which are often challenging to quantify through manual measurements alone. Furthermore, the tiller angle estimation derived from DPS provided an alternative method to plant architecture evaluation, demonstrating its potential for use in breeding programs aimed to optimize canopy structure. These findings establish DPS as a reliable and scalable tool for a digital phenotyping platform that enables comprehensive trait analysis with reduced labor and increased precision and the capability to continuously monitor plant growth and biomass accumulation. This study shows the potential of this novel digital tool for automating manual measurements, which can increase efficiency and expedite research and breeding in rice and other crops.
Why it matches plant phenotyping methods3Dモデル再構築に基づくデジタル表現型解析システムを開発・評価し、イネの複数形質を手測定と比較検証しているため、方法が研究の中心です。
abstractwe evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice
Reproduction assets foundThe paper's data availability statement explicitly says the analysis code is openly available on GitHub at the authors' repository Rice_VTGa.O, which contains the digital phenotyping/leaf-tracing analysis code for this study. No phenotype dataset or image deposit is stated.Code · publicGrant Number 39 [2023] and 38 [2024]),
and Microbiome and Metabolome Control Project, University
of Miyazaki, Japan.
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
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Rice_VTGa.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Kenji Aoki https://orcid.org/0000-0001-7003-1994
MasatsuguHashiguchi https://orcid.org/0000-0003-0637-2780
RyoAkashi https://orcid.org/0000-0002-5651-8285
Yuji Kishima https://orcid.org/0000-0002-0942-3371
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154Open asset ↗https://github.com/sandysan42/Rice_VTGa.O · Rice_VTGa.Opdf-raw-page:13 lines:1-84Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Studying the mechanisms that promote deep rooting in crops is crucial for engineering plant varieties with enhanced drought resilience and increased carbon sequestration capacity. Soil compaction is a major constraint on rooting depth and, to overcome this, root system penetrability needs to be enhanced. However, because of the limitations of current methods, phenotyping root penetrability remains a bottleneck. Here, we developed RootXplorer, a computer vision-based 3D phenotyping platform for high-throughput quantification of root penetration-related traits/phenotypes across dicot and monocot species. RootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale. We demonstrate that RootXplorer enables large-scale diversity screenings in conditions replicating soil compaction effects in multiple species, revealing species-specific strategies for overcoming mechanical impedance. These findings highlight the utility and promise of RootXplorer for accelerating research on root architectural plasticity under controlled compaction conditions, identifying genotypes with varying tolerance to mechanical impedance, and supporting data-driven breeding decisions for developing soil compaction-resilient crop varieties. This technology has important implications for future plant breeding strategies and supports ongoing climate change mitigation efforts.
Why it matches plant phenotyping methodsRoot penetrability関連形質を対象に、3D画像計測と自動ソフトウェアで抽出する高スループット表現型解析プラットフォームを開発しており、方法が研究の中心です。
abstractRootXplorer integrates a novel Phytagel-based cylinder system, a 3D imaging unit, and an automated software pipeline to extract root penetration-related traits with high precision and at a large scale.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the complete analysis pipeline and time-lapse video generation code in two public GitHub repositories under the authors' Salk Harnessing Plants Initiative organization. These directly support the paper's RootXplorer phenotyping analysis (image cropping, U-Net+Code · publicAll code for generating time-lapse videos is publicly available at https://github.com/Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapseOpen asset ↗Salk-Harnessing-Plants-Initiative/RootXplorer-cylinder-time-lapselines:167-180Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
We present a meticulously curated, long-term (1981-2024) dataset documenting maize phenology dynamics across Northeast China, the nation's most critical commercial grain base. Derived from 61 national agrometeorological stations, it captures the timing of 10 pivotal phenological stages (sowing, emergence, three-leaf, seven-leaf, jointing, tasseling, flowering, silking, milking, maturity) and derives the durations of 4 growth period lengths (sowing-jointing, jointing-silking, silking-maturity, sowing-maturity). The dataset underwent a rigorous, multi-tiered quality control protocol, including automated checks for internal consistency and expert arbitration for ambiguous records, ensuring high integrity. Subsequent analysis employed kernel density estimation to characterize the probability distribution of phenological events and univariate linear regression to quantify decadal trends. The resulting repository is substantial, comprising 976 georeferenced diagnostic plots in JPEG format and two primary data tables in XLSX format, with a total volume of 601.04 MB. Systematically organized by province and station, this dataset serves as a foundational empirical resource for quantifying climate-driven shifts in crop development, enhancing the parameterization and validation of process-based crop models, and informing the development of optimized cultivation practices and regional climate adaptation frameworks.
Why it matches plant phenotyping methodsトウモロコシの複数の生育ステージと生育期間を長期・広域に収録し、品質管理済みデータセットとして構築しているため、植物フェノタイピングデータセットが研究の中心です。
abstractit captures the timing of 10 pivotal phenological stages
Reproduction assets foundThe paper is a data descriptor whose maize phenology dataset (1981–2024, 61 stations, 10 phenological stages, diagnostic plots and XLSX tables) is openly deposited in Science Data Bank. No custom code was created.Dataset · publich stage timing and duration, it empowers farmers and
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agricultural planners to optimize production systems in response to evolving climatic
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conditions, thereby enhancing regional food security resilience.
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Data Availability
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The dataset generated during this study is openly available in the Science Data Bank at
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https://doi.org/10.57760/sciencedb.28709 or https://cstr.cn/31253.11.sciencedb.28709.312
Code availability
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No custom code was created for the production of this dataset.
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References
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1.Cai C, Ding T, Chen W. (2024). Potential yield of world maize under global warming based on ARIMA-TR model.
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Journal of Agrometeorology, 2024, 26(1).
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2.Li M. RetrospectOpen asset ↗Science Data Bank · 10.57760/sciencedb.28709pdf-raw-page:15 lines:1-94Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Background: Quantitatively detecting whether plants exhibit measurable bioelectric differences in the presence of nearby human movement remains challenging, in part because plant signals are low-amplitude, slow, and easily confounded by environmental factors. Methods: We recorded bioelectric activity from 2978 plant samples across three species (basil, salad, tomato) using differential electrode pairs (leaf and soil electrodes) sampling at 142 Hz. Two trained performers executed three specific eurythmic gestures near experimental plants while control plants remained isolated. Random Forest and Convolutional Neural Network classifiers were applied to distinguish the control from treatment conditions using engineered features including spectral, temporal, wavelet, and frequency domain characteristics. Results: Random Forest classification achieved 62.7% accuracy (AUC = 0.67) distinguishing differences in recordings collected near a moving human from control conditions, representing a statistically significant 12.7 percentage point improvement over chance. Individual performer signatures were detectable with 68.2% accuracy, while plant species classification achieved only 44.5% accuracy, indicating minimal species-specific artifacts. Temporal analysis revealed that the plants with repeated exposure exhibited consistently less negative bioelectric amplitudes compared to single-exposure plants. Innovation: We introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions. Conclusions: Plants exhibit modest but statistically detectable bioelectric differences in the presence of nearby human movement. Rather than attributing these differences to eurythmic movement itself, the present design can only demonstrate that plant recordings collected within ~1 m of a moving human differ, modestly but statistically, from recordings taken ≥3 m away. The underlying biophysical pathways and specific contributing factors (airflow, VOCs, thermal plumes, vibration, electromagnetic fields) remain unknown. These results should therefore be interpreted as exploratory correlations, not mechanistic evidence of gesture-specific plant sensing.
Why it matches plant phenotyping methods植物の生体電気記録をセンサーで取得し、特徴抽出と機械学習によって植物の状態差を判別する方法が研究の中心であり、単なる生理測定ではない。
abstractWe introduce a data-driven approach that pairs standardized, short-window bioelectric recordings with machine-learning classifiers (Random Forest, CNN) to test, in an exploratory manner, whether plant signals differ between human-moving-nearby and isolation conditions.
Reproduction assets foundThe paper's plant bioelectric recordings (wav sensor data from basil, salad, tomato with/without nearby human movement) are publicly deposited on figshare, with an explicit Data Availability Statement and URL matching an allowed URL.Dataset · publicThe datasets generated and analyzed during the current study are available from figshare https://figshare.com/articles/dataset/Machine_Learning_Detection_of_Plant_Bioelectric_Responses_to_Human_Eurythmic_Gestures_/30227083?file=58324288 (accessed 25 October 2025).Open asset ↗figshare · 30227083lines:172-191Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.
Why it matches plant phenotyping methodsUAV画像から作物の形態・成長形質を抽出し、時系列成長モデルと収量予測を検証するフレームワークが研究の中心であるため。
abstractUAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points.
Reproduction assets foundThe paper's data availability statement points to a public KnowPulse experiment page hosting the study's UAV-derived phenotyping and yield data; no author analysis code repository is stated.Dataset · publicof S).
We thank Dr. Ana Vargas at the Crop Development Center, U
of S for generously providing yield data from the independent
field trial.
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 supporting this study are available at:
https://knowpulse.usask.ca/experiment/AGILE-NAM-UAV-growth-modelling or from the authors upon request.
O RC I D
SandeshNeupane https://orcid.org/0000-0003-3679-1046
KirstinE. Bett https://orcid.org/0000-0001-7959-6959
SteveJ. Shirtliffe https://orcid.org/0000-0002-3603-7417
R E F E R E N C E S
Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J.Open asset ↗KnowPulse · AGILE-NAM-UAV-growth-modellingpdf-raw-page:15 lines:1-90Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
High-throughput phenotyping of growth kinetics and organ size in the model plant Arabidopsis thaliana requires rapid and precise methods for trait estimation. To address this need, we developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs. The enhanced segmentation model Cascade Mask Region-based Convolutional Neural Network (Mask R-CNN) achieved precision (measure of positive prediction accuracy), recall (sensitivity in detection), and F1 score values (harmonic mean of precision and recall) of 0.965, 0.958, and 0.961, respectively, for individual leaf segmentation. These metrics demonstrated a consistent improvement of approximately 1 percentage point over the baseline model. For silique segmentation, our enhanced DetectoRS model for silique segmentation attained precision, recall, and F1 scores of 0.954, 0.930, and 0.942, respectively. Notably, precision increased by 1%, while the F1 score improved by 2 percentage points. Trait parameters were automatically calculated with coefficient of determination values for leaf and silique traits ranging from 0.776 to 0.976 and mean absolute percentage error values from 1.89% to 7.90%. We phenotyped 166 Arabidopsis accessions, using APTES, and subjected the resulting values to a genome-wide association study (GWAS), revealing 1,042 single-nucleotide polymorphisms (SNPs) as being significantly associated with 18 leaf and silique traits, and one significant SNP on chromosome 3 linked to silique number. Furthermore, we validated APTES across other public Arabidopsis databases and other plant species, with segmentation results demonstrating its applicability across diverse datasets. In conclusion, APTES is a valuable automated tool for leaf and silique segmentation and trait estimation, which should offer benefits to the broader plant science community. Supplementary information The online version contains supplementary material available at 10.1007/s42994-025-00239-y.
Why it matches plant phenotyping methods植物の葉・莢の形質を画像から抽出する深層学習システムを開発し、性能検証・他データセットでの妥当性確認まで行っており、フェノタイピング手法が研究の中心である。
abstractwe developed the Arabidopsis Phenotypic Trait Estimation System, APTES, an open-access, high-throughput program that uses computer vision and deep learning to extract 64 leaf traits and 64 silique traits from photographs.
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 · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code · publicThe executable tool and software packages are available at https://drive.google.com/drive/folders/1i9IariiIrxuFtVIaRiaIzqvb8Gfg3xTc or http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .Open asset ↗lines:292-292Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches—such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration—provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics and cross-theme predictive integration to guide climate adaptation.
Why it matches plant phenotyping methods作物の生産性、フェノロジー、環境ストレスをリモートセンシングで測定・推定する方法論レビューであり、植物形質・状態の取得手法が中心である。
abstractThis review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring
Reproduction assets foundThis methodological review includes a case study (Figure 2) using MODIS NDVI composites, SILO gridded climate data, and ABARES historical winter crop yield data. The authors explicitly state the case-study datasets are publicly accessible via official portals; the SILO and ABARES portals are paper-specific public data-Dataset · publiclies, and observed productivity. Note: This figure is derived from
the authors’ ongoing study. The monthly NDVI composites (MOD13C2) were generated post-season, which limits their
utility for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available onlOpen asset ↗SILOpdf-layout-page:8 lines:1-53Dataset · publiclity for in-season forecasting. The gridded Climate data were obtained from the Australian Scientific Information for
Land Owners (SILO) database (https://www.longpaddock.qld.gov.au/silo/), and historical winter crop yield data were
sourced from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)
(https://www.agriculture.gov.au/abares/data).
However, Figure 2 also illustrates key limitations of NDVI-based monitoring. First, the complete
seasonal NDVI composite becomes available only after crop harvest, limiting its usefulness for in-
season yield forecasting or early drought warning. In other words, detailed phenological curves and
productivity metrics can onlyOpen asset ↗ABARESpdf-layout-page:8 lines:1-53Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
Why it matches plant phenotyping methods衛星データとAI/MLを用いて水稲の生育状態（phenology）と収量を圃場レベルで推定する手法を開発・評価しており、植物形質の取得・推定が研究の中心である。
abstractdeveloped several crop-based indicators to assess crop phenology
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.29858924) containing the data supporting the study's rice monitoring findings, which qualifies as a paper-specific public asset.Dataset · publicuddin Shaik: Software, Visualization, Writing –
original draft
Suman Saraswathibatla: Investigation, Project admin-
istration, Supervision
Mukund Patil: Validation, Writing – review & editing
Data Availability Statement
The data that support the findings of this study can be found
at https://figshare.com/s/b611c04368825e6a028b
(https://doi.org/10.6084/m9.figshare.29858924).Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Mandapati, R., Gumma, M. K., Metuku, D. R., Bel-lam, P. K.,
Panjala, P., Maitra, S., Maila, N. Crop yield assessment using
field-based data and crop models at the village level: A case
study on a homogeneous rice area in Telangana, India.
AgOpen asset ↗figshare · 10.6084/m9.figshare.29858924pdf-raw-page:21 lines:1-101Code / 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 · Europe PMC · checked 6 Sept 2026
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
Why it matches plant phenotyping methods植物の高スループット表現型取得を中心とするデータセットで、画像から農業形質を抽出するセンサー基盤、処理画像、表現型データ、解析スクリプトを提供しているため。
abstracthigh-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress
Reproduction assets foundThe authors deposited the paper's raw/processed phenotyping images, phenotypic and photosynthesis data, GWAS inputs/results, and R analysis scripts in the public e!DAL repository (DOI 10.5447/ipk/2025/8) in ISA-Tab/MIAPPE format.Dataset · publicThe produced raw datasets and source code were uploaded to the e!DAL repository in ISA-Tab format (http://dx.doi.org/10.5447/ipk/2025/8) according to the MIAPPE standard.Open asset ↗e!DAL · 10.5447/ipk/2025/8html-lines:126-157Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・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 aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:135-161Code / 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 15 Sept 2026
Field / plotRGB / grayscaleWhole plant / canopy / plot / fieldObject detectionGrowth / time-series analysisGrowth / development / phenology
1. Ecosystems are rapidly degrading. Widely used approaches to monitor ecosystems to manage them effectively are both expensive and time consuming. The recent proliferation of publicly available imagery from satellites, Google Earth, and citizen-science platforms holds the promise to revolutionising ecological monitoring and optimising their efficiency. However, the potential of these platforms to detect species and track their population dynamics remains under-explored. 2. We introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data. We apply this method to three case studies involving two major Mediterranean invasive plant taxa with contrasting growth forms. Specifically, we: (1) utilise deep learning to automatically detect individuals of prickly pear ( Opuntia sp.) across various Mediterranean habitats and image resolutions; (2) reconstruct 10 years of spatially explicit recruitment rates for Opuntia along a climatic gradient; and (3) quantify nearly 20 years of growth dynamics for the clonal invader Carpobrotus sp. in two contrasting environments. 3. Our object detection model, trained with Google Earth imagery, achieves 60-80% success in identifying individuals of Opuntia , regardless of habitat type. Model performance increases with target species colour consistency and contrast, as well with the usage of basic data augmentation techniques. Detection is constrained by individual area (<4 m 2 ) but captures 80% of the examined population. 4. Beyond detection, our time-series analysis of publicly available imagery enables detailed population monitoring. With 10-year image series available for Spain, Greece, and the UK, and 20 years for Portugal, we successfully estimate annual recruitment and growth rates and their climatic sensitivity, identify productive and unproductive years, estimate individual age, characterise population structure, model size-age relationships, and identify recruitment hotspots for targeted management. 5. Our pipeline opens new avenues for cost-effective, large-scale demographic monitoring by retrospectively harnessing open-access imagery. While demonstrated here with invasive plants, we discuss the broad applicability of our approach across taxa and ecosystems. The use of retrospective image analysis for long-term demography with Google Earth imagery has the potential to expedite conservation decisions, support effective restoration, and enable robust ecological forecasting in the Anthropocene.
Why it matches plant phenotyping methodsGoogle Earth画像と深層学習を用いて植物個体の検出、成長・加入率・年齢などの形態・動態形質を抽出する再利用可能な解析パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractWe introduce a fast, inexpensive method for retrospective image analysis combining current ground-truth data with historical RGB imagery from Google Earth to extract long-term demographic data.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits all data and code (Google Earth imagery-based demographic data, segmentation/training data, and analysis code) on FigShare with a DOI, and provides author video-tutorials of the phenotyping/demography pipeline on a YouTube playlist. Both are paper-specific,公开,Dataset · public9
Google Earth
10
11
Author Contributions: EF: Conceptualisation, field data collection, data analysis, first draft
12
writing. GC: First approach on part of the analysis. RS-G: Supervision, support in
13
conceptualisation, writing-feedback.
14
15
Data Availability Statement: All data and code can be found in FigShare: DOI:
16
https://doi.org/10.6084/m9.figshare.30024679.v1. Video-tutorials can also be found in this
17
YouTube playlist: https://www.youtube.com/playlist?list=PL_LKE-18
yTi9kBXfw_qDdJCQ3Sxu2fjGvDD, of EF’s account: @environmentaldatascientist.
19
20
Acknowledgments: We thank C. Ribalta-Pizarro for her assistance geolocalising individuals
21
on the field and collecting UAV data.Open asset ↗FigShare · 10.6084/m9.figshare.30024679.v1pdf-raw-page:1 lines:1-62Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions.
Why it matches plant phenotyping methods衛星画像から圃場レベルのイネ作付面積・収量を推定する分類および回帰手法が研究の中心であり、精度評価も実施しているため、植物形質推定の方法論として適格。
abstractThis research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data.
Reproduction assets foundThe paper's Data Availability Statement explicitly states that all data and code to reproduce the rice area and yield maps are publicly available in the authors' GitHub repository (ozdogan15/srilanka), which directly reproduces this paper's rice mapping and yield estimation analysis.Code · publicbrella Facility for Trade trust fund
(financed by the governments of the Netherlands, Norway, Sweden, Switzerland, and the United
Kingdom) and the World Bank’s Research Support Budget for financial support.
Data Availability Statement: All data and code to reproduce rice and yield maps are publicly
available at this repository: https://github.com/ozdogan15/srilanka#.
Acknowledgments: The authors acknowledge funding from the World Bank Whole of Economy
Program. We also thank the reviewers. The findings, interpretations, and conclusions expressed in
this paper are solely those of the authors and do not necessarily represent the views of the World
Bank, its affiliated organizations, or the ExOpen asset ↗ozdogan15/srilankapdf-layout-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較で検証しているため、フェノタイピング手法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / 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
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on
[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 confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.
Why it matches plant phenotyping methods8年間の高解像度RGB画像による圃場フェノタイピングデータセットを提示し、作物生育動態とG×E解析を可能にする方法・データ基盤が中心である。
titleFIP 1.0 Soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping
Reproduction assets foundThe paper is a data note whose core contribution is a public soybean phenotyping dataset (raw FIP images, segmentation masks, canopy cover data, BLUEs, weather, reference traits) deposited at ETH Research Collection, plus the authors' canopy cover extraction workflow code on GitLab. Both are paper-specific, public, andDataset · publicason, therefore,
from 2020 to 2022, photosynthetic photon fluence rate (PPFR) was taken from a LI-COR sensor placed next to the
field. The factor to convert radiation in MJ m−
2 to PPFR was 2.04 according to [26].
4.1 Data Files and Structure
The dataset presented in this study is available at ETH Research Collection under DOI: https://doi.org/10.3929/ethz-b-000742401. The dataset is structured into directories that align with the described data processing
pipeline used for extracting and analyzing canopy cover traits from field images. All files are provided in interoperable
and widely-used ‘.csv‘ and ‘.png‘ format.
• data/Design 2015 2022 Eschikon.csv: Experimental design file, including pOpen asset ↗ETH Research Collection · 10.3929/ethz-b-000742401pdf-raw-page:6 lines:1-47Code · publicCollection (https://doi.org/10.3929/ethz-b-000742401) and as Hugging Face data set card (doi.org/10.57967/hf/6052) allowing
interoperability and standardization with other datasets.
9 Code availability
Users with similar data can use the implemented workflow to get canopy cover from their experiments. The code
is available on: https://gitlab.ethz.ch/crop_phenotyping/fip-soybean-canopycover
10 Author contributions
BK: Developed algorithm, analyzed data and drafted manuscript; NK, LR, AH, AM: FIP development, BK, NK,
CO, LK, LR, OZ, SC, FL, HA, NS, FT, HZ, CAB, CB, AH: Collected and prepared data; Experimental design:
BK, LK, LR, AH; all authors improved and approved the manuscript
5Open asset ↗gitlab.ethz.ch · crop_phenotyping/fip-soybean-canopycoverpdf-raw-page:7 lines:1-46Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Shade avoidance response in plants includes a higher leaf elevation angle. A cost-effective and noninvasive high throughput image analysis technique was used to measure the dynamics of leaf elevation angles during shade avoidance response in Arabidopsis . Time-lapse images were taken from the top and the side of a plant using Raspberry Pi computers. The leaf elevation index for each plant is determined from the plant dimensions measured by an image analysis software package PlantCV . This method was used to monitor the dynamics of changing leaf elevation angles in wild-type plants and in shade avoidance mutants pif4-2pif5-3 and pif7-2 plants.
Why it matches plant phenotyping methodsRaspberry Piと画像解析を用いて葉の仰角を定量化する高スループット手法が研究の中心であり、植物形質の時系列取得に実質的に適用されている。
abstractA cost-effective and noninvasive high throughput image analysis technique was used to measure the dynamics of leaf elevation angles during shade avoidance response in Arabidopsis .
Reproduction assets foundThe paper deposits its authors' PlantCV-based python analysis script (example_workflow.py) as an Extended Data software item with a public DOI (Caltech DATA). No phenotype dataset or image deposit is described.Code · publicts used in this study
Ecotype
Genotype
Available From
Columbia
Wild type
Columbia
pif4-2pif5-3
ABRC # CS68096
Columbia
pif7-2
ABRC # CS71656
I thank Dr. Noah Fahlgren at the Donald Danforth Plant Science Center for his help with
PlantCV
.
Extended Data
Description: python script used in this study. Resource Type: Software. DOI:
https://doi.org/10.22002/q71sw-5vz65
BerryJC
FahlgrenN
PokornyAA
BartRS
VeleyKM
2018104An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping.PeerJ62167-8359e5727e572710.7717/peerj.572730310752PMC6174877
DevlinPF
HallidayKJ
HarberdNP
WhitelamGC
1996121The rosette habit of Arabidopsis thaliana is dependeOpen asset ↗10.22002/q71sw-5vz65html-lines:128-241Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This study presents a comprehensive analysis of winter wheat phenological variations in China's Huang-Huai-Hai Plain (HHHP) from 1981 to 2021, leveraging data from 62 national agrometeorological observation stations. As the world's largest winter wheat production region, the HHHP contributes over 60% of China's total output, playing a pivotal role in national food security. Using kernel density estimation (KDE) and univariate linear regression, the dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations. Results reveal significant shifts in phenological timings and growth stages under climate change, such as advanced heading stages and altered phase lengths, which correlate with temperature increases and extreme weather events. The dataset, comprising 1,120 figures generated via Origin Lab, is publicly available on ScienceDB, providing critical insights for climate adaptation strategies, cultivation optimization, and yield stability. Technical validation confirms the reliability of the data, sourced from standardized, long-term manual observations by trained professionals under China Meteorological Administration protocols. This work offers a foundational resource for understanding climate-crop interactions and guiding sustainable agricultural practices in a warming world.
Why it matches plant phenotyping methods冬小麦の複数生育ステージという植物形質を長期・標準化観測で収録した公開データセットであり、データの技術的検証も含むため、フェノタイピングデータセットとして中心的です。
abstractthe dataset characterizes interannual trends in key phenological stages-sowing, emergence, tillering, jointing, booting, heading, flowering, milking, and maturity-along with growth period durations
Reproduction assets foundThe paper describes a public dataset of winter wheat phenology (1,120 KDE and linear-trend figures from 62 agrometeorological stations, 1981–2021) deposited on ScienceDB under DOI 10.57760/sciencedb.23011, freely downloadable. No custom analysis code exists ('No custom code was created for the production of this dataetDataset · publicThe Variation of winter wheat phenology dataset in Huang Huai Hai Plain of China from 1981 to 2021 is available at ScienceDB 35 . The dataset is provided in JPG format estimated and plotted by Origin Lab. All the diagrams can be downloaded directly for free.Open asset ↗lines:47-83Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Summary Live imaging data analysis often requires an objective, local, and accurate way of quantification of cell dynamics. In the research field of polarized tip-growth, the cell fluctuations and/or fluctuations in tip position and growth direction hampers automated analyses of huge amounts of imaging sequences. The fluctuated nature in data makes it unclear how cell shape and growth are linked to intracellular events that could be the actual driving force of cell growth. To overcome these difficulties, we developed a powerful and user-friendly tool called KymoTip with an available format. In this software, novel functions such as coordinate normalization, tip-bottom detection, and signal kymograph were implemented. We confirmed that not only plasma membrane-labeled fluorescent images, but also images such as bright-field and cortical microtubule markers —so long as the cell contours can be identified— are amenable to KymoTip. Furthermore, by combining markers for cell contours with those that visualize intracellular structures, it becomes possible to quantitatively analyze various intracellular events, such as nuclear migration and calcium wave, in conjunction with cellular growth dynamics. Since KymoTip can be handled by non-specialist, it is expected to promote understanding of what happens at the sub- and cellular level with high throughput outcomes. Significance statement Faced with fluctuations in cell coordinates and cell tip positions, position correction of live imaging data and accurate detection of tip position are key challenges in plant developmental biology. We solved them with a powerful and user-friendly tool, KymoTip, that can realize cell position correction, cell tip detection with cell centerline, and quantification of intracellular events.
Why it matches plant phenotyping methods植物細胞のライブイメージから細胞形状・先端位置・成長動態を定量化する解析ソフトウェアを開発しており、植物フェノタイピング手法が中心です。
abstractwe developed a powerful and user-friendly tool called KymoTip
Reproduction assets foundThe paper's authors explicitly state that the KymoTip analysis code is publicly available on GitHub at https://github.com/blues0910/KymoTip, which is an allowed URL. This is the authors' own computational tool implementing the paper's tip-growth phenotyping analysis (segmentation, coordinate normalization, tip-bottom, Code · publicThe code for KymoTip is available on GitHub: https://github.com/blues0910/KymoTip.Open asset ↗blues0910/KymoTippdf-page:8 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Seed / grainGrowth / time-series analysisGrowth / development / phenology
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian inference, which utilizes previous measurements, historical data, and an expected model. Five Bayesian methods are assessed in this study: Important sampling (IS), Markov chain Monte-Carlo (MCMC), Gaussian process (GP), Extended Kalman filtering (EKF) and Sampling Importance Resampling particle filtering (SIR-PF). We test these five Bayesian sampling methods for the monitoring of germination rate in terms of compression, distortion and computation cost. The best trade-off is found by the MCMC method, which offers a compression rate of 0.2 with very little distortion. GP offers the most unbiased parameter estimation and the capability to adapt to various germination speeds. It also has reasonable computational times.
Why it matches plant phenotyping methods発芽率の時系列フェノタイピングに対するベイズ適応サンプリング法を開発・比較し、圧縮率、歪み、計算コストで評価しており、表現型取得・監視手法が研究の中心である。
abstractWe propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage.
Reproduction assets foundThe paper provides two paper-specific public assets: an authors' GitHub repository with the code implementing the five Bayesian adaptive sampling methods, and a public germination kinetics dataset (red clover accessions) deposited at doi.org/10.57745/JECJUI, which is the raw phenotype data analyzed in the study.Code · publicwe provide the codes and data to perform the computation and discuss the convergence of the process: The code is available at the following address: https://github.com/Fatryuk/BayesianAdaptivSampling.gitOpen asset ↗https://github.com/Fatryuk/BayesianAdaptivSampling.gitlines:29-41Dataset · publicData used in the article are table in.csv format containing raw germination along time available at the following repository https://doi.org/10.57745/JECJUIOpen asset ↗https://doi.org/10.57745/JECJUI · 10.57745/JECJUIlines:297-334Code / 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
RiceFlowerObject detectionGrowth / time-series analysisGrowth / development / phenology
Flower opening time (FOT) is a key trait for successful reproduction and reproductive isolation. In crop science, FOT is critical for stress avoidance and efficient breeding practices. This study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology. This approach enabled high-resolution monitoring of flowering dynamics in two cultivars: the japonica cultivar Taichung 65 (T65) and the indica cultivar IR24. The system accurately identified regions containing open flowers, and the estimated FOTs varied within a 3-h range, with a root mean square error of approximately 30 min compared to manual detection. A significant difference in estimated FOTs between IR24 and T65 demonstrated the system's potential for genetic screening applications. FOT of both cultivars exhibited a significant negative correlation with daily mean temperature. Notably, a temperature-sensitive period was identified in the morning, suggesting that temperature influences not only flower opening but also preceding physiological processes such as panicle and spikelet development. This study presents a novel approach to investigating FOT dynamics in rice and provides insights into the interaction between environmental factors and internal regulatory mechanisms governing this critical reproductive trait.
Why it matches plant phenotyping methods低コストタイムラプスカメラと機械学習によるイネの開花時刻という植物形質の自動検出・推定システムを開発し、手動検出との誤差で検証しているため、方法が中心的です。
abstractThis study developed a system for the automatic detection of rice flower openings and FOT estimation by integrating a low-cost time-lapse camera with machine learning technology.
Reproduction assets foundThe authors explicitly state that the Python scripts, training dataset (annotated time-lapse rice flower images), and trained YOLOX model used for flower-opening detection are publicly available on their GitHub repository (mwbotan/FLpanicle). This is a paper-specific, public, actionable asset directly reproducing the FCode · publicThe Python scripts, training dataset, and trained model used in this analysis are available on GitHub ( https://github.com/mwbotan/FLpanicle ).Open asset ↗mwbotan/FLpaniclelines:73-84Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10-day filtered timeseries: 1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and 2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsFPARという作物・植生キャノピーの明示的な状態量を対象に、MODIS/VIIRSデータのNRTフィルタリング、相互校正、品質評価を開発・記述しており、単なる農業利用ではなく再利用可能な測定データセットと抽出手法が中心である。
abstractwe present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications.
Reproduction assets foundThe paper's own filtered and intercalibrated MODIS/VIIRS FPAR dataset (the paper's core output) is explicitly described as open and freely available in near real time via the JRC Data Catalogue and the ASAP website, both of which appear in allowed_urls. No author analysis code is mentioned.Dataset · publicec.europa.eu/, last
access: 30 September 2025) early warning system. The
FPAR dataset is accompanied by associated quality layers
and has a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR Open asset ↗1aac79d8-0d68-4f1c-a40f-b6e362264e50pdf-raw-page:3 lines:1-86Dataset · publichas a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR data; (ii) to present the intercalibration performed
between the filtered MODIS-FPAR and the filtered VIIRS-
FPAR; (iii) to evaluate tOpen asset ↗ASAP websitepdf-raw-page:3 lines:1-86Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dendrometry is the main non-invasive macroscopic technique commonly used in plant physiology and ecophysysiology studies. Over the years several types of dendrometric techniques have been developed, each with their respective strengths and drawbacks. Automatic and continuous monitoring solutions are being developed, but are still limited, particularly for non-invasive monitoring of large-diameter trunks. In this study, we propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference, is non-invasive and has no upper limit on the trunk diameter on which it can be installed. We performed a 3-month validation experiment during which we deployed a fibre-optic cable at three localities around the trunks of two specimens of Brachychiton. We verified the accuracy of this new method through comparison with a conventional point-dendrometer, and we observed a consistent time lag between the various measurement locations that varies with the meteorological conditions. Finally, we discuss the feasibility of the fibre-based dendrometer in the context of existing dendrometric techniques and practical experimental considerations.
Why it matches plant phenotyping methods植物幹の周囲長変化を連続測定する新規ファイバー光学式デンドロメータを開発し、従来法との比較で精度を検証しており、植物表現型取得法が研究の中心です。
abstractwe propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference
Reproduction assets foundThe authors explicitly state that all data (DSTS strain recordings, dendrometer time series) and analysis scripts needed to reproduce the paper's figures are publicly deposited on Figshare.Dataset · publicnuscript. For the analysis we made use of the following Python libraries: Matplotlib 3.7.2 332
[Hunter, 2007], NumPy 1.25.1 [Harris et al., 2020], Pandas 2.0.3 [Pandas Development Team, 2023], 333
SciPy 1.11.1 [Virtanen et al., 2020]. All the data and scripts needed to reproduce the figures in this 334
study are available here: https://doi.org/10.6084/m9.figshare.25773432. 335
References
T. Ameglio and P. Cruiziat. Daily Variations of Stem and Branch Diameter: Short Overview from a Developed Example. In
T. K. Karalis, editor, Mechanics of Swelling, NATO ASI Series, pages 193–204, Berlin, Heidelberg, 1992. Springer. ISBN
978-3-642-84619-9. doi: 10.1007/978-3-642-84619-9 9.
T. Ameglio, H. CochOpen asset ↗figshare · 10.6084/m9.figshare.25773432pdf-raw-page:14 lines:1-45Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Earth's polar regions are experiencing significant climate change, impacting global oceanographic and weather patterns. Arctic "greening" is well studied, but a debate has emerged about whether similar trends are occurring in Antarctica and whether and how remote sensing can assess them. Recent studies have introduced a concept of "greening" in Antarctica, framed primarily around moss cover expansion over bare ground. This interpretation differs from Arctic greening studies, which focus mainly on changes in vascular plant productivity and successional dynamics. This paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context, considering regional limitations in technology, data availability, and the unique Antarctic vegetation characteristics. Unlike the Arctic, Antarctic vegetation consists mainly of nonvascular organisms (algae, cyanobacteria, lichens, and bryophytes) that interact with slow-weathering soils with minimal organic inputs. These biological and environmental differences likely influence NDVI greening metrics and their ecological relevance, but remain poorly understood due to limited long-term data and validation. Despite advances in remote sensing, Antarctic vegetation mapping remains in its early stages. The small size and patchy distribution of vegetation complicate detection of presence and extent, and even with modern satellites, capturing subcentimeter annual growth rates remains challenging. The lack of historical high-resolution imagery hampers change detection, limiting our ability to track habitat expansion, vegetation dynamics, and community composition changes over time. Based on critical assessment, we identify serious concerns regarding the accuracy and interpretation of NDVI-based greening trends in Antarctica in recent studies, particularly in relation to technological constraints and biological realism. To address these issues, we propose a refined framework for interpreting NDVI data in Antarctica, aiming to prevent misleading conclusions about vegetation changes and trends. This framework suggests an urgent need for re-evaluation of how "greening" is both quantified and interpreted in Antarctica.
Why it matches plant phenotyping methods南極植生の状態・変化をNDVIで推定するリモートセンシング手法を中心に、その精度・解釈上の問題を批判的に評価し、改良フレームワークを提案する方法論的レビューである。
abstractThis paper evaluates the Antarctic greening concept, focusing on how Normalized Difference Vegetation Index (NDVI)-based methods are applied and interpreted in this context
Reproduction assets foundThe paper's Data Availability Statement states the supporting data are openly available in Edinburgh Data Share at DOI 10.7488/ds/7945. This is a paper-specific public dataset deposit (the study's vegetation/greening analysis data). The related spectral library dataset (10.7488/ds/7720) is cited prior work, not this论文.Dataset · publicfunding provided by grants from Dartmouth's
College of Arts and Sciences and the Clare Garber Goodman Fund for
Anthropological Research.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data that support the findings of this study are openly available in
Edinburgh Data Share at https://doi.org/10.7488/ds/7945.References
Aartsma, P., J. Asplund, A. Odland, S. Reinhardt, and H. Renssen.
2021. “Microclimatic Comparison of Lichen Heaths and Shrubs:
Shrubification Generates Atmospheric Heating but Subsurface Cooling
During the Growing Season.” Biogeosciences 18: 1577–1599. https://doi.org/10.5194/bg-18-1577-2021.Allison, J. S., and R. I. SmithOpen asset ↗Edinburgh Data Share · 10.7488/ds/7945pdf-raw-page:14 lines:1-79Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction Recent advancements in sensor technologies have enabled collection of many large, high-resolution plant images datasets that could be used to non-destructively explore the relationships between genetics, environment and management factors on phenotype or the physical traits exhibited by plants. The phenotype data captured in these datasets could then be integrated into models of plant development and crop yield to more accurately predict how plants may grow as a result of changing management practices and climate conditions, better ensuring future food security. However, automated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking. In this study, we explore interdisciplinary application of MapReader, a computer vision pipeline for annotating and classifying patches of larger images that was originally developed for semantic exploration of historical maps, to time-series images of whole oilseed rape (Brassica napus) plants. Methods Models were trained to classify five plant structures in patches derived from whole plant images (branches, leaves, pods, flower buds and flowers), as well as background patches. Three modelling methods are compared: (i) 6-label multi-class classification, (ii) a chain of binary classifiers approach, and (iii) an approach combining binary classification of plant and background patches, followed by 5-label multi-class classification of plant structures. Results A combined plant/background binarization and 5-label multi-class modelling approach using a ‘resnext50d_4s2x40d’ model architecture for both the binary classification and multi-class classification components was found to produce the most accurate patch classification for whole B. napus plant images (macro-averaged F1-score = 88.50, weighted average F1-score = 97.71). This combined binary and 5-label multi-class classification approach demonstrate similar performance to the top-performing MapReader ‘railspace’ classification model. Discussion This highlights the potential applicability of the MapReader model framework to images data from across scientific and humanities domains, and the flexibility it provides in creating pipelines with different modelling approaches. The pipeline for dynamic plant phenotyping from whole plant images developed in this study could potentially be applied to imagery from varied laboratory conditions, and to images datasets of other plants of both agricultural and conservation concern.
Why it matches plant phenotyping methods植物全体画像から葉・花・莢などの構造を自動抽出・分類する動的フェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractautomated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking.
Reproduction assets foundThe paper's phenotyping analysis is based on a public RGB image dataset of Brassica napus plants, explicitly deposited by the authors with a public URL. MapReader is a generic pre-existing library and the HuggingFace railspace models are cited prior work, not paper-specific assets.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://research.aber.ac.uk/en/datasets/collection-of-side-view-and-top-view-rgb-images-of-brassica-napus .Open asset ↗research.aber.ac.uklines:982-1027Code / 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 · 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 confirmedEurope PMC · checked 14 Sept 2026
Field / plotRootMorphology / geometry measurementGrowth / time-series analysisRoot system architectureWater status / transpiration
Summary Deep-rooted crops accessing water and nutrients from deep soil layers enhance the resource base for crop production. However, studying these roots in field conditions is labor-intensive, limiting research scope. We established a field root research facility with 48 plots for replicated experiments. The facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis. We also attempted to install access-tubes and customized ingrowth-core production for less-invasive root activity determination. Our study revealed significant differences in deep root density among species, particularly at depths of 2.5 to 4.5 meters, over five years. The less invasive studies using ingrowth-cores reached depths of 4.2 meters. Nutrient tracer 15N analysis showed marked differences in deep root activity among crop species. TDR sensors indicated varying water depletion in deeper soil layers, influenced by crop species and root growth patterns. We established a field facility for studying deep root growth and function, demonstrating its effectiveness in analyzing diverse deep-rooted plant species. This facility provides an ideal platform for conducting meaningful research in deep soil layers, yielding statistically and biologically significant results for agricultural applications.
Why it matches plant phenotyping methods深根の形質取得を目的とした圃場施設であり、ミニライゾトロン画像とAIパイプラインによる根形質解析が中心的に記述されているため、植物フェノタイピング基盤として収録する。
abstractThe facility includes 144 six-meter-long minirhizotron tubes and an AI-based pipeline for rapid root trait analysis.
Reproduction assets foundThe paper's Data availability statement deposits the paper-specific root phenotyping assets — raw minirhizotron images, RootPainter training datasets, manual counts, and trained segmentation models — in a public Zenodo repository (DOI 10.5281/zenodo.15213661), which is listed in allowed_urls.Dataset · publico be studied
in well-replicated studies. The facility is an ideal
platform for conducting studies at deep soil layers in
the field with a capacity for generating statistically
and biologically meaningful results.
Data availability
The raw images, training datasets, manual counts
and models generated after training are available
http://doi.org/10.5281/zenodo.15213661.Author contributions
EH prepared the manuscript and all co-authors
contributed to writing. EH trained the model for
automatic root segmentation and conducted all the
ingrowth-core experiments. CC installed the TDR
system and conducted the experiment. AGS created
the RootPainter software and provided technical
advice on trainingOpen asset ↗zenodo · 10.5281/zenodo.15213661pdf-raw-page:18 lines:1-107Code / dataset availability confirmedarXiv · checked 6 Sept 2026
ArabidopsisTomatoLeafRootSeed / grainSegmentationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking six distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters, and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional Principal Component Analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes.ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise.
Why it matches plant phenotyping methods植物の時系列画像から根・地上部・種子などの形態形質を抽出・追跡するオープンなAI基盤を開発し、精度向上、再学習、GUI、高スループット解析、検証まで扱っており、フェノタイピング手法が研究の中心です。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper explicitly releases its full analysis source code (GitHub), the annotated infrared image dataset with multiclass segmentation masks (HuggingFace), demo phenotype video datasets, and a Docker image — all paper-specific, public, and actionable.Code · publicThe complete source code of ChronoRoot 2.0, including the implementation of all analysis methods described in this paper, is freely available under the GNU General Public License v3.0 at https://github.com/ChronoRoot/ChronoRoot2Open asset ↗ChronoRoot/ChronoRoot2lines:491-523Dataset · publicThe annotated image dataset used for training and validation contains 911 infrared images of Arabidopsis thaliana seedlings and 480 images of tomato with expert annotations for multiclass segmentation. This dataset is publicly available without restrictions at https://huggingface.co/datasets/ngaggion/ChronoRoot2Open asset ↗ngaggion/ChronoRoot2lines:491-523Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping. Using genetic markers and data from high-throughput phenotyping of a maize multiparent advanced generation inter-cross population and an Arabidopsis thaliana diversity panel, we show that dynamicGP outperforms a baseline genomic prediction approach for the multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and environment over plant development to improve the prediction accuracy of agronomically relevant traits.
Why it matches plant phenotyping methods遺伝マーカーと高スループット表現型データを統合し、植物形態・幾何・色彩形質の時系列を予測する計算手法dynamicGPが研究の中心であるため。
abstractHere we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize the temporal changes and to predict genotype-specific dynamics for multiple morphometric, geometric and colourimetric traits scored by high-throughput phenotyping.
Reproduction assets foundThe paper's authors publicly released their dynamicGP R implementation on GitHub and mirrored code plus genotyping data on Zenodo, and the maize HTP phenotype dataset is deposited on e!DAL (IPK). All are paper-specific, public, and directly actionable.Code · publicAn R implementation of algorithms 1 and 2 is available at https://github.com/dobby978/dynamicGP .Open asset ↗dobby978/dynamicGPlines:134-161Code · publicAll code that was used to generate the results of this study is available via GitHub at https://github.com/dobby978/dynamicGP and via Zenodo at https://doi.org/10.5281/zenodo.14959484 (ref. 32 ).Open asset ↗Zenodo · 10.5281/zenodo.14959484lines:181-276Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products-food, feed, fuel and fiber-will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, commonly known as duckweeds, are one family of plants that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of six different clones of L. gibba .
Why it matches plant phenotyping methodsALPHAは水生植物の成長率をハイスループットに定量化するための装置・表現型解析プラットフォームとして開発・実証されており、手法が研究の中心である。
abstractHerein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae.
Reproduction assets foundThe authors state that all source code for the phenotyping system and analysis, 3D models, and the data generated for this study (including PlantCV output and barcode map CSVs) are publicly available in the ALPHA GitHub repository.Dataset · publicAll source code used in the phenotyping system, 3D models for printed parts and data generated for this study are available in the ALPHA Github repository .Open asset ↗lines:85-105Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Summary Leaf water loss after stomatal closure is key to understanding the effects of prolonged drought on vegetation. It is therefore important to accurately quantify such water losses to improve physiology‐based models of drought‐induced plant mortality. We measured water loss of detached leaves continuously during dehydration in nine woody angiosperm species. We computed minimum leaf conductance ( g min ) at different water potential thresholds along a sequence of physiological function losses, spanning from turgor loss point to hydraulic failure. A mechanistic model evaluated the impact of different g min estimations on the time to hydraulic failure (THF). Residual conductance is not steady and decreases continuously at varying rates across species during the entire dehydration process, even after correcting for leaf shrinkage and vapor pressure deficit shifts. Different estimations of g min had a significant impact on the THF predicted by the model, especially for drought‐resistant species. We demonstrate that residual conductance is variable during dehydration, and thus, it is important to use physiological or water status boundaries for its estimation in order to determine distinct g min values of water loss. We describe an accurate, repeatable and open‐source methodology to estimate g min . Such methodology could enhance models of plant mortality under drought.
Why it matches plant phenotyping methods葉の脱水過程における最小葉コンダクタンスの定量法を開発・評価し、反復可能な方法論として提示しているため、植物生理フェノタイピング手法が中心です。
abstractWe describe an accurate, repeatable and open‐source methodology to estimate g min .
Reproduction assets foundThe paper's Data Availability Statement provides two paper-specific public assets: the authors' analysis/acquisition code (gminComputation in Python, g_Residual in R, and the 'cuticular' acquisition software) hosted on a public Gitlab repository, and the manuscript's underlying dehydration/gmin measurement data on the法Code · publicCodes developed for data acquisition (software ‘cuticular’ for Windows) and computation of raw residual conductance (project ‘gminComputation’ is developed as a console version in python, and ‘g_Residual’ is a script written in R language) are available in the following public Gitlab repository: https://gitub.u‐bordeaux.fr/phenoboisOpen asset ↗https://gitub.u‐bordeaux.fr/phenoboislines:509-550Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R 2 = 0.934) and RGB (R 2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15 , were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant ( pag1 ), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
Why it matches plant phenotyping methodsUAV搭載RGB・LiDARによる綿花草丈の高スループット取得・推定プラットフォームの開発と精度検証が研究の中心であり、GWASや遺伝子機能解析は応用部分です。
abstracta high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' source code, UAV-captured images, and analysis datasets in a public GitHub repository, which directly supports this paper's cotton plant-height phenotyping measurements and computational analysis.Code · publicThe source code, images captured by UAVs, data obtained from the analysis, and other datasets supporting the results presented here are available at https://github.com/Liqiangfan/419-cotton-plant-height-datasets .Open asset ↗Liqiangfan/419-cotton-plant-height-datasetslines:142-154Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Automatic plant growth monitoring is an important task in modern agriculture for maintaining high crop yield and boosting the breeding procedure. The advancement of 3D sensing technology has made 3D point clouds to be a better data form on presenting plant growth than images, as the new organs are easier identified in 3D space and the occluded organs in 2D can also be conveniently separated in 3D. Despite the attractive characteristics, analysis on 3D data can be quite challenging. We present 3D-NOD, a framework to detect new organs from time-series 3D plant data by spatiotemporal point cloud deep semantic segmentation. The design of 3D-NOD framework drew inspiration from how a well-experienced human utilizes spatiotemporal information to identify growing buds from a plant at two different growth stages. In the training phase, by introducing the Backward & Forward Labeling, the Registration & Mix-up, and the Humanoid Data Augmentation step, our backbone network can be trained to recognize growth events with organ correlation from both temporal and spatial domains. In testing, 3D-NOD has shown better sensitivity at segmenting new organs against the conventional way of using a network to conduct direct semantic segmentation. On a time-series dataset containing multiple species, Our method reached a mean F1-measure at 88.13 % and a mean IoU at 80.68 % on detecting both new and old organs with the DGCNN backbone.
Why it matches plant phenotyping methods植物の時系列3D点群から新生器官を検出・分割する手法を開発し、複数種データセットで性能評価しており、植物表現型取得が中心である。
abstractWe present 3D-NOD, a framework to detect new organs from time-series 3D plant data by spatiotemporal point cloud deep semantic segmentation.
Reproduction assets foundThe authors explicitly state that both the dataset (labeled time-series plant point clouds for tobacco, tomato, and sorghum) and the analysis code for the 3D-NOD framework are publicly available in a GitHub repository.Dataset · publicOur data and the code are available at: https://github.com/zingersu/3D-New-Organ-Detection-in-Plant-Growth-from-Spatiotemporal-Point-Clouds.Open asset ↗https://github.com/zingersu/3D-New-Organ-Detection-in-Plant-Growth-from-Spatiotemporal-Point-Cloudshtml-lines:481-514Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In the era of machine learning-driven plant imaging, the production of annotated datasets is a very important contribution. In this data paper, a unique annotated dataset of seedling emergence kinetics is proposed. It is composed of almost 70,000 RGB-depth frames and more than 700,000 plant annotations. The dataset is shown valuable for training deep learning models and performing high-throughput phenotyping by imaging. The ability of such models to generalize to several species and outperform the state-of-the-art owing to the delivered dataset is demonstrated. We also discuss how this dataset raises new questions in plant phenotyping.
Why it matches plant phenotyping methods植物の出芽速度を対象とする大規模RGB深度画像・アノテーションデータセットを提供し、深層学習および高スループット表現型解析への利用性を実証しており、表現型取得基盤が中心である。
abstracta unique annotated dataset of seedling emergence kinetics is proposed
Reproduction assets foundThis is a data paper whose core contribution is a public annotated RGB-depth seedling dataset (~70,000 frames, >700,000 annotations) deposited in DATA INRAE with DOI 10.57745/AMFJTK, explicitly stated as publicly accessible. Other allowed URLs (license, Intel datasheet, Jülich record) are not paper-specific assets.Dataset · publicSynthesis of the full time-lapse and RGB-Depth full frame quantity per species
Species
Pots time-lapse
Labelled pots time-lapse
RGB-depth full frame
Rapeseed
1 760
336
15 218
Tomatoes
1 960
480
33 283
Beans
2 320
400
21 445
Total
6 040
1 216
69 946
The dataset is publicly accessible in the DATA INRAE repository, DOI: https://doi.org/10.57745/AMFJTK . The file tree structure is illustrated in Fig. 4 . The dataset is organized into 11 compressed .zip files, each corresponding to a distinct trial. Within these files, images are sorted chronologically by acquisition start date, then by camera, and stored in .png format within dedicated color and depth folders. Labels are alsoOpen asset ↗DATA INRAE · 10.57745/AMFJTKlines:105-195Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Plant-pathogenic bacteria colonise their hosts using various strategies, exploiting both natural openings and wounds in leaves and roots. The vascular pathogen Xanthomonas campestris pv. campestris (Xcc) enters its host through hydathodes, organs at the leaf margin involved in guttation. Subsequently, Xcc breaches the hydathode-xylem barrier and progresses into the xylem vessels causing systemic disease. To elucidate the mechanisms that underpin the different stages of an Xcc infection, a need exists to image bacterial progression in planta in a non-invasive manner. Here, we describe a phenotyping setup and Python image analysis pipeline for capturing 16 independent Xcc infections in Arabidopsis thaliana plants in parallel over time. The setup combines an RGB camera for imaging disease symptoms and an ultrasensitive CCD camera for monitoring bacterial progression inside leaves using bioluminescence. The method reliably quantified bacterial growth in planta for two bacterial species, that is, vascular Xcc and the mesophyll pathogen Pseudomonas syringae pv. tomato (Pst). The camera resolution allowed Xcc imaging already in the hydathodes, yielding reproducible data for the first stages prior to the systemic infection. Data obtained through the image analysis pipeline was robust and validated findings from other bioluminescence imaging methods, while requiring fewer samples. Moreover, bioluminescence was reliably detected within 5 min, offering a significant time advantage over our previously reported method with light-sensitive films. Thus, this method is suitable to quantify the resistance level of a large number of Arabidopsis thaliana accessions and mutant lines to different bacterial strains in a non-invasive manner for phenotypic screenings.
Why it matches plant phenotyping methods植物感染を非侵襲的に画像化・定量するフェノタイピング装置とPython解析パイプラインを開発し、複数の細菌感染で検証しているため、方法が中心的です。
abstractHere, we describe a phenotyping setup and Python image analysis pipeline for capturing 16 independent Xcc infections in Arabidopsis thaliana plants in parallel over time.
Reproduction assets foundThe paper's Python image analysis pipeline (Digital phenotyper) for quantifying bioluminescent bacterial infection in Arabidopsis is explicitly and publicly deposited by the authors on GitHub.Code · publiccsv file and an overlayed image (.png file) of the RGB and CCD image was created for visual inspection. The pipeline features an environment file in which the different parameters can be adjusted to optimise the pipeline for other setups. All available parameters, code and instructions for this pipeline are provided on GitHub ( https://github.com/MolPlantPathology/Digital_phenotyper ).
2.3
Digital Phenotyping Quantifies Disease Severity at Different Stages of Infection
To confirm the validity of our method, we benchmarked our digital phenotyping pipeline against other well‐established methods. To do so, we performed spray inoculations of Xcc8004 Δ xopAC Tn 7:lux on three Arabidopsis genotypeOpen asset ↗MolPlantPathology/Digital_phenotyperlines:101-107Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Soil imaging in the field and laboratory has greatly advanced our understanding of plant root systems. Soil fungi function as important plant symbionts and decomposers of complex organic material in soil environments. For fungal hyphae, however, the application of soil imaging remains scarce, limiting our understanding of hyphal systems in soil. This scarce application is partly due to the challenging development of a soil imaging device for hyphae: technical requirements to resolve fine hyphae (2-5 μm in diameter) are high, while the device cost must be low to facilitate sufficient deployment that can capture the high spatial heterogeneity of hyphal dynamics in soil. This protocol describes the do-it-yourself assembly and application of a low-cost high-resolution imaging device for observing hyphae in soil. The assembly of the open-source imaging device relies on many 3D-printed parts, reducing material costs to ca. 930 USD. The application of the imaging device yields soil profile images with a resolution of up to 0.52 μm px-1 (49000 dpi) within an observable volume of 70 × 210 × 1.5 mm. By repeatedly imaging a soil profile using the presented techniques, changes in the amount, distribution, and morphology of hyphae in soil can be observed and quantified.
Why it matches plant phenotyping methods土壌中の菌糸の量・分布・形態を画像から観察・定量する低コスト高解像度イメージング装置の組立・応用プロトコルであり、植物関連状態の取得手法が中心である。
abstractThis protocol describes the do-it-yourself assembly and application of a low-cost high-resolution imaging device for observing hyphae in soil.
Reproduction assets foundThe paper deposits its imaging-device design files, operating software, and high-resolution soil profile image sets on Zenodo, all paper-specific and publicly available.Dataset · publicerials and methods
The protocol described in this peer-reviewed article is published on protocols.io, https://dx.doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1 , and is included for printing as S1 File with this article. Furthermore, three sets of high-resolution images yielded following the protocol are available from Zenodo at https://doi.org/10.5281/zenodo.10730414 . The high-resolution images were acquired between May and October 2023 in a Quercus serrata grove at the Kansai Research Center of the Forestry and Forest Products Research Institute (FFPRI) in Kyoto City, Japan (34°56’N, 135°46’E). The grove was located on a Cambisol [ 44 ] in flat terrain. Monthly mean air temperature rangedOpen asset ↗Zenodo · 10.5281/zenodo.10730414lines:31-38Dataset · publicl.
The protocol is also available on protocols.io.
(PDF)
S1 Dataset
Set of soil profile images acquired at focus depths of 0, 0.025, and 0.05 mm.
Original images in the JPG format taken at an imaging resolution of 0.65 μm px -1 (39200 dpi).
(ZIP)
Data Availability Statement
All design files used to assemble the imaging device ( https://doi.org/10.5281/zenodo.10689905 ), all software to operate the imaging device ( https://doi.org/10.5281/zenodo.10815832 ), and several sets of images yielded with the imaging device ( https://doi.org/10.5281/zenodo.10730414 ) are available from the data repository Zenodo. All remaining data are within the manuscript and its Supporting Information files.Open asset ↗Zenodo · 10.5281/zenodo.10689905lines:147-155Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor- and time-intensive due to challenging environmental conditions and highly dynamic plant processes. To enable more detailed studies on leaf angles, we modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision. We demonstrate the system's accuracy and reliability, with minimal deviation from reference values. The method can be utilized by other researchers to gather data on leaf angles and other structural plant traits at regular intervals to access the dynamics of leaves, plants, and canopies. The system's low cost and adaptability can enhance the efficiency of crop monitoring in plant breeding and phenotyping experiments. Detailed documentation and code are available on GitHub.•An open-source farming robot is retrofitted to function as an automatic data collection platform•Hard to access leaf angles can be retrieved with high accuracy•Leaf angle dynamics can be observed with high temporal resolution.
Why it matches plant phenotyping methodsステレオビジョンを用いて葉角度を高精度・高頻度に測定するロボット基盤を開発・改良し、精度と信頼性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 modified a well-established automated farming robot to obtain high-resolution 3D point clouds at customizable intervals of individual plants using stereo vision.
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 used codes and recorded data are available at: https://github.com/FrederikHennecke/PointCloudHarvest .Open asset ↗FrederikHennecke/PointCloudHarvestlines:218-236Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades. However, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plants, and phenotypic traits result from the cumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. Previous studies have selected a set of genes potentially affecting Sorghum nitrogen responsiveness to be characterized. The knockout mutants of these genes are generated using the CRISPR-Cas9 technique. Using a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, temporal pixel count and greenness index traits were extracted as a proxy of plant growth and N responses, which, subsequently, were modeled by mathematical functions, allowing us to estimate seven key parameters from the growth curves. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters, with the Edit 1 showing especially reduced sensitiveness to use the available N resources. This high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像を用いた時系列の植物表現型取得と、画素数・緑色度および成長曲線パラメータの抽出から成る高スループット表現型パイプラインが研究の中心である。
abstractUsing a LemnaTec plant imaging system, this study obtained time series imagery data from 29 to 130 d after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the raw LemnaTec imagery datasets and the extracted phenotypic data (pixel count and greenness index time series) in a public GitHub repository, which directly reproduces this paper's plant-phenotyping measurements.Dataset · publicJC, Yang J; experimental data generation: Jin H,
Park A, Li G; data analysis and interpretation of results: Jin H,
Sreedasyam A. All authors reviewed the results and approved the final
version of the manuscript.
Data availability
The raw imagery datasets and the extracted phenotypic data are
available in the GitHub repository: https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping.Acknowledgments
This project was supported by the US Department of Energy (Grant No.
DE-SC0023138), and the National Science Foundation under the award
number OIA-1826781.
N responses of sorghum mutants
Page6of8 Jin et al.GenomicsCommunications 2025, 2: e010Open asset ↗Sorghum-edits-N-Phenotypingpdf-raw-page:6 lines:77-121Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHDALS 2023 was 1.64, and the CHDCAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands.
Why it matches plant phenotyping methodsALSと航空写真測量による樹高・森林垂直構造・樹冠高多様性の推定を現地測定と比較検証しており、植物（森林）の形態形質測定が研究の中心です。
abstractThe comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data
Reproduction assets foundThe paper's own field measurements (2013, 2023) and derived analysis data are not publicly deposited; the Data Availability Statement says raw data are available only upon reasonable request from the corresponding author. The freely available national ALS/DAP source data are public via the Slovenian national remote-snsDataset · publicmote Sens. Environ. 2018, 208, 1–14. [CrossRef]
14. Goodbody, T.R.H.; Coops, N.C.; White, J.C. Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review
of Opportunities, Challenges, and Future Directions. Curr. For. Rep. 2019, 5, 55–75. [CrossRef]
15. GURS. Daljinsko zaznavanje. 2024. Available online: https://www.e-prostor.gov.si/podrocja/drzavni-topografski-sistem/daljinsko-zaznavanje/ (accessed on 13 November 2024).
16. Haala, N. The landscape image matching algorithms. In Proceedings of the 54th Photogrammetric Week, Stuttgart, Germany,
9–13 September 2013; pp. 271–284.
17. Triglav Čekada, M.; Bric, V. Končan je projekt Laserskega skeniranja Slovenije. Geod. VOpen asset ↗GURSpdf-raw-page:14 lines:1-48Code / 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades; however, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plant, phenotypic traits result from the accumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. To characterize the targeting N-responsiveness and growth trajectory, we employed CRISPR-Cas9 technique to generate sorghum mutants using CRISPR technology. Using a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, we extracted a number of morphological and greenness index traits as a proxy of plant growth and N responses. Subsequently, we employed two different methods to model the temporal N-responsive traits, allowing us to estimate seven key parameters from the growth curve. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters. The high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.
Why it matches plant phenotyping methodsLemnaTec画像による時系列形質取得と成長曲線モデリングを組み合わせた高スループット表現型解析パイプラインが、研究の主要な技術的要素として記述されています。
abstractUsing a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions.
Reproduction assets foundThe paper's Supporting Information section links four public GitHub-hosted supplementary data files containing the paper-specific phenotypic values (fitted pixel count and ExG curve parameters) and statistical contrasts, directly reproducing this study's sorghum N-response phenotyping measurements and analysis outputs.Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrastOpen asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitpx.csvpdf-raw-page:11 lines:1-21Supplement · publicSupporting Information
Supporting Tables
Table S1. The phenotypic values calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitpx.csv)
Table S2. The phenotypic values calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/fitexg.csv)
Table S3. The contrasts of the phenotypes calculated from the pixel count curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/PXcontrasts.xlsx)
Table S4. The contrasts of the phenotypes calculated from the ExG curves.
(https://github.com/JIN-HY/Sorghum-edits-N-Phenotyping/blob/main/ExGcontrast.xlsx)
11/14Open asset ↗JIN-HY/Sorghum-edits-N-Phenotyping · fitexg.csvpdf-raw-page:11 lines:1-21Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
Dissecting the drought resistance (DR) mechanism and designing drought-resistant rice varieties are promising strategies to address the challenge of climate change. Here, we selected a typical drought-avoidant (DA) variety IRAT109 and drought-tolerant (DT) variety Hanhui15 as the parents to develop a stable recombinant inbred line (RIL) population (F 8 , 1,262 lines). The de novo assembled genomes of both parents were released. Through re-sequencing of the RIL population, a set of 1,189,216 reliable SNPs were obtained and used for constructing a dense genetic map. Using both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period and identified 32,586 drought-responsive quantitative trait loci (QTLs) including 2,097 unique QTLs. The QTLs related to panicle i-traits occurred on the middle of chromosome 8 over 600 times, while the QTLs related to leaf i-traits on the 5’ end of chromosome 3 over 800 times, indicating potential effect of these QTLs on plant phenotypes. We chose three candidate genes ( OsMADS50, OsGhd8, OsSAUR11 ) related to leaf, panicle, and root traits respectively and verified their functions in resisting drought. Gene OsMADS50 was found to negatively regulate DR by modulating leaf dehydration, grain size, and root downward growth. Furthermore, a total of 18 and 21 composite QTLs significantly related to grain weight and plant biomass were screened from 597 lines in RIL population under drought conditions in field experiments, and composite QTL region was highly overlapped (76.9%) with known DR gene region. Based on three candidate DR genes, we proposed the haplotype design suitable for different environments and breeding objectives. This study provides a valuable reference for multi-modal and time-series phenomic analyses, deciphers the genetic mechanism of DA and DT rice varieties, and offers a molecular navigation map for breeding DR variety.
Why it matches plant phenotyping methods地下・地上フェノミックプラットフォームとマルチモーダルカメラで全生育期間の画像形質を大量取得しており、フェノタイピング手法の適用と技術的ワークフローが研究の中核です。
abstractUsing both aboveground and underground phenomic platforms and multimodal cameras, we captured 139,040 image-based traits (i-traits) of whole plant’s phenotypes in response to drought stress throughout entire rice growth period
Reproduction assets foundThe paper's phenome data (aboveground and belowground rice images/i-traits) and the authors' data-handling code and deep-learning model are explicitly deposited at public URLs listed in the Data Availability Statement. Genome data (riceome.hzau.edu.cn) is molecular omics and excluded.Code · publicAll the phenome data and core data-handling code have been deposited online.Open asset ↗lines:140-175Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant net photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites. The final dataset (https://doi.org/10.5281/zenodo.12191876; Zahn and Bou-Zeid, 2024) spans a 5-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing confidence in these estimates. Partitioning of CO2 components, on the other hand, was found to be less robust and more dependent on prior knowledge of water use efficiency. This highlights some limitations of these present methods that we discuss, emphasizing the broader challenge posed by the lack of an accurate reference method to validate against. Despite these limitations, this dataset has several potential applications, especially in addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods複数手法で蒸散・植物純光合成などの植物生理フラックスを分離推定し、手法間比較と妥当性・限界評価を行った大規模データセットであり、植物状態の取得手法が中心である。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total evapotranspiration (ET) and CO2 fluxes into plant and soil fluxes across 47 National Ecological Observatory Network (NEON) sites.
Reproduction assets foundThe paper's flux-partitioning dataset (transpiration, evaporation, plant photosynthesis, soil respiration across 47 NEON sites) is publicly deposited on Zenodo, and the authors' scripts implementing all five partitioning methods are also publicly available on Zenodo with explicit availability statements.Dataset · publicl. ( 2020 ) across FLUXNET sites. By comparing different algorithms, we can further explore their uncertainties and focus on model improvement. Finally, as more data become available, other options can be used to train machine learning algorithms, focusing on gap-filling.
7 Code and data availability
The dataset is available at https://doi.org/10.5281/zenodo.12191876 ( Zahn and Bou-Zeid , 2024 ) . In addition to all the flux components, it contains the auxiliary meteorological inputs used to implement the Extreme Gradient Boosting algorithm for gap-filling and feature importance analysis. The scripts used to implement all five partitioning methods can be found at https://doi.org/10.5281/zenOpen asset ↗Zenodo · 10.5281/zenodo.12191876lines:346-361Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
As climate changes, maintenance of yield stability requires efficient selection for drought tolerance. Drought-tolerant cultivars have been successfully but slowly bred by yield-based selection in arid environments. Marker-assisted selection accelerates breeding but is less effective for polygenic traits. Therefore, we investigated a selection based on phenotypic markers derived from automatic phenotyping systems. Our trial comprised 64 potato genotypes previously characterised for drought tolerance in ten trials representing Central European drought stress scenarios. In two trials, an automobile LIDAR system continuously monitored shoot development under optimal (C) and reduced (S) water supply. Six 3D images per day provided time courses of plant height (PH), leaf area (A3D), projected leaf area (A2D) and leaf angle (LA). The evaluation workflow employed logistic regression to estimate initial slope (k), inflection point (Tm) and maximum (Mx) for the growth curves of PH and A2D. Genotype × environment interaction affected all parameters significantly. Tm(A2D)ₛ and Mx(A2D)ₛ correlated significantly positive with drought tolerance, and Mx(PH)ₛ correlated negatively. Drought tolerance was not associated with LAc, but correlated significantly with the LAₛ during late night and at dawn. Drought-tolerant genotypes had a lower LAₛ than drought-sensitive genotypes, thus resembling unstressed plants. The decision tree model selected Tm(A2D)ₛ and Mx(PH)c as the most important parameters for tolerance class prediction. The model predicted sensitive genotypes more reliably than tolerant genotype and may thus complement the previously published model based on leaf metabolites/transcripts.
Why it matches plant phenotyping methods自動LIDARによる連続3D画像取得と、植物形態・成長形質の抽出および解析ワークフローが、乾燥耐性評価の中心的手法として用いられている。
abstractwe investigated a selection based on phenotypic markers derived from automatic phenotyping systems.
Reproduction assets foundThe paper's LIDAR phenotyping and yield data are deposited publicly in E!DAL (Köhl et al. 2022, doi 10.5447/ipk/2022/12). The SAS analysis scripts are only available from the corresponding author (request_only).Dataset · publicData availability All data are available at E!DAL (Köhl et al. 2022). Material and SAS scripts used for
evaluation are available from the corresponding author.Open asset ↗E!DALpdf-page:27 lines:1-62Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Common beanAerial / UAVField / plotLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldCountingMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenology
Substantial effort has been made in manually tracking plant maturity and to measure early-stage plant density and crop height in experimental fields. In this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH), potentially offering higher throughput, accuracy, and cost-effectiveness than traditional methods. A time series of drone images was utilized to estimate dry bean RM employing a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model. For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. PH was obtained using a quantile method from digital surface model (DSM) and point cloud (PC) data sources. The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. The inclusion of growing degree days (GDD) data improved the model's performance under specific environmental stresses. The Faster R-CNN model effectively identified early-stage bean plants, demonstrating superior accuracy over traditional methods and consistency across different flight altitudes. For PH estimation, moderate correlations with ground-truth data were observed across both datasets analyzed. The choice between PC and DSM source data may depend on specific environmental and flight conditions. Overall, the CNN-LSTM and Faster R-CNN models proved more effective than conventional techniques in quantifying RM and SC. The subtraction method proposed for estimating PH without accurate ground elevation data yielded results comparable to the difference-based method. Additionally, the pipeline and open-source software developed hold potential to significantly benefit the phenotyping community.
Why it matches plant phenotyping methodsドローン画像と深層学習を用いて成熟度、株数、草丈を推定する手法を開発・評価し、パイプラインとオープンソースソフトウェアも提示しており、植物表現型取得が研究の中心である。
abstractIn this study, RGB drone imagery and deep learning (DL) approaches are explored to measure relative maturity (RM), stand count (SC), and plant height (PH)
Reproduction assets foundThe paper explicitly states that all R/Python analysis code, apps, and the complete datasets (orthomosaics, shapefiles, ground notes, clipped plots) are publicly available via the authors' GitHub organization and three Zenodo deposits for RM, SC, and PH.Dataset · publicof the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vuOpen asset ↗zenodo · 10.5281/zenodo.7922565lines:677-730Dataset · publicauthors declare that they have no competing interests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainabOpen asset ↗zenodo · 10.5281/zenodo.7922584lines:677-730Dataset · publicrests.
Data Availability
Developed software and analysis are available in the GitHub repositories at https://github.com/msudrybeanbreeding and datasets can be download at Zenodo deposit page ( https://zenodo.org/ ) using the links to RM: https://doi.org/10.5281/zenodo.7922565; SC: https://doi.org/10.5281/zenodo.7922584; and PH: https://doi.org/10.5281/zenodo.7922589 .
Supplementary Materials
Supplementary 1
Figs. S1 to S14
Tables S1 and S2
Data files S1 to 21
References
1.
Uebersax
MA , Cichy
KA , Gomez
FE , Porch
TG , Heitholt
J , Osorno
JM , Kamfwa
K , Snapp
SS , Bales
S
.
Dry beans ( Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review
. LeguOpen asset ↗zenodo · 10.5281/zenodo.7922589lines:677-730Code · publics from each individual breeding plot were extracted from the time series of images (6 and 9 flights date), and the RM was estimated using an optimized threshold value of 0.06. To perform the VI extractions from each breeding plot in the field, an open-source Streamlit app in Python was implemented and can be accessed online at: https://msudrybeanbreeding-vegetation-index--vi-extractions-v0-3-9knpzt.streamlit.app/ . Additionally, to accommodate user preferences, an R script is available to perform VI extractions analysis (Data S7 ).
SC DL model
The SC pipeline deployed in this study comprised 6 distinct steps, starting from the raw images and annotations, and ending with the final SC predictiOpen asset ↗lines:139-147Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での植物病徴を画像から取得・追跡・定量する撮像および画像解析手法を開発・検証しており、植物表現型測定が中心である。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is publicly available on the authors' GitHub repository, and that a sample dataset plus the trained reference mark detection model are downloadable from the ETH Research Collection. Both are paper-specific, public, and actionable.Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:98-107Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:98-107Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation. RESULTS: This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH. CONCLUSION: The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.
Why it matches plant phenotyping methodsフォックステールミレットの生育形質を画像から抽出する高スループット画像システムと画像処理手法を開発・評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality.
Reproduction assets foundThe paper's authors explicitly state that the source code of their foxtail millet image processing program (used to extract phenotypic i-traits such as PH, CHA, SPA, compactness indices, and colour pixel features) is publicly available on GitHub. The MDSi database URL is a general transcriptomic resource, not a paper-Code · publicmation about plant health and physiological responses.
The program was created using the OpenCV library within the Microsoft Visual Studio (C++) environment. The extracted phenotypic data were converted to CSV file format for further analysis and storage. The source code of the program is available for download and reference at https://github.com/ScreenPlant/Foxtail-Millet-Image-Processing .
Analysis of i-traits throughout the entire growth periodOpen asset ↗ScreenPlant/Foxtail-Millet-Image-Processinglines:50-61Code / 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 15 Sept 2026
Field / plotTissuePhysiological trait estimationGrowth / time-series analysisGrowth / development / phenology
Intra-annual variations of carbon stable isotope ratios (δ13C) in different tree compartments could represent valuable indicators of plant carbon source-sink dynamics, at weekly time scale. Despite this significance, the absence of a methodological framework for tracking δ13C values in tree rings persists due to the complexity of tree ring development. To fill this knowledge gap, we developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species [Picea mariana (Mill.) BSP.] during the growing season. We collected and isolated the weekly incremental growth of the cambial region and the developing tree ring from five mature spruce trees over three consecutive growing seasons (2019-21) in Simoncouche and two growing seasons (2020-21) in Bernatchez, both located in the boreal forest of Quebec, Canada. Our method allowed for the creation of intra-annual δ13C series for both the growing cambium (δ13Ccam) and developing xylem cellulose (δ13Cxc) in these two sites. Strong positive correlations were observed between δ13Ccam and δ13Cxc series in almost all study years. These findings suggest that a constant supply of fresh assimilates to the cambium-xylem continuum may be the dominant process feeding secondary growth in the two study sites. On the other hand, rates of carbon isotopic fractionation appeared to be poorly affected by climate variability, at an inter-weekly time scale. Hence, increasing δ13Ccam and δ13Cxc trends highlighted here possibly indicate shifts in carbon allocation strategies, likely fostering frost resistance and reducing water uptake in the late growth season. Additionally, these trends may be related to the black spruce trees' responses to the seasonal decrease in photosynthetically active radiation. Our findings provide new insights into the seasonal carbon dynamics and growth constraints of black spruce in boreal forest ecosystems, offering a novel methodological approach for studying carbon allocation at fine temporal scales.
Why it matches plant phenotyping methods樹木の形成層・木部における週次δ13C変動を追跡する測定法を開発し、複数年・地点で適用して検証しているため、植物の生理状態を取得する方法が中心である。
abstractwe developed a method to monitor weekly variability of δ13C in the cambium-xylem continuum of black spruce species
Reproduction assets foundThe paper's weekly δ13C cambium/xylem measurements are stated to be publicly available via the authors' Quebec-Labrador tree-ring dashboard. A GitHub repository for figure data is mentioned but without a URL and only 'upon publication', so it is not actionable. NOAA GML and the Arizona repository URL are external/citedDataset · publicCanada, 490 de La Couronne, Québec, QC G1K 9A9, Canada.
Conflict of interest
None declared.
Funding
This work was funded by the National Sciences and Engineering Research Council of Canada (NSERC) to É.B. (RGPIN 2021-04216).
Data availability
The weekly carbon isotope measurements published in the study will be available here: https://quebeclabradortr.shinyapps.io/TRdashboard4/ . Additional data used to produce the figures will be available from a GitHub repository, upon publication of the article.
References
Alvarez C, Bégin C, Savard MM, Dinis L, Marion J, Smirnoff A, Bégin Y. (2018). Relevance of using whole-ring stable isotopes of black spruce trees in the perspective of climate reconstrOpen asset ↗TRdashboard4lines:362-389Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
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 confirmedEurope PMC · checked 14 Sept 2026
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
Why it matches plant phenotyping methods植物細胞・組織の分解状態を定量する4次元蛍光共焦点イメージングと計算ツールが研究の中心であり、植物状態の形態的変化を抽出する方法を開発している。
abstractIn this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales.
Reproduction assets foundThe paper's WallTrack computational pipeline (used to track and quantify 4D confocal imaging of poplar cell wall deconstruction) is publicly available on the authors' FARE laboratory GitLab repository. The underlying imaging/phenotype data are not publicly deposited; the authors state data will be made available on.Code · publicnano and micro
scales.
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 WallTrack code is accessible through the FARE laboratory
GitLab repository at: https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d. Data will be made available on request.
Acknowledgments
The authors thank Anouck Habrant for her help in confocal imaging
and Grégoire Malandain, Solmaz Hossein Khani, Khadidja Ould Amer,
and Ali Faraj for their comments on the manuscript. This work was
supported by Agence Nationale de la Recherche (ANR) Open asset ↗https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d · refahi_et_al_4dpdf-raw-page:11 lines:1-66Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.
Why it matches plant phenotyping methods高解像度画像時系列と複数の植物形質を含む大規模な公開圃場フェノタイピングデータセットであり、再利用可能なフェノタイピング基盤・ベンチマークとして中心的です。
abstractThis data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data.
Reproduction assets foundThis data note directly publishes its own phenotyping measurements and image time series: the FIP 1.0 dataset (images, aligned image sequences, eight wheat traits, environmental and marker data) is publicly available on the ETH Research Collection and Hugging Face, and the authors' analysis/processing code is publicly,Dataset · publicn
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and Hugging Face Data set): https://doi.org/10.5061/dryad.n02v6wwzc
• Private Agroscope marker data repository: Confidential (Con-
tact: Boulos Chalhoub, boulos.chalhoub@agroscope.admin.ch).
This repository contains marker data (Illumina InfiniumOpen asset ↗mikeboss/FIP1pdf-raw-page:7 lines:1-110Dataset · publicn with FAIR principles [26]:
• Findable: This publication and the Hugging Face data set card
(https://doi.org/10.57967/hf/3191) provide detailed meta-
data and a comprehensive description of the data set’s contents,
making it discoverable to researchers.
• Accessible: The data is hosted on the Research Collection of ETH
Zurich (https://doi.org/20.500.11850/697773), a reliable and
openly accessible data storage.
• Interoperable: The use of the open-source Hugging Face
datasets [27] package makes it easy to use and export to differ-
ent formats. The data is fully MIAPPE v1.1 [28] conform. Given
the shared genotypes the data set can be used to enhance the
data by Gogna et al. [13] by 6 envOpen asset ↗20.500.11850/697773pdf-raw-page:2 lines:84-130Code · publiclly aggregating the derived data into the final
data set using the fip1-dataset repository.
In addition, the data set can be recreated using the fip1-dataset
repository from the derived data that is freely available in the ETH
research collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL Open asset ↗fip-1.0-data-set-traitspdf-raw-page:7 lines:1-110Code · publicresearch collection.
Trait Data Compilation
Project name: FIP 1.0 Data Set - Traits
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip-1.0-data-set-traits
Operating system(s): Platform independent
Programming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data AvailabiOpen asset ↗fip1-alignmentpdf-raw-page:7 lines:1-110Code · publicramming language: R, Python
License: GNU GPL v3
Image Data Alignment
Project name: fip1-alignment
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-alignment
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Set Compilation
Project name: fip1-dataset
Project home page: https://gitlab.ethz.ch/crop_phenotyping/fip1-dataset
Operating system(s): Platform independent
Programming language: Python
License: GNU GPL v3
Data Availability
• Data Repository: http://doi.org/20.500.11850/697773
• Hugging Face Data set: https://huggingface.co/datasets/mikeboss/FIP1
• Public GABI marker data repository (also integrated in main
Data Repository and HOpen asset ↗fip1-datasetpdf-raw-page:7 lines:1-110Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.
Why it matches plant phenotyping methodsカリフラワー画像時系列を用いた収穫適期という植物状態の推定手法を開発・評価し、データ取得時点の選択とモデル精度を検証しているため、フェノタイピング手法が中心である。
abstractUsing time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations.
Reproduction assets foundThe paper analyzes the GrowliFlower cauliflower UAV image time series dataset and provides a public data availability link to the dataset metadata on phenoroam.phenorob.de. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found at: https://phenoroam.phenorob.de/geonetwork/srv/eng/catalog.search#/metadata/cb328232-31f5-4b84-a929-8e1ee551d66a .Open asset ↗phenoroam.phenorob.de · cb328232-31f5-4b84-a929-8e1ee551d66alines:386-397Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.
Why it matches plant phenotyping methods航空画像・オルソモザイクから作物列を自動セグメンテーションし、時系列の生育・成長を推定する画像解析パイプラインが研究の中心であり、植物形質の取得手法として適格。
abstractThis study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops
Reproduction assets foundThe paper's Data Availability Statement points to the authors' public Mendeley Data repository (GobhiSet, DOI 10.17632/dcjjcwc5dh.4), which contains the raw, manually, and automatically annotated RGB aerial images and ortho-mosaics of cauliflower used for the YOLOv8x-seg and Grounded SAM training and growth analysis inDataset · publicon of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data was created. However, the data that were used to perform
this research can be found in the article published at https://doi.org/10.1016/j.dib.2024.110506 and
available in the repository DOI: 10.17632/dcjjcwc5dh.4 (https://data.mendeley.com/drafts/dcjjcwc5dh).Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Di, L.; Ustundag, B. Crop Growth Modeling and Yield Forecasting. In Agro-Geoinformatics; Springer: Cham, Switzerland, 2021.
[CrossRef]
2. Mithen, S.; Jenkins, E.; Jamjoum, K.; Nuimat, S.; Nortcliff, S.; Finlayson, B. Experimental crop growingOpen asset ↗data.mendeley.com · 10.17632/dcjjcwc5dh.4pdf-raw-page:17 lines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms.
Why it matches plant phenotyping methods葉の摂食痕をスキャン画像と解析パイプラインで定量し、摂食イベントごとの消費葉面積や時空間的な摂食部位を抽出する方法が研究の中心であるため、植物の損傷状態を測定するフェノタイピング手法として採用する。
abstractHere, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves.
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 documentation for the analysis pipeline is available online ( https://github.com/nsotta/feeding-mark-analysis ).Open asset ↗nsotta/feeding-mark-analysis · nsotta/feeding-mark-analysislines:91-152Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 13 Sept 2026
Abstract Molecular and physiological changes across crop developmental stages shape the plant phenome and render its prediction from genetic markers challenging. Here we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits. Using genetic markers and data from high-throughput phenotyping of a maize multi-parent advanced generation inter-cross population, we show that dynamicGP outperforms a state-of-the-art genomic prediction approach for multiple traits. We demonstrate that the developmental dynamics of traits whose heritability varies less over time can be predicted with higher accuracy. The approach paves the way for interrogating and integrating the dynamical interactions between genotype and phenotype over crop development to improve the prediction accuracy of agronomically relevant traits.
Why it matches plant phenotyping methods遺伝マーカーと高スループット表現型データを統合し、作物形質の時系列動態を予測する計算手法dynamicGPが研究の中心であり、複数形質で既存手法と比較検証している。
abstractHere we present dynamicGP, an efficient computational approach that combines genomic prediction with dynamic mode decomposition to characterize temporal changes in the crop phenotype and to predict genotype-specific dynamics for multiple traits.
Reproduction assets foundThe preprint explicitly provides public availability statements for both the maize HTP phenotypic datasets (IPK DOI repository) and the authors' R implementation of the dynamicGP algorithms (GitHub).Dataset · publicThe phenotypic data sets used in this study are available at: https://doi.ipk-gatersleben.de/DOI/0c9c6237-41f2-411f-a51e-809eb23d1088/f844533e-d775-46dd-8523-d485591f6ea8/2/1847940088Open asset ↗lines:88-96Code · publicAn R implementation of Algorithms 1 and 2 is available at https://github.com/dobby978/dynamicGPOpen asset ↗dobby978/dynamicGPlines:131-142Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract The need for more sustainable agricultural systems is becoming increasingly apparent. The global demand for agricultural products — food, feed, fuel and fiber — will continue to increase as the global population continues to grow. This challenge is compounded by climate change. Not only does a changing climate make it difficult to maintain stable yields but current agricultural systems are a major source of greenhouse gas emissions and continue to drive the problem further. Therefore, future agricultural systems must not only increase production but also significantly decrease negative environmental impacts. One approach to addressing this is to begin breeding and cultivating new plant species that have fundamental sustainability advantages over our existing crops. The Lemnaceae, a.k.a duckweeds, are one such species that have potential to increase output and reduce the negative environmental impacts of agricultural production. Herein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae. ALPHA is being used for selective breeding of one species, Lemna gibba , toward the goal of creating a new crop for use in sustainable agricultural systems. ALPHA can be used on many small aquatic plant species to assess growth rates in different environmental conditions. A proof of principle use case is demonstrated where ALPHA is used to determine saltwater tolerance of 6 different varieties of L. gibba .
Why it matches plant phenotyping methods小型水生植物の成長率を高スループットに定量する自動フェノタイピング装置を開発・実証しており、表現型取得法が研究の中心です。
abstractHerein we describe the Automated Lab-scale PHenotyping Apparatus, ALPHA, for high-throughput phenotyping of Lemnaceae.
Reproduction assets foundThe authors state that all source code for the phenotyping system, the PlantCV image analysis pipeline, the R growth-curve analysis, 3D models, and the data generated for this study (including PlantCV_Output_Salinity.csv and Barcode_Sample_Map.csv) are publicly available in the ALPHA GitHub repository.Code · publicAll source code used in the phenotyping system, 3D models for printed parts and data generated
for this study are available in the ALPHA Github repository.Open asset ↗pdf-raw-page:2 lines:1-49Dataset · publicThis code requires data output from the quantification pipeline
“PlantCV_Output_Salinity.csv” and the barcode map “Barcode_Sample_Map.csv”. Both are also
available in the Github repository.Open asset ↗pdf-raw-page:7 lines:1-33Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain. The utility of dense versus sparse temporal collections (e.g., daily, weekly, and monthly flights) has important implications for experimental design, resource allocation, and the scope of scientific questions investigated through UAS. UAS‐derived image data were collected on over 1500 maize hybrid yield trial plots with a temporal (longitudinal, 4D) sampling density of 2.8 days on average between 43 flights throughout the growing season. Correlations of vegetation index (VI) phenomic features between flight dates were generally high between flights separated by only 1 or 2 days but dropped when 3, 4, or more days separated the flights. These varied depending on specific dates and the VI used. Correlations between flights were lower around flowering time than during other parts of the season indicating the phenotypic uniqueness of this developmental period. The cross‐validation accuracy of end of season yields prediction models on untested genotypes from the UAS data (0.59 and 0.62) far exceeded genomic prediction accuracy (0.24) for the same test set hybrids regardless of whether all flight dates were used for prediction or only dates before flowering. Phenomic prediction accuracy marginally increased as additional flight dates were added throughout the season.
Why it matches plant phenotyping methodsUAS画像を用いた高頻度植物フェノタイピングの時間分解能と予測性能を評価しており、取得・解析方法の技術的検証が中心です。
abstractUnoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools
Reproduction assets foundThe paper's data availability statement explicitly provides the authors' analysis/figure-generation scripts on GitHub and both the scripts and phenotypic tabular data on Zenodo, directly enabling reproduction of this paper's UAS phenomic prediction analyses.Code · publicd by USDA award # 2022-
70412-38454 Agriculture Genome to Phenome Initiative
(AG2PI) seed grant.
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 scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E SOpen asset ↗JacobWashburn-USDA/dense_UAVpdf-raw-page:10 lines:1-332Dataset · publicE 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 scripts used in the analyses and figure generation
for this manuscript are available at https://github.com/JacobWashburn-USDA/dense_UAV. Both the scripts and
phenotypic tabular data needed to recreate the analyses are
available at https://doi.org/10.5281/zenodo.11085557.O RC I D
JacobD. Washburn https://orcid.org/0000-0003-0185-7105
Alper Adak https://orcid.org/0000-0002-2737-8041
AaronJ. DeSalvio https://orcid.org/0000-0003-1818-4699
R E F E R E N C E S
Adak, A., Anderson, S. L., & Murray, S. C. (2023). Pedigree-
management-flight interaction for temporal phenotype analysis and
temporal phenomOpen asset ↗10.5281/zenodo.11085557pdf-raw-page:10 lines:1-332Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotWhole plant / canopy / plot / fieldPhysiological trait estimationGrowth / time-series analysisPhotosynthesis / fluorescenceWater status / transpiration
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
Why it matches plant phenotyping methods植物・土壌フラックスを分離推定する5手法を47地点で実装し、手法間の一致度を評価した長期データセットであり、植物の生理状態（蒸散・光合成）の取得・推定法が中心的です。
abstractwe generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites.
Reproduction assets foundThe paper's five-year NEON flux-partitioning dataset and the authors' partitioning-method scripts are explicitly deposited on Zenodo with public DOIs.Dataset · publicows the availability
of flux components as a fraction of the total number of half-
hour periods in the record. Overall, all the methods cover
a similar temporal distribution of flux partitioning and are
potential candidates for ensemble averaging.
4 Description of the final dataset
The final dataset is available for download at
https://doi.org/10.5281/zenodo.12191876 (Zahn and Bou-
Zeid, 2024). It is organized into different folders for each
site, with each site containing a .csv file for each method.
This format is selected to be user-friendly and accessible in
various programming languages and software packages. For
FVS and CECw, in addition to their ensemble averages for
https://doi.org/Open asset ↗Zenodo · 10.5281/zenodo.12191876pdf-raw-page:9 lines:136-149Code · publicnthesis, transpiration and stomatal conduc-
tance: potential and limitations, Plant Cell Environ., 35, 657–
667, https://doi.org/10.1111/j.1365-3040.2011.02451.x, 2011.
Zahn, E.: einaraz/PartitioningMethods: Processing Eddy-
Covariance Data: Five Evapotranspiration Flux Parti-
tioning Methods (v1.0.1) [Software], Zenodo [code],
https://doi.org/10.5281/zenodo.11510363, 2024.
Zahn, E. and Bou-Zeid, E.: Partitioning of water and CO2 fluxes at
NEON sites into soil and plant components: a five-year dataset
for spatial and temporal analysis [dataset], Zenodo [data set],
https://doi.org/10.5281/zenodo.12191876, 2024.
Zahn, E., Chor, T. L., and Dias, N. L.: A Simple Methodology for
Quality ControlOpen asset ↗Zenodo · 10.5281/zenodo.11510363pdf-raw-page:22 lines:1-58Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics.
Why it matches plant phenotyping methods植物成長を対象に、遺伝・環境効果を分離し、品種別の成長曲線を推定するNLMEM/SAEM手法を中心的に提案・適用しているため、成長形質の計算的推定に該当する。
abstractWe propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth.
Reproduction assets foundThe authors state that all materials, including the real soybean UAV-derived plant-height phenotype data, are publicly available in their GitHub repository, which also contains the analysis code for the NLMEM/SAEM approach.Dataset · publics proposed in this study could genetically model the growth patterns of plants and animals, and help to understand, control, and predict their inheritance. The method proposed in this study is scalable to larger data sets owing to its computational speed. The codes for the method and the data used in this study are available at https://github.com/madelattre/Scripts-soybean-paper and can be extended according to the conditions of the application in each research project.
Supporting information
S1 File
The section entitled “Algorithmic details” describes the SAEM algorithm key distributions and the algorithm steps for both parameter estimation (Algorithm A) and genetic effects prediction (AlgoOpen asset ↗madelattre/Scripts-soybean-paperlines:207-227Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.
Why it matches plant phenotyping methodsUAV画像から植物高と成長率を抽出し、時系列形質による圃場内収量変動予測を評価しており、植物表現型の取得・解析手法が研究の中心です。
abstractTemporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield
Reproduction assets foundThe paper explicitly states that all analysis scripts are on GitHub and all UAV-derived phenotypic data (plot heights, vegetative indices, orthomosaics, DEMs, plot boundaries, masks, manual heights, yield, weather) are deposited in DRUM with a DOI.Code · publicAll of the scripts and files used to generate and analyze data are available on GitHub at
https://github.com/HirschLabUMN/Production_Drone_Height.git.Open asset ↗HirschLabUMN/Production_Drone_Heightpdf-page:17 lines:1-56Code / 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 confirmedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.
Why it matches plant phenotyping methods植物プロトプラストのクロマチン状態を高スループット画像とカスタム画像解析で定量し、100以上の特徴を抽出・ベンチマークしており、表現型取得法が研究の中心である。
abstractwe used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts.
Reproduction assets foundThe paper's chromatin feature datasets (Dryad doi:10.5061/dryad.pnvx0k6wp) and images (BioStudies S-BIAD1157) are paper-specific public assets, but their repository URLs are not among the allowed URLs, so they cannot be listed. The authors' adapted entropy analysis script is publicly available on the authors' GitHub (aCode · publicinterval (a new observation from the same group will fall inside the ellipse with probability P= 0.95).
Entropy analysis
The initial script for computing Shannon Entropy is described in Dussiau et al. (2022) and is available at https://osf.io/9mcwg/ . The adapted script for computing entropy of chromatin features is provided at https://github.com/barouxlab/ChromatinEntropy . When all cells (segmented nuclei) express the same value for a given feature, this entropy of the feature will be null. The more cell-to-cell variability for a given chromatin feature, the higher value of entropy.
Plots and statistical tests
Box plots, violin plots, scatter plots, 2D contours and histograms were created Open asset ↗barouxlab/ChromatinEntropylines:121-147Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
BACKGROUND: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap. METHODS: We present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation. RESULTS: Experiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model. CONCLUSION: The realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.
Why it matches plant phenotyping methods植物画像を生成し、そこから植物個体別形質を推定する二段階の画像ベース表現型解析フレームワークを開発・評価しており、表現型取得・推定手法が研究の中心である。
abstractWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained.
Reproduction assets foundThe paper's authors explicitly state that source code and links to the phenotyping datasets (Arabidopsis, GrowliFlower, MixedCrop) are publicly available in their GitHub repository, which implements the multi-conditional CWGAN crop growth simulation and growth estimation framework.Code · publicSource code and links to the datasets are publicly available at https://github.com/luked12/crop-growth-cgan .Open asset ↗luked12/crop-growth-cganlines:216-253Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Growth chamberRGB / grayscaleRootObject detectionSegmentationGrowth / time-series analysisBiomass / plant weightGrowth / development / phenologyRoot system architecture
Abstract Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.
Why it matches plant phenotyping methods植物根の画像分割と根バイオマス・成長の表現型抽出ワークフローを開発・検証しており、フェノタイピング手法が中心である。
abstractthis article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans.
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 · publicPython codes for root scans segmentation enabled by RhizoNet were created by the authors and are described in this paper. These codes will be available free of charge upon acceptance, and with open source at: https://github.com/lbl-camera/rhizonet .Open asset ↗lbl-camera/rhizonetlines:154-177Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.
Why it matches plant phenotyping methods高解像度画像・深層学習による器官レベル老化モニタリングと熱画像による連続的なキャノピー温度測定を開発・適用し、stay-green機能の表現型評価法として検証しているため、方法が中心的である。
abstractnewly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation
Reproduction assets foundThe paper publicly deposits its manually annotated segmentation datasets (target-domain patches for the off-nadir stem/ear segmentation model) via the ETH Zurich research repository. All other raw phenotyping data (thermal images, physiological measurements) is only available on request from the authors. Generic tools/Dataset · publicd through logical operations to obtain the fractions of green, chlorotic, and necrotic tissues for each vegetation component. For details, refer to ( Anderegg et al., 2023 ). The annotated data sets representing the target domain will be made freely available via the Repository for Publications and Research data of ETH Zürich ( https://doi.org/10.3929/ethz-b-000668219 ).
Figure 2
Effects of canopy shading on agronomic traits and canopy characteristics. Effects of shading on (A) grain yield, (B) above ground vegetative dry biomass (total above ground biomass after threshing), (C) peduncle length, (D) plant height, (E) spike volume, (F) thousand kernel weight, (G) grain protein concentration.Open asset ↗Repository for Publications and Research data of ETH Zürich · 10.3929/ethz-b-000668219lines:58-67Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
This research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops, captured via a DJI Phantom 4. The dataset, publicly accessible, comprises 244 raw RGB images, acquired over six distinct dates in October and November of 2020 as well as 6 orthomosaics from an experimental farm located in Portici, Italy. The images, uniformly distributed across crop spaces, have undergone both manual and automatic annotations, to facilitate the detection, segmentation, and growth modelling of crops. Manual annotations were performed using bounding boxes via the Visual Geometry Group Image Annotator (VIA) and exported in the Common Objects in Context (COCO) segmentation format. The automated annotations were generated using a framework of Grounding DINO + Segment Anything Model (SAM) facilitated by YOLOv8x-seg pretrained weights obtained after training manually annotated images dated 8 October, 21 October, and 29 October 2020. The automated annotations were archived in Pascal Visual Object Classes (PASCAL VOC) format. Seven classes, designated as Row 1 through Row 7, have been identified for crop labelling. Additional attributes such as individual crop ID and the repetitiveness of individual crop specimens are delineated in the Comma Separated Values (CSV) version of the manual annotation. This dataset not only furnishes annotation information but also assists in the refinement of various machine learning models, thereby contributing significantly to the field of smart agriculture. The transparency and reproducibility of the processes are ensured by making the utilized codes accessible. This research marks a significant stride in leveraging technology for vision-based crop growth monitoring.
Why it matches plant phenotyping methods作物の生育モニタリングを目的としたRGB画像・オルソモザイクの公開データセットで、手動／自動アノテーションと成長モデリングを中心的に扱っているため、植物フェノタイピング手法・データセットに該当する。
abstractThis research introduces an extensive dataset of unprocessed aerial RGB images and orthomosaics of Brassica oleracea crops
Reproduction assets foundThe paper's own GobhiSet dataset (raw RGB images, orthomosaics, manual/automatic annotations, binary masks) and Python analysis scripts are publicly deposited on Mendeley Data with an explicit direct URL and DOI.Dataset · public0137
Longitude: 14; 20; 47.7701
Data post-processing and storage location: Department of Engineering, University of Campania ‘Luigi Vanvitelli,’ Aversa, Italy
Coordinates: 40.96846317808221, 14.208207168044456
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/dcjjcwc5dh.3
Direct URL to data: https://data.mendeley.com/datasets/dcjjcwc5dh/3
1.
Value of the Data
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This dataset is a collection of multi-date aerial imagery of the Brassica oleracea var. Botrytis crop [ 1 ]. The images were acquired between the first and seventh weeks after sowing the cauliflower, with the intention of observing its growth over this period. The images were annotated with two typOpen asset ↗Mendeley Data · 10.17632/dcjjcwc5dh.3lines:50-75Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.
Why it matches plant phenotyping methodsUAV画像から形態・生理関連形質を抽出・解析する統合ソフトウェア基盤の開発と性能実証が中心であり、植物フェノタイピング手法として明確に適格です。
abstractwe developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis.
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 IHUP software developed in this study is available from https://drive.google.com/uc?export=download&id=1aZalN0yqli9l2pqyQAPK0IiACs7UhrHF . For further usage details, please contact the corresponding author.Open asset ↗lines:510-674Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Introduction: is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods: We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results: The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion: The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.
Why it matches plant phenotyping methods日次の非破壊画像計測を用いて成長関連形質とWUEを時系列で抽出し、早期表現型を解析しており、画像ベースの植物表現型取得が研究の主要な方法として記述されている。
abstractWe conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment.
Reproduction assets foundThe paper's image-derived phenotypic measurements and analysis tables (accession list with phenotypic data, germination data, heritability, trait rankings, SNP-trait correlations, candidate genes) are stated to be included in the article's Supplementary Materials, publicly available at the Frontiers supplementary URL. Supplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2024.1278802/full#supplementary-material
Supplementary File S1
Table of Bioenergy Association Panel accessions used in this study (adapted from Brenton et al., 2016 ) with image-derived phenotypic data.
Supplementary File S2
Heatmap of a kinship matrix showing correlation analysis among the 369 BAP accessions. The coloOpen asset ↗lines:229-258Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Poikilohydric plants respond to hydration by undergoing dry-wet-dry cycles. Carbon balance represents the net gain or loss of carbon from each cycle. Here we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration. Methods and results We applied the carbon balance technique to capture responses to water stress in populations of the moss Syntrichia caninervis , comparing 19 associated physiological variables. Carbon balance was negative in desiccation-acclimated (field-collected) mosses, which exhibited large respiratory losses. Contrastingly, carbon balance was positive in hydration-acclimated (lab-cultivated) mosses, which began exhibiting net carbon uptake Conclusions Carbon balance is a functional trait indicative of physiological performance, hydration stress, and survival in poikilohydric plants, and the carbon balance method can be applied broadly across taxa to test hypotheses related to environmental stress and global change.
Why it matches plant phenotyping methodsコケ植物の炭素収支を測定する標準プロトコルとカスタムチャンバーを開発し、炭素収支を機能形質として評価しているため、植物フェノタイピング手法が中心である。
abstractHere we present the first standard protocol for measuring carbon balance, including a custom-modified chamber system for infrared gas analysis, 12-h continuous monitoring, resolution of plant-substrate relationships, and in-chamber specimen hydration.
Reproduction assets foundThe authors deposit all case-study data and analysis materials in a public GitHub repository, explicitly stated in the Data Availability Statement. The R Markdown/R analysis workflow (Appendix S3) and supporting files are also provided, making the paper's carbon-balance phenotyping data and computational analysis code,Code · publiclability Statement
A detailed carbon balance protocol, RMD file, custom chamber baseplate data files, and standard curve data are available in the Supporting Information for this manuscript. All other data used in the manuscript, including in the hydration‐acclimation case study, are available via the public GitHub repository ( https://github.com/KirstenKCoe/Coe-et-al.-2024-APPS ).Open asset ↗KirstenKCoe/Coe-et-al.-2024-APPSlines:474-476Code / dataset availability 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 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
Cell plate formation during cytokinesis entails multiple stages occurring concurrently and requiring orchestrated vesicle delivery, membrane remodelling, and timely deposition of polysaccharides, such as callose. Understanding such a dynamic process requires dissection in time and space; this has been a major hurdle in studying cytokinesis. Using lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions, through the behavior of yellow fluorescent protein (YFP)-tagged cytokinesis-specific GTPase RABA2a vesicles. We monitored the entire duration of cell plate development, from its first emergence, with the aid of YFP-RABA2a, in both the presence and absence of cytokinetic callose. By developing a robust cytokinetic vesicle volume analysis pipeline, we identified distinct behavioral patterns, allowing the identification of three easily trackable cell plate developmental phases. Notably, the phase transition between phase I and phase II is striking, indicating a switch from membrane accumulation to the recycling of excess membrane material. We interrogated the role of callose using pharmacological inhibition with LLSM and electron microscopy. Loss of callose inhibited the phase transitions, establishing the critical role and timing of the polysaccharide deposition in cell plate expansion and maturation. This study exemplifies the power of combining LLSM with quantitative analysis to decode and untangle such a complex process.
Why it matches plant phenotyping methodsLLSMによる4次元画像取得と、細胞板の小胞体積を定量化する解析パイプラインの開発が研究の中心であり、植物細胞の形態・発達状態を抽出する方法として substantive です。
abstractUsing lattice light sheet microscopy (LLSM), we studied cell plate development in four dimensions
Reproduction assets foundThe paper deposits representative 4D lattice light sheet microscopy datasets (YFP–RABA2a cell plate imaging) used for its quantitative analysis on Zenodo, a paper-specific public asset. No author analysis code repository with explicit availability language is stated; the other URLs are method guidelines, not paper dataDataset · publicRepresentative datasets used in the study are available on Zenodo at https://doi.org/10.5281/zenodo.10515765 .Open asset ↗Zenodo · 10.5281/zenodo.10515765lines:85-93Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
The analysis of plant phenotype parameters is closely related to breeding, so plant phenotype research has strong practical significance. This paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle). First, the multi-output model identifies Arabidopsis accession lines and regression to predict Arabidopsis’s 22-day growth status. The experimental results showed that the model had excellent performance in identifying Arabidopsis lines, and the model’s classification accuracy was 99.92%. The model also had good performance in predicting plant growth status, and the regression prediction of the model root mean square error (RMSE) was 1.536. Next, a new dataset was obtained by increasing the time interval of Arabidopsis images, and the model’s performance was verified at different time intervals. Finally, the model was applied to classify Arabidopsis organelles to verify the model’s generalizability. Research suggested that deep learning will broaden plant phenotype detection methods. Furthermore, this method will facilitate the design and development of a high-throughput information collection platform for plant phenotypes.
Why it matches plant phenotyping methods深層学習による植物画像からの系統識別・生育状態推定を開発し、時間間隔データで検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis paper used deep learning to classify Arabidopsis thaliana from the macro (plant) to the micro level (organelle).
Reproduction assets foundThe paper's plant-phenotyping analysis is built on the Arabidopsis thaliana time-series image dataset from Namin et al., which the authors explicitly state is publicly available for download. No author analysis code or trained model is shared.Dataset · publicData is publicly available at: http://phenocam.anu.edu.au/cloud/a_data/_webroot/published-data/2017/2017-Namin-et-al-DeepPheno.zip (accessed on 16 April 2024).Open asset ↗phenocam.anu.edu.au · 2017-Namin-et-al-DeepPheno.ziplines:226-239Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Background Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.
Why it matches plant phenotyping methods自家蛍光画像と機械学習により植物のストレス組織割合を抽出し、新規な時系列ストレス表現型を算出する方法が研究の中心である。
abstractWe developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier.
Reproduction assets foundThe paper's autofluorescence image dataset (UNL-UW-AFD, 3360 images of three Brassica rapa genotypes) is explicitly stated to be publicly available for download at the authors' URL. No author analysis code with a public URL is stated.Dataset · publicwe built and made publicly available Autofluorescence Dataset collaboratively developed by the University of Nebraska–Lincoln and the University of Wyoming (UNL-UW-AFD) as a benchmark dataset, at https://plantvision.unl.edu/dataset . The dataset consists of 3360 autofluorescence images captured for three genotypes, i.e., R500 , CC , and VT .Open asset ↗plantvision.unl.edu · UNL-UW-AFDlines:339-346Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
PoplarTissueSegmentationGrowth / time-series analysisGrowth / development / phenology
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
Why it matches plant phenotyping methods再生組織を時系列で定量するセマンティックセグメンテーションを中核としたフェノミクス・ワークフローを開発し、大規模GWASに適用しているため、植物表現型取得法が中心的です。
abstractTo enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time.
Reproduction assets foundThe authors publicly release their GWAS analysis code: the MTMC-SKAT R package and the inplantaGWAS repository containing phenotype data parsing, association mapping, and downstream analysis code used in this study. The SNP and image datasets are stated to be publicly available but only via a citation (Nagle et al. 202Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/inplantaGWASlines:318-364Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/mtmcskatlines:318-364Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Abstract Background. Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results. We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, image registration, symptom tracking, and leaf- and symptom characterization. The average accuracy of the co-registration of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in lesion numbers and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions. The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under natural field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.
Why it matches plant phenotyping methods圃場での画像取得、症状検出・追跡・セグメンテーション、葉および病斑形質の定量化手法を開発・検証しており、植物病害表現型の取得が研究の中心です。
abstractWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences.
Reproduction assets foundThe paper explicitly states that all image-processing/analysis code is available at the authors' GitHub repository (and-jonas/sympathique-wheat), and that a sample data set plus the trained reference mark detection model can be downloaded from the ETH research collection (doi 10.3929/ethz-b-000659812). Both are paper-Code · publicAll code related to the processing of image time series and leaf- and lesion-level trait extraction is available from https://github.com/and-jonas/sympathique-wheat for documentation.Open asset ↗and-jonas/sympathique-wheatlines:80-87Dataset · publicA sample data set and the trained reference mark detection model can be downloaded from ETH research collection at https://doi.org/10.3929/ethz-b-000659812 .Open asset ↗10.3929/ethz-b-000659812lines:80-87Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely used in plant biology to determine growth rates and other phenotypic characters, with segmentation into foreground and background being a primary challenge. Statistical clustering and learning approaches can reduce the need for user input into this process, though these are computationally demanding, can generalise poorly and are not intuitive to end users. As such, simple strategies that rely on the definition of a range of target colors are still frequently adopted. These are limited by the geometries in color space that are implicit to their definition; i.e. thresholds define cuboid volumes and selected colors with a radius define spheroid volumes. A more comprehensive specification of target color is a hull, in color space, enclosing the set of colors in the image foreground. We developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors. We implemented convex hulls and then alpha-hulls, i.e. a limit applied to hull edge length, to support concave surfaces and disjoint color volumes. AlGrow also provides automated annotation by detecting internal circular markers, such as pot margins, and applies relative indexes to support movement. Analysis of publicly available Arabidopsis image series and metadata demonstrated effective automated annotation and mean Dice coefficients of >0.95 following training on only the first and last images in each series. AlGrow provides both graphical and command line interfaces and is released free and open-source with compiled binaries for the major operating systems.
Why it matches plant phenotyping methods植物画像から面積・成長などの表現型を抽出する画像解析ソフトウェアを開発し、Arabidopsis画像系列で性能検証しているため、方法が中心的である。
abstractWe developed AlGrow, a software tool that allows users to easily define hulls by clicking on the source image or a three-dimensional projection of its colors.
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 · public163 obtained from https://www.plant-phenotyping.org/datasets-home, as these are also able to demonstrateOpen asset ↗pdf-page:4 lines:1-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Introduction: ) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive. Method: Here we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector. Results: Applying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date. Discussion: Our optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.
Why it matches plant phenotyping methodsUAV画像と深層学習によるイネ穂検出・計数を中核とし、出穂関連形質を高スループットに定量するフェノタイピング手法およびワークフローを評価・適用している。
abstractaerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits
Reproduction assets foundThe article states that all relevant code for the UAV phenotyping and panicle detection pipeline is publicly available in the authors' GitHub repository r1cheu/phenocv. Other URLs (Ultralytics, COCO, WinQTLCart) are generic third-party tools, not paper-specific assets.Code · publicAll relevant code can be accessed at https://github.com/r1cheu/phenocv .Open asset ↗r1cheu/phenocvlines:317-328Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta , which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non- living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and fastinitiation in morning than in evening. These observations suggest that Cuscuta , despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.
Why it matches plant phenotyping methodsCuscutaのコイリングや運動動態を画像から自動抽出するPythonパイプラインの開発・信頼性検証が研究の中心であり、植物状態・形態動態のフェノタイピング手法に該当する。
abstractSubsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: the authors' Python/Jupyter notebook phenotyping pipeline on GitHub and the time-lapse video datasets used in the study hosted as a YouTube playlist. Both are directly tied to this paper's Cuscuta coiling phenotyping measurements and分析Dataset · publicNT
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All authors declare that they have no conflicts of interest.
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The developed Python-based pipeline is available as a collection of Jupyter notebooks at
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https://github.com/ejamezquita/cuscuta/ . The datasets used and/or analyzed during the current
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https://youtube.com/playlist?list=PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxru&feature=shared
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ORCID
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Max Bentelspacher https://orcid.org/0009-0004-7357-917X
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Erik J. Amézquita https://orcid.org/0000-0002-9837-0397
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Supral Adhikari https://orcid.org/0000-0002-9914-2986
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Jaime Barros https://orcid.org/0000-0002-9545-312X
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So-Yon Park https://orcid.org/0000-Open asset ↗PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxrupdf-raw-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.
Why it matches plant phenotyping methodsRGB画像からCNNで植物体高と樹冠径を予測し、予測精度を評価する手法開発・検証が研究の中心であるため。
abstractThe objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter.
Reproduction assets foundThe paper's curated dataset of morphological measurements (plant height, canopy diameter) and weekly RGB photographs of the 15 Capsicum accessions is explicitly deposited on Zenodo with a DOI matching an allowed URL. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe resulting dataset, already curated, has been made publicly available at Zenodo (Alves Barroso et al., 2024 ).Open asset ↗Zenodolines:146-227Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract Senescence is a degenerative biological process that affects most organisms. Timing of senescence is critical for annual and perennial crops and is associated with yield and quality. Tracking time-series senescence data has previously required expert annotation and can be laborious for large-scale research. Here, a convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton (Gossypium hirsutum L.), an early application of single-plant analysis (SPA). Using images from 14 UAS flights capturing most of the senescence window, the CNN achieved 71.4% overall classification accuracy across six senescence categories, with class accuracies ranging between 46.8–89.4% despite large imbalances in numbers of images across classes. For example, the number of images ranged from 109 to 1,129 for the lowest-performing class (80% senesced) to the highest-performing class (fully healthy). The results demonstrate that minimally pre-processed UAS images can enable translatable implementations of high-throughput phenotyping using deep learning methods. This has applications for understanding fundamental plant biology, monitoring orchards and other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methodsCNNとドローン時系列画像により個体ごとの綿花の老化状態を推定する手法が研究の中心であり、精度評価も実施しているため。
abstracta convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton
Reproduction assets foundThe authors state that all CNN analysis code, evaluation metrics, figure generation scripts, and the raw single-plant UAS images are publicly available in their GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicAll of the code used to assess the CNN, calculate evaluation metrics, and generate figures are available at the GitHub repository associated with this manuscript (55): https://github.com/ajdesalvio/cotton-chronology/tree/main. All files necessary to run the script, including the raw images, are available in the repository.Open asset ↗github.com/ajdesalvio/cotton-chronology · cotton-chronologylines:106-141Code / dataset availability confirmedEurope PMC · bioRxiv · checked 14 Sept 2026
ArabidopsisMesh / voxelMicroscopyFlowerSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
Background Arabidopsis thaliana sepals are excellent models for analyzing growth of entire organs due to their relatively small size, which can be captured at a cellular resolution under a confocal microscope [1]. To investigate how growth of different tissue layers generates unique organ morphologies, it is necessary to live-image deep into the tissue. However, imaging deep cell layers of the sepal is practically challenging, as it is hindered by the presence of extracellular air spaces between mesophyll cells, among other factors which causes optical aberrations. Image processing is also difficult due to the low signal-to-noise ratio of the deeper tissue layers, an issue mainly associated with live imaging datasets. Addressing some of these challenges, we provide an optimized methodology for live imaging sepals and subsequent image processing. This helps us track the growth of individual cells on the outer and inner epidermal layers, which are the key drivers of sepal morphogenesis. Results For live imaging sepals across all tissue layers at early stages of development, we found that the use of a bright fluorescent membrane marker, coupled with increased laser intensity and an enhanced Z-resolution produces high-quality images suitable for downstream image processing. Our optimized parameters allowed us to image the bottommost cell layer of the sepal (inner epidermal layer) without compromising viability. We used a ‘voxel removal’ technique to visualize the inner epidermal layer in MorphoGraphX [2, 3] image processing software. Finally, we describe the process of optimizing the parameters for creating a 2.5D mesh surface for the inner epidermis. This allowed segmentation and parent tracking of individual cells through multiple time points, despite the weak signal of the inner epidermal cells. Conclusion We provide a robust pipeline for imaging and analyzing growth across inner and outer epidermal layers during early sepal development. Our approach can potentially be employed for analyzing growth of other internal cell layers of the sepals as well. For each of the steps, approaches, and parameters we used, we have provided in-depth explanations to help researchers understand the rationale and replicate our pipeline.
Why it matches plant phenotyping methodsライブイメージング、画像処理、細胞セグメンテーションと追跡を統合した、萼片の成長・形態解析パイプラインの最適化が中心であり、植物表現型取得法に該当する。
abstractwe provide an optimized methodology for live imaging sepals and subsequent image 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 · publicThe images of the WT flowers, as well as the final edited images can be accessed at https://doi.org/10.17605/OSF.IO/UMW9B . The images shown in this manuscript correspond to WT replicate 2.Open asset ↗OSF.IO/UMW9Blines:137-166Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.
Why it matches plant phenotyping methods植物研究向けの自動化プラットフォームを開発し、画像データ取得と解析を含むduckweed成長アッセイを自動化しており、表現型取得ワークフローが中心的です。
abstractWe present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant.
Reproduction assets foundThe authors explicitly state that all design files, build instructions, example data (including the duckweed growth assay images) and analysis scripts are publicly available in their GitHub repositories, which directly reproduce this paper's duckweed phenotyping measurements and computational analysis.Code · publicData Availability: All design files, build instructions, example data and analysis scripts are available at GitHub: https://github.com/machineagency/duckbot and https://github.com/machineagency/science_jubilee/ .Open asset ↗machineagency/duckbotlines:150-163Code · publicData Availability: All design files, build instructions, example data and analysis scripts are available at GitHub: https://github.com/machineagency/duckbot and https://github.com/machineagency/science_jubilee/ .Open asset ↗machineagency/science_jubileelines:150-163Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use.
Why it matches plant phenotyping methodsUAV LiDAR点群から圃場区画ごとの作物高を自動抽出するソフトウェアと処理アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractwe present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points.
Abstract Abstract. Senescence is a highly ordered biological process involving resource redistribution away from ageing tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned/unoccupied/uncrewed aerial system/vehicle (UAS, UAV and drone) flights captured the senescence window across two experiments while functional principal component analysis effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: the red chromatic coordinate (RCC) index and the transformed normalized difference green and red (TNDGR) index. Convolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis. The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1–M6). Model performance was strongest for FPC1 scores from VSRs (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to-moderate for RCC index (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis. This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methods単一個体の綿花について、時系列UAS画像とCNN・機能的データ解析を用いて老化表現型を推定する方法が研究の中心であり、性能評価も実施している。
abstractConvolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis.
Reproduction assets foundThe paper's DATA AVAILABILITY section states that all code for FPCA, ANOVA and CNN regression, plus all files needed to run the scripts including the raw single-plant UAS images, are publicly available in the authors' GitHub repository. This is a paper-specific, public, actionable asset covering both the phenotyping (cCode · public)
graduate program. OGR and SMD were partially supported
by Cotton Incorporated Awards 18-201 and 20-724, and NSF
Award 1739092.
DATA AVAILABILITY
All of the code used for FPCA, ANOVA and CNN regres-
sion is available at the GitHub repository [see Supporting
Information—Notes S1] associated with this manuscript
(DeSalvio 2024): https://github.com/ajdesalvio/cotton-sand-wiches. All files necessary to run the scripts, including the raw
images, are available in the repository.
NSF STATEMENT
Any opinion, findings and conclusions or recommendations
expressed in this material are those of the authors(s) and do not
necessarily reflect the views of the National Science Foundation.
REFERENCES
Adak A,Open asset ↗ajdesalvio/cotton-sand-wichespdf-raw-page:15 lines:1-93Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Premise Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research. Methods We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used. Results The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP). Discussion Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development.
Why it matches plant phenotyping methods3DマイクロCT、2D画像、画像処理パイプラインを用いてランの根・果実のねじれ運動を時空間的に抽出する手法が研究の中心であり、植物形態表現型の取得に該当する。
abstractWe utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos.
Reproduction assets foundThe paper's authors publicly released their custom 3D time-lapse pipeline code on GitHub and the micro-CT reconstruction data (young and mature E. pusilla plants) on Figshare, both explicitly cited in the Data Availability Statement.Code · publicThe scripts of the newly built 3D time‐lapse pipeline are available from GitHub ( https://github.com/LMVaskimo/3D-Lapse-Pipeline ).Open asset ↗LMVaskimo/3D-Lapse-Pipelinelines:238-443Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published26 Dec 2023Integrated environmental assessment and managementCited by 3 · OpenAlex ↗
Why it matches plant phenotyping methods作物の発育段階・生育時期という植物状態を対象に、調和化データベースを開発し独立データで評価しているため、規制用途であっても植物フェノタイピングのデータ基盤・検証研究として中心的です。
abstractwe describe the development of a harmonized, pan-European, CropLife Europe Crop Development Database (C2D2)
Reproduction assets foundThe paper's own harmonized crop development database (C2D2), containing the non-disclosive raw, intermediate, and final data needed to reproduce the reported results, is openly deposited on Zenodo with an Open Data Badge.Dataset · publicThis article has earned an Open Data Badge for
making publicly available the digitally shareable data
necessary to reproduce the reported results. The data are
available at https://zenodo.org/records/8393135. Learn
more about the Open Practices badges from the Center
for Open Science: https://osf.io/tvyxz/wiki.DATA AVAILABILITY STATEMENTSTATEMENT
The raw proprietary efficacy trial data supplied by the CLE
member companies for the project are not available to en-
sure compliance with data protection (to protect the identity
of growerOpen asset ↗zenodo · 8393135pdf-raw-page:14 lines:96-150Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerClassificationGrowth / time-series analysisGrowth / development / phenology
O_LIPhenology -- the timing of recurring life history events--is strongly linked to climate. Shifts in phenology have important implications for trophic interactions, ecosystem functioning and community ecology. However, data on plant phenology can be time consuming to collect and current records are biased across space and taxonomy. C_LIO_LIHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images. We analyse >1.8 million iNaturalist records for plants listed in the National Botanical Gardens within South Africa, a country famed for its floristic diversity ([~]21,000 species) but poorly represented in phenological databases. C_LIO_LIWe were able to correctly classify images with >90% accuracy. Using metadata associated with each image, we then reconstructed the timing of peak flower production and length of the flowering season for the 6,986 species with >5 iNaturalist records. C_LIO_LIOur analysis illustrates how machine learning tools can leverage the vast wealth of citizen science biodiversity data to describe large-scale phenological dynamics. We suggest such approaches may be particularly valuable where data on plant phenology is currently lacking. C_LI
Why it matches plant phenotyping methods植物画像にCNNを適用して開花フェノロジーを分類し、開花時期と開花期間を推定する手法が研究の中心であるため、植物フェノタイピング手法研究に該当します。
abstractHere, we explore the performance of Convolutional Neural Networks (CNN) for classifying flowering phenology on a very large and taxonomically diverse dataset of citizen science images.
Reproduction assets foundThe authors publicly release all data and R code needed to recreate the analyses on GitHub (ML-Phenology-Code), and the phenotyping input data are iNaturalist research-grade observation images (1,807,310 images) downloaded via the iNaturalist GBIF DarwinCore Archive, both publicly accessible.Code · publicl images; R.D.S., N.B., and T.J.D. constructed and built
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the models. R.D.S. analysed the data; R.D.S. and T.J.D. interpreted results; R.D.S. and T.J.D.
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wrote the manuscript with significant input from N.B. and M.vdB.
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Data availability
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All data and R code needed to recreate analyses are available on GitHub at
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CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted December 23, 2023.
;
https://doi.Open asset ↗rossdstewart/ML-Phenology-Codepdf-raw-page:14 lines:1-47Code / 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 confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
PoplarLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection ( P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.
Why it matches plant phenotyping methods時系列画像フェノタイピングとFSPMを統合し、根系形態パラメータを自動抽出・評価する方法が中心である。
abstractwe applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM.
Reproduction assets foundThe paper's Data Availability statement deposits all data and R scripts (the paper's phenotyping measurements and analysis) on Zenodo, and the adapted CropRootBox.jl model code on GitHub. Only the Zenodo URL matches an allowed URL, so the Zenodo asset is reported; the GitHub repository is noted but its URL is not in anDataset · publicview and editing: S.P., D.B., K.Y., S.D., and S.-H.K.
Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The model is housed in Github at github.com/uwkimlab/CropRootBox.jl_propagation.jl . All data and and R scripts are housed in Zenodo at https://doi.org/10.5281/zenodo.8083525 .
Supplementary Materials
Supplementary 1
Figs. S1 to S7
Tables S1 to S2
Click here for additional data file.
References
1.
Seethepalli
A , Dhakal
K , Griffiths
M , Guo
H , Freschet
GT , York
LM
.
RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
RiceLaboratory / benchtopRootGrowth / time-series analysisRoot system architecture
The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of root growth of Oryza sativa, Hudsonia montana, and multiple species of orchids including Platanthera integrilabia over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time- lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.
Why it matches plant phenotyping methods植物根の長期・高スループット画像取得システムを開発し、根成長という植物形質の時系列計測に実証適用しているため、フェノタイピング手法が中心である。
abstractWe designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months.
Reproduction assets foundThe paper's authors explicitly state that all code files (robot control, QR code generation, image sorting/preprocessing) are publicly available on their GitHub organization, with URLs given in the text. No standalone phenotype dataset deposit is stated, but the authors' analysis/control code qualifies as a paper-sphenCode · publico pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All code files are available at https://github.com/the-rhizodynamics-robot .
Data Availability
All code files are available at https://github.com/the-rhizodynamics-robot .
Introduction
All organisms must perform effective environmental exploration for survival. Plants are sessile and interact with the location where they grow; therefore, they can only navigate and explore their (typically) below-groOpen asset ↗the-rhizodynamics-robotlines:71-78Code · publicprocessing and analysis of the resulting imaging data. We have included code and documentation for an image preprocessing and time-lapse movie creation system we have developed. This system uses printed QR code labels to identify the individual growth vessels from the raw images ( S3 File : QR Code Generation, code available at https://github.com/orgs/the-rhizodynamics-robot/repositories ), which are then subsequently sorted into individual subdirectories, after which (optionally) stabilized time-lapse videos are created using the open source video editing software FFMPEG [ 24 ] [ S4 File : Image Sorting, code available at https://github.com/the-rhizodynamics-robot ]. This system has been teOpen asset ↗the-rhizodynamics-robotlines:95-102Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisTissueClassificationCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Transcriptomic data can be used to predict environmentally impacted phenotypic traits. This type of prediction is particularly useful for monitoring difficult-to-measure phenotypic traits and has become increasingly popular for monitoring high-value agricultural crops and in precision medicine. Despite this increase in popularity, little research has been done on how many samples are required for these models to be accurate, and which normalization should be used. Here we create a massive RNA-seq dataset from publicly available Arabidopsis thaliana data with corresponding measurements for age and tissue type. We use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required. We find that Median Ratios Normalization significantly increases performance when predicting age. We also find that in the case of our dataset, only a few hundred samples are required to predict tissue types, and only a few thousand samples are necessary to accurately predict age. Researchers should consider these results when choosing the number of samples in a transcriptomic experiment and during data-processing. Author Summary Large datasets have become ubiquitous in both research and industry, with thousands and sometimes millions of samples being collected for a single project. In biology a prominent new technology is RNA-seq, which can be used to measure the expression level of thousands of genes for a single sample. These measurements are used for a variety of downstream applications, including predicting phenotypic traits (i.e. height, disease, etc.). A number of experiments have attempted to use RNA-seq data to make phenotype predictions with varying success. This is partially due to the small sample size of their experiments. RNA-seq datasets are currently relatively small--only a dozen to a few hundred samples--due to the cost per sample. This is expected to change as the cost of sequencing decreases. In this paper we create a massive conglomerate RNA-seq dataset from publicly available Arabidopsis thaliana RNA-seq data. We use this dataset to determine how many samples are required to accurately predict plant age and tissue type using machine learning models. We also explore the best way to normalize large datasets. Our results show the potential of massive RNA-seq datasets, and can be used to inform experimental design for phenotype prediction.
Why it matches plant phenotyping methodsRNA-seqデータから植物の年齢・組織型を予測する機械学習について、必要サンプル数と正規化法を大規模Arabidopsisデータで評価しており、表現型推定手法の検証が中心である。
abstractWe use this dataset to determine how many samples are required for accurate model prediction and which normalization method is required.
Reproduction assets foundThe paper's normalized gene expression matrices, curated phenotype annotation datasets, and intermediary files are publicly deposited on Zenodo, and all analysis code is publicly available on GitLab. These directly reproduce the paper's plant-phenotyping measurements (Arabidopsis age/tissue annotations) and modeling/MLDataset · public(NoNo). TMM normalization [24,29] and MRN normalization [25] were performed using
the Python “conorm” package 1.2.0 [30]. TPM and NoNo normalization values were an
output of Kallisto [27]. How these normalizations impacted sample count is visualized as
S2 Figure. We have made these GEMs publicly available on Zenodo at the link
https://zenodo.org/records/10183151
Sample Phenotype Annotations Pre-Processing
Sample phenotype annotations were retrieved from the NCBI BioProject database
[16,17] using BioSampleParser which was slightly modified to check for successful data
retrieval [31]. Phenotype annotations were retrieved for 48696 NCBI BioSamples,
representing data from 2643 BioProjects.Open asset ↗Zenodopdf-raw-page:9 lines:1-55Dataset · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. BostanciOpen asset ↗Zenodo · 10.5281/zenodo.10183150pdf-raw-page:43 lines:1-51Code · publicData Availability Statement
All normalized gene expression datasets, phenotype datasets, and intermediary files
created for this research are publically available on Zenodo at link
https://zenodo.org/doi/10.5281/zenodo.10183150
All code written in support of this publication is publicly available on GitLab at link
https://gitlab.com/ficklinlab-public/modeling-with-transcriptomics
Funding
This work was supported by the Washington Tree Fruit Research Commission
(WTFRC) project #AP-22-101 and USDA ARS internal appropriation funds.
References
1. Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning
Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Open asset ↗GitLabpdf-raw-page:43 lines:1-51Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. We present a two-stage framework consisting first of an image prediction model and second of a growth estimation model, which both are independently trained. The image prediction model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate different conditions along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors of different kinds. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. For various crop datasets, the framework allows realistic, sharp image predictions with a slight loss of quality from short-term to long-term predictions. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between an image- and process-based crop growth model.
Why it matches plant phenotyping methods画像生成と成長推定を統合したフレームワークを開発し、生成画像から植物個体の形質を抽出・比較することが中心であるため、植物フェノタイピング手法として含める。
abstractWe present a two-stage framework consisting first of an image prediction model and second of a growth estimation model, which both are independently trained.
Reproduction assets foundThe paper explicitly states that its source code (the multi-conditional CWGAN crop growth simulation framework) is publicly available on GitHub. The SIMPLACE documentation URL is a generic external resource, not a paper-specific asset.Code · publicwth estimation model.
A transferability experiment demonstrates that our framework has the potential to be transferred to crop mixtures in another field with different environmental conditions.
2 Materials and Methods
This section introduces the data basis (Sec. 2.1 ) and the framework 1 1
1
Source code is publicly available at https://github.com/luked12/crop-growth-cgan , where a 2-step approach is followed.
First, an image is predicted (Sec. 2.2 ), and second, the growth is estimated using plant phenotyping (Sec. 2.3 ).
While existing state-of-the-art models are used for growth estimation, which is fine-tuned on our data, the methodological focus of this work is on the first part, image prOpen asset ↗luked12/crop-growth-cganlines:128-235Code / dataset availability confirmedCrossref · checked 7 Sept 2026
The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path.
Why it matches plant phenotyping methodsハイパースペクトル由来の2種類の正規化差分指標を組み合わせ、作物の成長・老化過程や生育段階を時系列で推定する手法が研究の中心であるため。
abstractBy using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only.
Reproduction assets foundThe paper's analysis is based on the publicly available GHISA EO-1 Hyperion hyperspectral dataset from USGS, explicitly named in the Data Availability Statement. No author analysis code or trained models are deposited.Dataset · publicData Availability Statement: The data are publicly available at https://www.usgs.gov/media/files/
ghisa-usa-eo-1-hyperion-dataset (accessed on 14 November 2023).Open asset ↗ghisa-usa-eo-1-hyperion-datasetpdf-page:13 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Within-host spread of pathogens is an important process for the study of plant-pathogen interactions. However, the development of plant-pathogen lesions remains practically difficult to characterize beyond the common traits such as lesion area. Here, we address this question by combining image-based phenotyping with mathematical modelling. We consider the spread of Peyronellaea pinodes on pea stipules that were monitored daily with visible imaging. We assume that pathogen propagation on host-tissues can be described by the Fisher-KPP model where lesion spread depends on both a logistic growth and an homogeneous diffusion. Model parameters are estimated using a variational data assimilation approach on sets of registered images. This modelling framework is used to compare the spread of an aggressive isolate on two pea cultivars with contrasted levels of partial resistance. We show that the expected slower spread on the most resistant cultivar is actually due to a significantly lower diffusion coefficient. This study shows that combining imaging with spatial mechanistic models can offer a mean to disentangle some processes involved in host-pathogen interactions and further development may allow a better identification of quantitative traits thereafter used in genetics and ecological studies.
Why it matches plant phenotyping methods画像ベースの病斑追跡と時空間モデルを組み合わせ、病斑拡散パラメータを推定する手法が研究の中心であるため。
abstractHere, we address this question by combining image-based phenotyping with mathematical modelling.
Reproduction assets foundThe article cites two public Recherche Data Gouv deposits containing this paper's own phenotyping assets: the image sequences of growing lesions on pea stipules used for monitoring, and the segmentation outputs used for image-based phenotyping. Both are explicitly referenced with DOIs in the reference list.Dataset · publicImage sequences of growing lesions—Ascochyta blight of pea. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/MQXKCP .Open asset ↗Recherche Data Gouv · 10.57745/MQXKCPlines:296-384Dataset · publicSegmentation of ascochyta blight symptoms on pea stipules. Recherche Data Gouv; 2022. Available from: https://doi.org/10.57745/5B1XGU .Open asset ↗Recherche Data Gouv · 10.57745/5B1XGUlines:296-384Code / 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
831
Code to reproduce the entire workflow including calibration and validation data is
832
available at https://github.com/EOA-team/sentinel2_crop_trait_timeseries
833
under GNU General Public License v3.0.
834
Credit Authorship Contribution Statement
835
Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
836
dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
837
Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.
Why it matches plant phenotyping methods植物画像から成長状態を抽出する画像セグメンテーション手法を開発し、ハイスループット表現型解析プラットフォームで評価しているため、方法が研究の中心である。
abstractThe Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework
Reproduction assets foundThe paper's authors publicly released both their analysis code (OSC-CO2 framework on GitHub) and the paper-specific plant phenotyping image dataset (CosegPP+, a VSTEM plant imagery dataset from the UNL LemnaTec platform) with explicit availability statements and URLs.Code · publicthe object’s (plant’s) shape, orientation, and size at a specific point in time. OSC-CO 2 is designed to process datasets that contain a variety of features, such as perspective (V), species (S), temporality (T), environmental conditions (E) and modality (M) (VSTEM) (
Figure 1
). The code for OSC-CO 2 is publicly available at: https://github.com/rubiquinones/OSC-CO2 .
Figure 1
A preview of a VSTEM Dataset. This work will use the CosegPP dataset ( Quiñones et al., 2021 ) and modify it as CosegPP+ and categorize it as a VSTEM dataset for our problem definition. The first row shows the growth sequence of a Buckwheat plant from 3 rd July 2019 to 27 th July 2019. The second row shows the threeOpen asset ↗rubiquinones/OSC-CO2lines:37-49Dataset · publicasets through segmentation using Otsu’s method ( Otsu, 1979 ) and cosegmentation using Subdiscover ( Meng et al., 2016 ). These two methods were chosen since ( Quiñones et al., 2021 ) defined these as the top methods for being able to segment some of the challenging features of computer vision. CosegPP+ is publicly available at https://doi.org/10.5281/zenodo.6863013 .
We replaced the original images with the outputs generated by Otsu’s method and Subdiscover. Meaning that each time point
i
will have at most
a
binary images where
a
is the number of algorithms (i.e., Otsu’s method and Subdiscover) used. Some groups do not contain Subdiscover binary masks due to the method’s limitation in notOpen asset ↗10.5281/zenodo.6863013lines:350-411Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.
Why it matches plant phenotyping methods圃場の時系列高解像度画像からキャノピー被覆率の変化を定量化し、霜害指数（FDI）を開発・検証しており、画像ベース表現型取得が研究の中心である。
abstractImage-based field phenotyping methods allow quantifying not only plant growth but also plant senescence.
Reproduction assets foundThe authors state that all analysis code for the Frost Damage Index is publicly available on GitLab with example data; the full phenotype dataset is only available upon request.Code · publicAll code is available at https://gitlab.ethz.ch/ftschurr/fdi_example with example data. All data are available upon reasonable request.Open asset ↗gitlab.ethz.ch/ftschurr/fdi_examplelines:81-106Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
Why it matches plant phenotyping methods葉色画像から個体を自動検出し、深層学習で葉色およびアントシアニン含量を推定する撮像・解析手法を開発し、大規模フィールドデータで適用しているため、植物フェノタイピング手法が中心である。
abstractWe developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically.
Reproduction assets foundThe paper deposits its field time-series plant images and annotation/labeling data in two Dryad repositories, and the PlantServation demo set (scripts plus demo data) on Zenodo. All are paper-specific, public, and actionable.Dataset · publicThe time-series image data for the Swiss site generated in this study have been deposited in a Dryad repository [ https://doi.org/10.5061/dryad.1g1jwsv11 ] 93 .Open asset ↗Dryad · 10.5061/dryad.1g1jwsv11lines:200-258Dataset · publicThe time-series image data for Japanese site generated in this study as well as the labeling data for image analysis used in this study are available in a Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Dryad · 10.5061/dryad.h70rxwdnklines:200-258Code · publicThe PlantServation demo set (ca. 600 MB) including scripts and demo data for PlantServation software is available at Zenodo [ https://zenodo.org/record/7321725 ] 95 accessible via Dryad repository [ https://doi.org/10.5061/dryad.h70rxwdnk ] 94 .Open asset ↗Zenodo · 7321725lines:200-258Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 10 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の動的表現型を取得・定量するオープンソース撮像プラットフォームと画像処理パイプラインの設計・実装を主題としており、植物フェノタイピング手法が中心である。
abstractHere we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's SMART image-analysis pipeline (used for all rosette and leaf-specific phenotyping measurements in this study) is explicitly stated to be publicly available as source code on GitHub and as a prepackaged Docker container, with authors' URLs given in the text.Code · public161 pipeline used are available as source code on GitHub (https://github.com/Computational-Plant-Open asset ↗Computational-Plant-pdf-page:6 lines:1-34Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
On-farm food loss (i.e., grade-out vegetables) is a difficult challenge in sustainable agricultural systems. The simplest method to reduce the number of grade-out vegetables is to monitor and predict the size of all individuals in the vegetable field and determine the optimal harvest date with the smallest grade-out number and highest profit, which is not cost-effective by conventional methods. Here, we developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis. The individual sizes were fed to the temperature-based growth model and predicted the optimal harvesting date. Two years of field experiments revealed that our pipeline successfully estimated and predicted the head size of all broccolis with high accuracy. We also found that a deviation of only 1 to 2 days from the optimal date can considerably increase grade-out and reduce farmer's profits. This is an unequivocal demonstration of the utility of these approaches to economic crop optimization and minimization of food losses.
Why it matches plant phenotyping methodsドローンリモートセンシングと画像解析により、個々のブロッコリー頭部サイズを自動・非破壊推定するパイプラインを開発・検証しており、植物形質取得が中心的です。
abstractwe developed a full pipeline to accurately estimate and predict every broccoli head size ( n > 3,000) automatically and nondestructively using drone remote sensing and image analysis.
Reproduction assets foundThe authors' full phenotyping/analysis pipeline source code is publicly available on GitHub (UAVbroccoli). Original drone image data (224 GB for 2020, 72 GB for 2021) exist but are only available upon request via Google Drive. Generic tools (YOLOv5, BiSeNet, labelme, EasyIDP, scikit-image) are third-party libraries, soCode · publicurvey powered by ML/DL for sustainable agricultural development, there are some limitations to its use. First, our system is neither fully automated nor app-based; therefore, farmers without computer science backgrounds cannot use this system directly in their own fields. However, because the source code is open to the public ( https://github.com/UTokyo-FieldPhenomics-Lab/UAVbroccoli ), local agricultural institutes and agricultural companies are able to modify and use the system according to their target. This study is definitely not a one-stop solution, but is a pioneer in real agriculture applications. Second, unlike traditional manual methods with limited throughput, the proposed method Open asset ↗UTokyo-FieldPhenomics-Lab/UAVbroccolilines:291-292Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.
Why it matches plant phenotyping methodsアヒルウキクサの画像ベース成長アッセイを含む、培養・実験管理ソフトウェアとデータ解析ワークフローの開発が中心であり、植物表現型取得を支援する方法論的貢献である。
abstractcomputational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis.
Reproduction assets foundThe paper's duckweed growth-assay experimental data and Python analysis scripts are publicly available in the authors' GitHub repository, explicitly stated in the availability statement and results sections. The Aquarium platform itself is a generic pre-existing tool, not a paper-specific asset.Code · publicAll code used in this study is available on Github.Open asset ↗lines:119-135Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Github repository, https://github.com/mtscott321/duckweed_data_analysis .Open asset ↗mtscott321/duckweed_data_analysislines:119-135Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Rust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection. Results A highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error. Conclusions A new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.
Why it matches plant phenotyping methodsエンドツーエンドのRGB画像解析パイプラインを開発・最適化・検証し、エンドウ葉のさび病症状と病勢進展を定量化する方法が研究の中心である。
abstractThis study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.
Reproduction assets foundThe authors deposited the R analysis script and the 600 pea leaflet images used in this rust phenotyping study in a public Zenodo repository, explicitly cited in the Data Availability statement and reference list.Dataset · publicThe datasets generated during and/or analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.7991462 [ 93 ].Open asset ↗Zenodo · 10.5281/zenodo.7991462lines:147-229Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background: Rapid and accurate detection of tassels is of great significance for maize breeding, seed production and the acquisition of key growth stage. To liberate manpower and improve the efficiency of production management, many automatic detection methods with acceptable accuracy have been proposed. However, images acquisition parameters of these methods were quite different, so they cannot provide an operable standard for practical applications. In this study, based on multi-temporal unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy from two aspects of efficient use of samples and data acquisition standards. Based on the detection results, we estimated tasseling date of different plots and analyzed varieties’ differences. Results: The results show that model performance would not be greatly affected before the amount of training data changed by orders of magnitude, but it can be improved effectively by adjusting sub-images’ sizes, and the final model was selected with AP@0.5IOU was 0.916; images obtained at 12 pm were more suitable for tassels detection, AP@0.5IOU, recall and precision were 3%, 2% and 6% higher than that at 8 am; optimal spatial resolution was around 1cm for tassels detection by considering the recognition effect and data acquisition efficiency. Conclusions: This study analyzed key factors affecting maize tassels detection and provided a reasonable reference for future applications, which is helpful to screen out varieties from large-scale breeding materials.
Why it matches plant phenotyping methodsUAV画像によるトウモロコシ雄穂検出のデータセット構築、検出精度の検証、撮影条件の最適化を中心とする植物フェノタイピング研究である。
abstractwe created UAV Maize Tassel Detection (UAVMTD) dataset, and used Faster R-CNN to answer what are the key factors affecting detection accuracy
Reproduction assets foundThe paper's UAV maize tassel detection dataset (UAVMTD: 142 UAV RGB images, 28,182 labeled tassels) is explicitly stated to be publicly available on the authors' GitHub repository, which is an allowed URL. No separate analysis code or trained model checkpoints are explicitly deposited.Dataset · public1 Consent for publication
2 Not applicable.
3 Availability of data and materials
4 The dataset analyzed are available at: https://github.com/Xulizzz/UAVMTD
5 Competing interests
6 The authors declare that they have no known competing financial interests or personal
7 relationships that could have appeared to influence the work reported in this paper.
8 Funding
9 This work was supported by the National Key Research and Development Program of
10 China and Shandong Province, China（20Open asset ↗Xulizzz/UAVMTDpdf-layout-page:33 lines:1-49Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.
Why it matches plant phenotyping methodsアヒルウキクサを対象に、自動化された大規模実験系と画像ベースの時系列成長データ取得・解析を開発・提示しており、植物フェノタイピング基盤が研究の中心である。
abstractWe developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' data (Dryad) and code (Zenodo) for this duckweed high-throughput imaging/phenotyping study.Dataset · publicData Availability: We have made our data and code available at the following repositories: Dryad: https://doi.org/10.5061/dryad.t4b8gtj6tOpen asset ↗Dryad · 10.5061/dryad.t4b8gtj6tlines:170-179Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 14 Sept 2026
Abstract Nondestructive plant phenotyping is fundamental for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-through phenotyping facilities can further our understanding of plant development and stress responses, their high costs significantly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration. We paired these devices with a suite of computational pipelines for integrated and straightforward data analysis. We validated the suitability of our system for large screens by evaluating a cowpea diversity panel for responses to drought stress. The observed natural variation was subsequently used for Genome-Wide Association Study, where we identified nine genetic loci that putatively contribute to cowpea drought resilience during early vegetative development. We validated the homologs of the identified candidate genes in Arabidopsis using available mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of species.
Why it matches plant phenotyping methods低コストの画像・重量ベース装置と計算パイプラインを開発し、植物成長・蒸発散を測定するフェノタイピングシステムとして検証しており、手法が研究の中心である。
abstractwe developed sets of low-cost image- and weight-based devices to monitor plant growth and evapotranspiration.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public142 how to build and program the device can be found at https://github.com/ok84-star/AAWSMO. DetailsOpen asset ↗ok84-star/AAWSMOpdf-page:6 lines:1-42Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Background Investigations into the growth and self-organization of plant roots is subject to fundamental and applied research in various areas such as botany, agriculture, and soil science. The growth activity of the plant tissue can be investigated by isotope labeling experiments with heavy water and subsequent detection of the deuterium in non-exchangeable positions incorporated into the plant biomass. Commonly used analytical methods to detect deuterium in plants are based on mass-spectrometry or neutron-scattering and they either suffer from elaborated sample preparation, destruction of the sample during analysis, or low spatial resolution. Confocal Raman micro-spectroscopy (CRM) can be considered a promising method to overcome the aforementioned challenges. The substitution of hydrogen with deuterium results in the measurable shift of the CH-related Raman bands. By employing correlative approaches with a high-resolution technique, such as helium ion microscopy (HIM), additional structural information can be added to CRM isotope maps and spatial resolution can be further increased. For that, it is necessary to develop a comprehensive workflow from sample preparation to data processing. Results A workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed. The accuracy and linearity of deuterium detection by CRM were tested and confirmed with samples of deuterated glucose. A set of root samples taken from deuterated Zea mays in a time-series experiment was used to test the entire workflow. The deuterium content in the roots measured by CRM was close to the values obtained by isotope-ratio mass spectrometry. As expected, root tips being the most actively growing root zone had incorporated the highest amount of deuterium which increased with increasing time of labeling. Furthermore, correlative HIM-CRM analysis allowed for obtaining the spatial distribution pattern of deuterium and lignin in root cross-sections. Here, more active root zones with higher deuterium incorporation showed less lignification. Conclusions We demonstrated that CRM in combination with deuterium labeling can be an alternative and reliable tool for the analysis of plant growth. This approach together with the developed workflow has the potential to be extended to complex systems such as plant roots grown in soil.
Why it matches plant phenotyping methods植物根の成長状態を測定する相関HIM-CRMワークフローを開発し、重水素検出の精度・直線性と質量分析との一致を検証しているため、植物フェノタイピング手法が中心である。
abstractA workflow to prepare and analyze roots of hydroponically grown and deuterium labeled Zea mays by correlative HIM-CRM micro-analysis was developed.
Reproduction assets foundThe paper's data availability statement deposits the datasets generated and analyzed (CRM/HIM phenotyping measurements of deuterium-labeled maize roots) in the UFZ Data Investigation Portal, a public repository with an explicit URL. No author analysis code with a public URL is stated.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the UFZ Data Investigation Portal ( https://www.ufz.de/record/dmp/archive/13952 ) repository.Open asset ↗UFZ Data Investigation Portal · archive/13952lines:198-288Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Abstract Background Significant effort has been made in manually tracking plant maturity and to measure early-stage plant density, and crop height in experimental breeding plots. Agronomic traits such as relative maturity (RM), stand count (SC) and plant height (PH) are essential to cultivar development, production recommendations and management practices. The use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost. Recent advances in deep learning (DL) approaches have enabled the development of automated high-throughput phenotyping (HTP) systems that can quickly and accurately measure target traits using low-cost RGB drones. In this study, a time series of drone images was employed to estimate dry bean relative maturity (RM) using a hybrid model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for features extraction and capturing the sequential behavior of time series data. The performance of the Faster-RCNN object detection algorithm was also examined for stand count (SC) assessment during the early growth stages of dry beans. Various factors, such as flight frequencies, image resolution, and data augmentation, along with pseudo-labeling techniques, were investigated to enhance the performance and accuracy of DL models. Traditional methods involving pre-processing of images were also compared to the DL models employed in this study. Moreover, plant architecture was analyzed to extract plant height (PH) using digital surface model (DSM) and point cloud (PC) data sources. Results The CNN-LSTM model demonstrated high performance in predicting the RM of plots across diverse environments and flight datasets, regardless of image size or flight frequency. The DL model consistently outperformed the pre-processing images approach using traditional analysis (LOESS and SEG models), particularly when comparing errors using mean absolute error (MAE), providing less than two days of error in prediction across all environments. When growing degree days (GDD) data was incorporated into the CNN-LSTM model, the performance improved in certain environments, especially under unfavorable environmental conditions or weather stress. However, in other environments, the CNN-LSTM model performed similarly to or slightly better than the CNN-LSTM + GDD model. Consequently, incorporating GDD may not be necessary unless weather conditions are extreme. The Faster R-CNN model employed in this study was successful in accurately identifying bean plants at early growth stages, with correlations between the predicted SC and ground truth (GT) measurements of 0.8. The model performed consistently across various flight altitudes, and its accuracy was better compared to traditional segmentation methods using pre-processing images in OpenCV and the watershed algorithm. An appropriate growth stage should be carefully targeted for optimal results, as well as precise boundary box annotations. On average, the PC data source marginally outperformed the CSM/DSM data to estimating PH, with average correlation results of 0.55 for PC and 0.52 for CSM/DSM. The choice between them may depend on the specific environment and flight conditions, as the PH performance estimation is similar in the analyzed scenarios. However, the ground and vegetation elevation estimates can be optimized by deploying different thresholds and metrics to classify the data and perform the height extraction, respectively. Conclusions The results demonstrate that the CNN-LSTM and Faster R-CNN deep learning models outperforms other state-of-the-art techniques to quantify, respectively, RM and SC. The subtraction method proposed for estimating PH in the absence of accurate ground elevation data yielded results comparable to the difference-based method. In addition, open-source software developed to conduct the PH and RM analyses can contribute greatly to the phenotyping community.
Why it matches plant phenotyping methodsRGBドローン画像と深層学習を用いて、乾燥豆の成熟期、株数、草高を推定する手法を開発・比較・検証しており、表現型取得が研究の中心である。
abstractThe use of RGB images collected via drones may replace traditional measurements in field trials with improved throughput, accuracy, and reduced cost.
Reproduction assets foundThe preprint explicitly states that the authors' open-source phenotyping software (RM, SC, PH pipelines) is available on GitHub, with specific tools (matuRity, Vegetation index calculator, PlantHeightR, draw-plots-qgis) hosted at public URLs, and that the datasets (orthomosaics, shapefiles, ground notes, clipped plots,Code · publicle 2: Data S1). The GCPs were input and identified into the Pix4D project using the basic manual editor before initial processing.
R [ 57 ] software integrated with QGIS [ 58 ] was used to generate the polygon shapefiles according to plot boundary delimitation using the function ‘Draw plots from clicks’ available at https://github.com/diegojgris/draw-plots-qgis (Fig. 1 -b). Shapefiles were defined using images collected from the first flight available from each location. GDAL (Geospatial Data Abstraction Library) tool plugin in QGIS was used to spatial polygon vectors (or shapefiles) adjustments with a buffer zone for each plot to prevent any influence of neighboring plots. AdditOpen asset ↗diegojgris/draw-plots-qgislines:82-143Code · public3
4. DISCUSSION
The available open source HTP tools, matuRity [ 69 ], PlantHeightR [ 93 ], and Vegetation index calculator provided in this study, have the potential to facilitate and increase the data analysis performance in plant breeding and related areas. The user can either access them on-line or download the repository at https://github.com/msudrybeanbreeding?tab=repositories . Additionally, the step-by-step pipelines deployed in this study using DL methods are available at the GitHub repositories, as well as the complete data set used to perform the analysis including orthomosaics, shapefiles, ground notes, clipped plots, and programming codes. Thus, researchers may be able to replicaOpen asset ↗msudrybeanbreedinglines:572-648Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.
Why it matches plant phenotyping methods根圏線虫感染による植物の耐性を定量化するため、画像による緑色キャノピー面積の測定と、960個体を同時測定する高スループット表現型解析プラットフォームを開発しており、表現型取得法が研究の中心である。
abstractThrough imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe paper's authors publicly deposited the full plant image dataset (green canopy phenotyping pictures) on figshare and the analysis code/model (SYLM and R growth analysis scripts) on a WUR GitLab repository, both explicitly linked in the Data availability statement.Dataset · publicAlso, the full picture dataset has been made available at doi: https://doi.org/10.6084/m9.figshare.23518923.v1 .Open asset ↗figshare · 10.6084/m9.figshare.23518923.v1lines:263-263Code · publicUsing these equations, the tolerance limit T SYLM and the minimum yield m were estimated (model and code available via gitlab: https://git.wur.nl/published_papers/willig_2023_camera-setup ).Open asset ↗git.wur.nl · published_papers/willig_2023_camera-setuplines:53-66Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.
Why it matches plant phenotyping methodsリンゴ樹の開花強度という植物形質をUAV RGB画像から推定するための3年間の画像・地上真値データセットであり、再利用可能なフェノタイピング基盤として中心的です。
abstractThe present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020.
Reproduction assets foundThis Data in Brief article describes the authors' own public Zenodo deposit containing the paper-specific UAV RGB images, flower cluster/floridity ground truth, and GCP files for the apple orchard flowering monitoring study, with direct download URL provided.Dataset · publicRepository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.6802308
Direct URL to data: https://zenodo.org/record/6802308#.YvvMFuxBz0pOpen asset ↗Zenodo · 10.5281/zenodo.6802308lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world's population, occupying an important position in China's agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice.
Why it matches plant phenotyping methodsイネの全生育期間にわたる画像ベース形質の取得・解析戦略を開発しており、フェノタイピング手法が研究の中心である。
abstractIn this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice.
Reproduction assets foundThe paper's Data Availability statement points to a public website hosting the related data and code (i-trait phenotype dataset and analysis code) for this rice image-based phenotyping study. The URL matches an allowed entry. Confidence is moderate because the statement uses future tense ('will be made available') and,Dataset · publicted in the experimental design and data analysis. W.Y. and W.H. supervised the project and designed the research. Competing interests: The authors declare that they have no competing interests.
Data Availability
The related data and code supporting the conclusion for this article will be made available on the following website: http://plantphenomics.hzau.edu.cn/usercrop/Rice/download .
Supplementary Materials
Supplementary 1
Movie S1. Dynamic graph of the processing method.
Note S1. Trait analysis technical documentation.
Table S1. Information on Oryza sativa .
Table S2. Statistical summary of the 6 developed models for estimating the panicle dry weight.
Table S3. Summary of the 5-fold crossOpen asset ↗plantphenomics.hzau.edu.cnlines:256-286Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plant architecture plays a major role in flowering and therefore in crop yield. Attempts to visualize and analyse strawberry plant architecture have been few to date. Here, we developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry. We applied this software to six seasonal strawberry varieties whose plants were exhaustively described monthly at the node scale. Results showed that the architectural pattern of the strawberry plant is characterized by a decrease of the module complexity between the zeroth-order module (primary crown) and higher-order modules (lateral branch crowns and extension crowns). Furthermore, for each variety, we could identify traits with a central role in determining yield, such as date of appearance and number of branches. By modeling the spatial organization of axillary meristem fate on the zeroth-order module using a hidden hybrid Markov/semi-Markov mathematical model, we further identified three zones with different probabilities of production of branch crowns, dormant buds, or stolons. This open-source software will be of value to the scientific community and breeders in studying the influence of environmental and genetic cues on strawberry architecture and yield.
Why it matches plant phenotyping methodsイチゴ植物体の時空間的な構造形質を取得・解析するオープンソースソフトウェアを開発しており、表現型取得・解析手法が研究の中心である。
abstractwe developed open-source software combining two- and three-dimensional representations of plant development over time along with statistical methods to explore the variability in spatio-temporal development of plant architecture in cultivated strawberry.
Reproduction assets foundThe paper's strawberry architectural phenotype data (MTG-encoded plant descriptions) are publicly deposited in the authors' GitHub repository, and the OpenAlea.Strawberry analysis/visualization software is open-source on GitHub with a Docker image for deployment. The data.inrae.fr deposits contain only a demonstration,Dataset · publicAll data are available at Github: https://github.com/openalea/strawberry/tree/master/share/dataOpen asset ↗https://github.com/openalea/strawberry/tree/master/share/datalines:406-468Code · publicFirst, OpenAlea.Strawberry is an open-source Python package ( https://github.com/openalea/strawberry ), available in the OpenAlea platformOpen asset ↗https://github.com/openalea/strawberrylines:337-344Code / 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 14 Sept 2026
In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.
Why it matches plant phenotyping methods植物の時系列成長データを用いた遺伝子型分類について、複数の機械学習・深層学習手法を比較評価しており、表現型データの自動取得・解析ワークフローが研究の中心です。
abstractOur successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.
Reproduction assets foundThe paper's authors explicitly state that all code implementing their ML/DL genotype-classification models is publicly available on GitHub at the authors' repository URL. PlantCV is a generic third-party library, not a paper-specific asset, and no separate phenotype dataset deposit is described beyond this repository.Code · publice. In our study, if a plant of another genotype misclassified as “wt” genotype. True negatives are data points that are truly negative despite being classified as negative. False negatives are data items with negative labels but positive values.
3
Code availability
All codes implementing the models are available through GitHub (https://github.com/NazmusSakeef/Plant-Genomics-Using-Machine-Learning/tree/main/dataset).
4
Results and discussionOpen asset ↗NazmusSakeef/Plant-Genomics-Using-Machine-Learninglines:107-116Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotStem / branchGrowth / time-series analysisGrowth / development / phenology
Traditional phenological models use chilling and thermal forcing (temperature sum or degree-days) to predict budbreak. Because of the heightening impact of climate and other related biotic or abiotic stressors, a model with greater biological support is needed to better predict budbreak. Here, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers. As a general principle, we assume that phenology is driven by the carbon status of the plant, which is closely related to environmental variables and the annual cycle of dormancy-activity. The carbon balance of a branch was modelled from autumn to winter with cold acclimation and dormancy and from winter to spring when deacclimation and growth resumption occur. After being calibrated in a field experiment, the model was validated across a large area (> 34 000 km 2 ), covering multiple conifers stands in Québec (Canada) and across heated plots for the SPRUCE experiment in Minnesota (USA). The model accurately predicted the observed dates of budbreak in both Québec (±3.98 d) and Minnesota (±7.98 d). The site-independent calibration provides interesting insights on the physiological mechanisms underlying the dynamics of dormancy break and the resumption of vegetative growth in spring.
Why it matches plant phenotyping methods針葉樹の芽吹き時期という植物状態を予測する機 mechanistic phenology modelを開発し、野外実験と複数地域・加温プロットで較正および検証しており、方法が研究の中心である。
abstractHere, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers.
Reproduction assets foundThe paper's Data availability section points to the public SPRUCE experiment dataset (used for model validation of black spruce budbreak under warming treatments) hosted on the mnspruce.ornl.gov site, which is an allowed URL. The Canadian sugar/phenology dataset (doi: 10.5683/SP3/MVRZMQ) is also public but its URL is不在Dataset · publico Cartenı̀ https://orcid.org/0000-0001-8985-1132
Annie Deslauriers https://orcid.org/0000-0002-4994-5772
Stefano Mazzoleni https://orcid.org/0000-0002-1132-2625
Data availability
Sugar and phenological datasets for the Canadian experiments
are available at doi: 10.5683/SP3/MVRZMQ. Data for the
SPRUCE experiment can be found at: https://mnspruce.ornl.gov/datasets/public.References
Adams HD, Germino MJ, Breshears DD, Barron-Gafford GA, Guardiola-
Claramonte M, Zou CB, Huxman TE. 2013. Nonstructural leaf
carbohydrate dynamics of Pinus edulis during drought-induced tree mortality
reveal role for carbon metabolism in mortality mechanism. New Phytologist
197: 1142–1151.
Ainsworth EA, Bush DR. 2011Open asset ↗mnspruce.ornl.govpdf-raw-page:11 lines:1-85Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
High-throughput plant phenotyping-the use of imaging and remote sensing to record plant growth dynamics-is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.
Why it matches plant phenotyping methods植物画像から重複個体を自動分割し、草丈を推定する自己教師ありCNNパイプラインを開発しており、フェノタイピング手法が研究の中心である。
abstractwe propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' R pipeline code and sample field image data (6 training photos, 52 test photos) on GitHub at the allowed URL.Code · publicThe R code of the proposed pipeline, sample image data (including 6 field photos for training and 52 example field photos taken by one of our cameras for testing), and description are available on Github at https://github.com/xingcheg/Plant-Traits-Extraction-2023 .Open asset ↗xingcheg/Plant-Traits-Extraction-2023lines:84-106Code / 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 · Crossref · checked 14 Sept 2026
Background The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable. Results An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed. Conclusion The technical realization of "Phenomenon" allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Why it matches plant phenotyping methods植物組織培養の形質を自動・非破壊・連続測定するマルチセンサーフェノタイピングシステムを開発し、画像分割や深度計測を検証しており、方法が研究の中心である。
abstractAn automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and 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。Code · publicThe dataset supporting the conclusions of this article (Hard- and Software of “Phenomenon” phenotyping system) are available in an open-access Github repository, https://github.com/halube/Phenomenon .Open asset ↗halube/Phenomenonlines:224-282Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognised as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana , since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode-plant interactions. Through imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst- and root-knot nematode tolerance limits in A. thaliana through classical modelling of tolerance limits. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable further studies into a mechanistic understanding of tolerance to below-ground biotic stress. Highlight The mechanisms of tolerance to root-parasitic nematodes remain unknown. We developed a high-throughput phenotyping system that enables unravelling the underlying mechanisms of tolerance to nematodes.
Why it matches plant phenotyping methods線虫耐性を評価するためのキャノピー画像計測と高スループット表現型解析プラットフォームの開発が研究の中心である。
abstractThrough imaging of tolerance-related parameters the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection.
Reproduction assets foundThe authors state that custom R scripts and functions used to analyse the high-throughput green canopy area growth data are publicly available via their GitLab repository at git.wur.nl/published_papers/willig_2023_camera-setup, and the data availability statement points to the same repository. The protocols.io link is Code · publiclimits (T) and relative minimum yield (m) were estimated for all
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measurements.
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Plant growth analysis using the high-throughput phenotyping platform
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To analyse the growth data of the plants obtained from the high-throughput platform, custom scripts and
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functions were written in “R” (available via gitlab:
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https://git.wur.nl/published_papers/willig_2023_camera-setup). For analysis we used the median daily
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leaf area (cm2), which was calculated by taking the median leaf area of the daily measurements (15 per
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day). The data was log2-transformed before analysis for normalization. The rate of growth was
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determined per day per plant by
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𝑅𝑥,𝑡 = log2(𝐴𝑥,𝑡−1 − 𝐴𝑥,𝑡Open asset ↗git.wur.nl/published_papers/willig_2023_camera-setuppdf-raw-page:11 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Recent studies indicate that food demand will increase by 35-56% over the period 2010-2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition "Autonomous Greenhouse Challenge". This paper describes and analyzes the results of the third edition of this competition. The competition's goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO 2 were decisive in realizing the competition's goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R 2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work.
Why it matches plant phenotyping methods深度カメラ画像とコンピュータビジョンによりレタスの草丈・被覆率・収量関連形質を推定し、精度評価と自動意思決定指標への応用を行っており、植物表現型取得法が中心である。
abstractimages taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest.
Reproduction assets foundThe paper's complete challenge dataset (climate time-series and annotated lettuce crop images used for the computer vision phenotyping) is published open access on 4TU.ResearchData, cited both in the Data Availability Statement and in reference 56.Dataset · public3rd Autonomous Greenhouse Challenge-Real Challenge Data Climate and Images Dataset: 4TU.ResearchData 2023 Available online: https://data.4tu.nl/articles/dataset/3rd_Autonomous_Greenhouse_Challenge_Online_Challenge_Lettuce_Images/15023088Open asset ↗4TU.ResearchData · 15023088lines:853-968Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variability.
Why it matches plant phenotyping methodsリンゴ器官の成長形質を階層的・反復的に収録した再利用可能なデータセットであり、成長率やアロメトリーの推定・モデル較正に用いるデータ資源として方法論的価値がある。
titleA hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources.
Reproduction assets foundThe paper is a Data in Brief article describing its own apple tree growth phenotype dataset (shoot, fruit, trunk measurements) deposited publicly on Mendeley Data with DOI 10.17632/852r5dnzd5.1 and a direct URL. This is a paper-specific, public, actionable phenotype dataset.Dataset · publiccommercial orchard
City: Caldaro, Bolzano/Bozen province, Trentino Alto Adige region
Country: Italy
Latitude and longitude collected samples/data: 46° 21’ N, 11° 16’ E, Altitude 240 m
Period: May-November 2014
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/852r5dnzd5.1
Direct URL to data: https://data.mendeley.com/datasets/852r5dnzd5/1
Related research article
F. Reyes, T. DeJong, P. Franceschi, M. Tagliavini, D. Gianelle, Maximum growth potential and periods of resource limitation in apple tree, Frontiers in Plant Science 7 (2016). doi: 10.3389/fpls.2016.00233
Value of the Data
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The dataset allows analysis of the impact of fruit load, on vegetative aOpen asset ↗Mendeley Data · 10.17632/852r5dnzd5.1lines:1-61Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
LiDAR / point cloudFruitMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyFruit / seed / panicle traits
How fruit size and shape are determined is of research interest in agriculture and developmental biology. Fruit typically exhibits three-dimensional structures with genotype-dependent geometric features. Although minor developmental variations have been recognized, little research has fully visualized and measured these variations throughout fruit growth. In this study, a high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon ( Diospyros kaki ) cultivars with various complex shapes. We obtained 2,380 3D fruit models that fully represented fruit appearance, and enabled precise and automated measurements of unique geometric features throughout fruit development. The 3D fruit model analysis identified key stages that determined the shape attributes at maturity. Typically, genetic diversity in vertical groove development was found, and such grooves can be filled by tissue expansion in the carpal fusion zone during fruit development. Furthermore, transcriptome analysis of fruit tissues from groove/non-groove tissues revealed gene co-expression networks that were highly associated with groove depth variation. The presence of YABBY homologs was most closely associated with groove depth and indicated the possibility that this pathway is a key molecular contributor to vertical groove depth variation. These results demonstrate the validity of fruit 3D growth analysis, which is a powerful tool for identifying the developmental mechanisms of fruit shape variation and the molecular basis of this diversity.
Why it matches plant phenotyping methods高解像度3Dスキャナと自動3Dモデル解析により、果実形状を発達期間 boyunca定量化する方法が研究の中心であり、果実形状の表現型解析手法として妥当です。
abstracta high-resolution 3D scanner was used to investigate the fruit development of 51 persimmon ( Diospyros kaki ) cultivars with various complex shapes
Reproduction assets foundThe paper's 3D fruit models (phenotyping inputs/outputs) are publicly deposited on figshare, and the authors' shape-measurement analysis code is on GitHub. Both are paper-specific, public, and actionable. The trimesh and visNetwork URLs are generic third-party libraries, not paper-specific assets.Code · public109 and measure shape features, and the code is available at https://github.com/pomology-ku/persimmon-fruit-3D-Open asset ↗github · pomology-ku/persimmon-fruit-3D-pdf-page:6 lines:1-59Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Viruses are key players in marine ecosystems where they infect abundant marine microbes. RNA viruses are emerging as key members of the marine virosphere. They have recently been identified as a potential source of mortality in diatoms, a group of microalgae that accounts for roughly 40% of the primary production in the ocean. Despite their likely importance, their impacts on host populations and ecosystems remain difficult to assess. In this study, we introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements to expand our understanding of viral infection. We followed different stages of infection of the bloom-forming diatom Guinardia delicatula by the RNA virus GdelRNAV-04 until the complete lysis of the host. From 20h after infection, we observed quantifiable changes in subcellular host morphology and biomass. Our microscopy monitoring also showed that viral infection of G. delicatula induced the formation of auxospores as a probable defense strategy against viruses. Our method enables the detection of discriminative morphological features on the subcellular scale and at high throughput for comparing populations, making it a promising approach for the quantification of viral infections in the field in the future.
Why it matches plant phenotyping methods自動3D共焦点顕微鏡と定量画像解析を組み合わせ、感染に伴う珪藻の細胞形態・バイオマスを高スループットに定量する手法が研究の中心である。
abstractwe introduce an innovative approach that combines automated 3D confocal microscopy with quantitative image analysis and physiological measurements
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe full annotated script can be found on https://github.com/mariescopy/Guinardia_ViralInfectionOpen asset ↗mariescopy/Guinardia_ViralInfectionlines:286-318Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeTissueSegmentationGrowth / time-series analysisTrackingGrowth / development / phenology
The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a novel ensemble segmentation model that integrates three different but promising networks, namely, SEResNet, InceptionV3, and VGG19, in the encoder part of its base model, which is the UNet model. EmergeNet can correctly detect the coleoptile at its first emergence when it is tiny and therefore barely visible on the soil surface. The performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED). It contains top-view time-lapse images of maize coleoptiles starting before the occurrence of their emergence and continuing until they are about one inch tall. EmergeNet detects the emergence timing with 100% accuracy compared with human-annotated ground-truth. Furthermore, it significantly outperforms UNet by generating very high-quality segmented masks of the coleoptiles in both natural light and dark environmental conditions.
Why it matches plant phenotyping methodsコレオプタイルの出芽時期と成長を画像から抽出する深層学習セグメンテーション手法を開発し、ベンチマークデータセットで性能検証しているため、植物フェノタイピング手法が中心である。
abstractThe performance of EmergeNet is evaluated using a benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED).
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 dataset can be freely downloaded from https://plantvision.unl.edu/dataset .Open asset ↗lines:324-339Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeaf2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.
Why it matches plant phenotyping methods静的データから器官の発生時系列と成長軌跡を再構成するモデルベース手法が研究の中心であり、葉の形態形成・成長という植物表現型を推定している。
abstractWe introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data.
Reproduction assets foundThe paper's data availability statement explicitly provides three paper-specific public assets: a new version of the MorphoLeaf phenotyping application, COPASI and R scripts for temporal calibration parameter estimation, and the leaf datasets used in the analysis, each with a public URL.Dataset · publicLeaf datasets are available at https://doi.org/10.15454/BMELNY .Open asset ↗10.15454/BMELNYlines:210-440Code · publicCOPASI and R scripts used to estimate temporal calibration parameters are available at https://doi.org/10.15454/DPFU1T .Open asset ↗10.15454/DPFU1Tlines:210-440Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
Why it matches plant phenotyping methods画像解析パイプラインを開発し、植物形態から病害進展を定量化する方法が研究の中心であるため含める。
abstractExpanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
Reproduction assets foundThe paper's data availability statement points to a public Purdue-hosted repository containing the raw plant images and genotype data used for the image-based disease phenotyping and QTL analysis. The analysis code, however, is only available upon request from an author, so it is not a public asset.Dataset · publicrd (1755401) to BPD, and the endowment of the Charles
William Harrison Distinguished Professorship at Purdue University
to EJD.
CONFLICT OF INTEREST
Authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
Raw images of RILs and parents for each replicate and
each time point as well as genotype data are available at
https://skynet.ecn.purdue.edu/~sbairedd/downloads/Rs_ril_data/. Code is available from Dr. Edward Delp.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. Design of our low-cost phenotyping platform including
automatic turntable, backdrop, lightning, and RGB camera.
Figure S2. Raw RGB pictures showOpen asset ↗skynet.ecn.purdue.edupdf-raw-page:15 lines:1-93Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
1Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes (from zero to high nitrate concentration) across four time points (days 5, 9, 13, and 17 after sowing). Significant nitrate condition response and genotype interactions were identified for many root traits with greatest impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions that could be useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.
Why it matches plant phenotyping methods根系画像・解析パイプラインを用いた4D根系構造の取得と多数の根形質の抽出が研究の中心であり、植物フェノタイピング手法の実質的な応用に該当する。
abstractUsing a root imaging and analysis pipeline, 4D pennycress root system architecture was characterized under four nitrate regimes
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 · public151 codes including the packages needed are available https://doi.org/10.5281/zenodo.7536940. A total of 44Open asset ↗zenodo · 10.5281/zenodo.7536940pdf-page:5 lines:1-52Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract. Understanding mechanisms of tree mortality and geometric patterns of canopy gaps is relevant for robust estimates of carbon stocks and balance in tropical forests, and for assessing how they are responding to climate change. We combined monthly RGB images acquired from an unmanned aerial vehicle with field surveys to identify gaps in an 18-ha permanent plot in an old-growth Central Amazon forest over a period of 28 months. In addition to detecting, we measured the size and shape of gaps, and analyzed their temporal variation and correlation with rainfall. We further described associated modes of tree mortality or branch fall and quantified associated losses of biomass. Overall, the sensitivity of gap detection differed between field surveys and imagery data. In total, we detected 32 gaps either in the images and field, ranging in area from 9 m2 to 835 m2. Relatively small gaps (
Why it matches plant phenotyping methodsUAV画像とフィールド調査を用いて森林キャノピーギャップを検出・定量化し、検出感度を比較しており、植物群落の構造状態を測定する方法の適用・検証が中心的です。
abstractWe combined monthly RGB images acquired from an unmanned aerial vehicle with field surveys to identify gaps
Reproduction assets foundThe paper's R analysis code is publicly archived on Zenodo (10.5281/zenodo.8298693), and the supporting lidar data are openly available on Zenodo (10.5281/zenodo.7636454). Other data (UAV imagery, field gap measurements) are only available upon request from the co-authors.Dataset · publiccoverage, relatively short revisiting time and long data se- available at https://doi.org/10.5281/zenodo.7636454 (Ometto et al.,Open asset ↗Zenodo · 10.5281/zenodo.7636454pdf-page:12 lines:1-50Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
Why it matches plant phenotyping methodsUAV画像から作物のキャノピー高を抽出し、時系列モデルで生育・形質を予測する枠組み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開発・適用に該当する。
abstractHere we provide a novel framework for trait prediction using CH data in rice.
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 ) in 2019, 2020, and 2021 from the aspect of genetics and examined how to use the CH data for the prediction of traits usually measured by hand ( Figure 1 ).Open asset ↗lines:342-376Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.
Why it matches plant phenotyping methods花の検出と時系列表現型の抽出を行う深層学習手法を開発し、ベンチマークデータセットと評価も提示しており、植物フェノタイピング手法が中心である。
abstractwe introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis.
Reproduction assets foundThe paper publicly releases the FlowerPheno benchmark dataset (17,022 multiview RGB image sequences of sunflower, canna, and coleus with ground-truth flower bounding boxes) and the FlowerPhenoNet source code, both with explicit availability statements and URLs.Dataset · publicThe dataset can be freely downloaded
from https://plantvision.unl.edu/dataset, accessed on 15 February 2021.Open asset ↗plantvision.unl.edupdf-page:4 lines:1-41Code · publicThe source code is available at
https://github.com/localchocotaco/FlowerPhenoNet, accessed on 27 November 2022.Open asset ↗github.com/localchocotaco/FlowerPhenoNetpdf-page:18 lines:1-58Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Why it matches plant phenotyping methods自動化された高スループット画像表現型プラットフォームを開発し、77種類の画像形質を定量化・検証し、機械学習による草丈予測も評価しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages.
Reproduction assets foundThe paper explicitly states that all images and phenotypic data are available on figshare and that the HTP/RGB image and GWAS analysis pipeline code is available on the authors' GitHub repository (maizeHTP). Both are paper-specific, public, and actionable.Dataset · publicAll the images and phenotypic data are available at https://figshare.com/account/home#/projects/141743 .Open asset ↗figshare · projects/141743lines:168-200Code · publicThe code for HTP from LemnaTec and the code for the RGB image and GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizeHTP .Open asset ↗GitHub · GUOWEIJUN/maizeHTPlines:168-200Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Premise With modern advances in genetic sequencing technology, plant phenotyping has become a substantial bottleneck in crop improvement programs. Traditionally, researchers have manually measured phenotypic traits to help determine genotype-phenotype relationships, but manual measurements can be time consuming and expensive. Recently, automated phenotyping systems have increased the spatial and temporal density of measurements, but most of these systems are extremely expensive and require specialized expertise. In the present paper, we develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness. Methods During a greenhouse experiment on the effects of abiotic stress on Brassica rapa , we collected images of hundreds of plants every hour for over a month with a system that cost approximately US$1000. Results In comparison with manually acquired images, this HTP system was able to produce similar estimates of foliar area and greenness, developmental trends, and treatment effects. Foliar area was correlated between the two image sets, but greenness was not. Discussion These findings highlight the potential of HTP systems built from low-cost hardware and freely available software. Future work can use this system to investigate genotype-environment interactions and the genetic loci underlying morphological changes resulting from abiotic stress.
Why it matches plant phenotyping methods低コストの画像ベース高スループット表現型計測システムを開発・検証し、葉面積と緑色度を自動推定することが研究の中心であるため。
abstractwe develop and validate a low-cost, scalable, high-throughput phenotyping (HTP) system for automating the measurement of foliar area and greenness.
Reproduction assets foundThe paper's data availability statement deposits both the phenotyping data (images/measurements) and the analysis scripts on Zenodo with explicit public DOIs, making both paper-specific assets directly actionable.Dataset · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.5725224lines:188-243Code · publicAll of the data and analysis scripts have been deposited to Zenodo (data: https://doi.org/10.5281/zenodo.5725224 ; scripts: https://doi.org/10.5281/zenodo.6366716 ).Open asset ↗Zenodo · 10.5281/zenodo.6366716lines:188-243Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based imagery has become widely used in collecting agronomic traits, enabling a much greater volume of data to be generated in a time-series manner. As one of the cutting-edge imagery analysis tools, machine learning-based object detection provides automated techniques to analyze these imagery data. In our previous study, UAVs have been used to collect aerial photography for field trials of 233 diverse inbred lines, grown under different nitrogen treatments. Images were collected during different plant developmental stages throughout the growing season. This dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season. To improve detection accuracy, we have developed an image segmentation method to remove non-tassel pixels and then feed these filtered images into machine learning algorithms. As a result, our method showed a significant improvement in the accuracy of maize tassel detection. This method can be used in future research to produce time-series counts of tassels at the plot level, and will allow for accurate estimates of flowering-related traits, such as the earliest detected flowering date and the duration of each plot's flowering period. This phenotypic data and the trait-associated genes provide new opportunities for crop improvement and to facilitate future plant breeding.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの雄穂数と開花関連形質を自動推定する画像分割・機械学習手法の開発が中心であり、植物表現型測定法に該当する。
abstractThis dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season.
Reproduction assets foundThe paper's UAV imagery, plot-level images, and metadata are publicly deposited in CyVerse under DOI 10.25739/4t1v-ab64, per the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe original UAV images, the clipped plot-level images, and the associated metadata used in this study
have been deposited in CyVerse (10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-raw-page:5 lines:1-39Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
High temperatures inhibit plant growth. A proposed strategy for improving plant productivity under elevated temperatures is the use of plant growth-promoting rhizobacteria (PGPR). While the effects of PGPR on plant shoots have been extensively explored, roots—particularly their spatial and temporal dynamics—have been hard to study, due to their below-ground nature. Here, we characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II. The platform uses custom-made agar plates, which allow air exchange to occur with the agar medium and enable the shoot to grow outside the compartment. The platform provides light protection to the roots, the exposure of it to the shoots, and the non-invasive phenotyping of both organs. Arabidopsis thaliana, co-cultivated with Paraburkholderia phytofirmans PsJN at elevated and ambient temperatures, showed increased lengths, growth rates, and numbers of roots. However, the magnitude and direction of the growth promotion varied depending on root type, timing, and temperature. The root length and distribution per depth and according to time was also influenced by bacterization and the temperature. The shoot biomass increased at the later stages under ambient temperature in the bacterized plants. The study offers insights into the timing of the tissue-specific, PsJN-induced morphological changes and should facilitate future molecular and biochemical studies on plant–microbe–environment interactions.
Why it matches plant phenotyping methodsGrowScreen-Agar IIという非侵襲的な高解像度フェノタイピング・イメージングプラットフォームを用い、根とシュートの形態を時空間的に測定することが研究の中心的手法です。
abstractwe characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform—GrowScreen-Agar II.
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/plants11212927/s1 , Figure S1: WinRhizo analyzed root lengths and root and shoot biomass; Figure S2: Root sampling and bacterial colonization confirmation; Figure S3: Sample root images generated by the GrowScreen-Agar II; Figure S4: Agar plates for GrowScreen-Agar II; Figure S5: Magazines for GrowScreen-Agar II; Figure S6: Imaging station of GrowScreen-Agar II; Table S1: Mean values and standard error of different root type morphological traits; Table S2: Mean values and standard error of different root system traits describing distribution and spread.Open asset ↗lines:106-120Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
CassavaRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.
Why it matches plant phenotyping methods根系画像から根径分布などの表現型形質を自動抽出する新手法を開発し、仮想モデルで検証しており、表現型取得・解析が研究の中心である。
abstractWe introduce a newly developed method that is capable to analyze the distribution of root diameters automatically
Reproduction assets foundThe paper's root diameter analysis software is publicly available on the authors' GitLab (grow-screen-field). The phenotype/image data are deposited on Zenodo (doi: 10.5281/zenodo.5883368), but no Zenodo URL is in the allowed list, so only the code asset is reported. Paraview and Detectron2 are generic third-party toolCode · publicthe Helmholtz. We thank Alexander Putz for his technical support and N. Punyasu for allowing us to use her parametrization of the OpenSimRoot cassava model.
Data Availability
The data presented in this study are openly available in Zenodo.org (doi: 10.5281/zenodo.5883368 ) [ 39 ]. The software has been published in Gitlab under https://gitlab-public.fz-juelich.de/grow-screen-field . The parameters of the OSR-models are available from the authors upon request.
Authors’ Contributions
J.W. did the software implementation and compiled all algorithms and methods into a software with graphical user interface. He also helped developing the methodology. T.W. provided the data for the real root case Open asset ↗gitlab-public.fz-juelich.de/grow-screen-fieldlines:110-132Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Capturing cell-to-cell signals in a three-dimensional (3D) environment is key to studying cellular functions. A major challenge in the current culturing methods is the lack of accurately capturing multicellular 3D environments. In this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis and soybean cells. To analyze the generated large image datasets, we developed a high-throughput image analysis pipeline. Furthermore, we showed the cell cycle reentry of bioprinted cells for which the timing coincides with the induction of core cell cycle genes and regeneration-related genes, ultimately leading to microcallus formation. Last, the identity of bioprinted Arabidopsis root cells expressing endodermal markers was maintained for longer periods. The framework established here paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle reentry toward tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング枠組みと、大規模画像から生存性・細胞分裂・細胞同一性を解析する高スループット画像解析パイプラインを開発しており、表現型取得・解析が研究の中心的な技術的貢献である。
abstractIn this study, we established a framework for 3D bioprinting plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their high-throughput confocal z-stack cell quantification pipeline (Python scripts wrapped in an R Shiny GUI), used to analyze the paper's bioprinted plant cell imaging datasets, on GitHub with a Zenodo deposit (10.5281/zenodo.7012765). The Zenodo record 5537065 in allowed_urls isCode · publicScripts for our high-throughput and automatic image analysis are available at https://github.com/LisaVdB/Confocal-z-stack-cell-detection and 10.5281/zenodo.7012765 .Open asset ↗LisaVdB/Confocal-z-stack-cell-detection · 10.5281/zenodo.7012765lines:247-261Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in many plant species including tree crops leading to aberrant growth, flowering, and fruiting. Currently, research in this field is impeded by the lack of affordable non-invasive methods for on-line monitoring of dormancy. We report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants. The proposed method is based on continuous near-field sensing of the photosynthetic activity of shoots via pulse-amplitude modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli are subjected to frequency-domain analysis. The proposed approach allows to overcome the variance coming from diurnal changes of insolation and to derive estimations on the depth of dormancy. Our approach was validated over three seasons in an experimental apple (Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the tress at different phases of dormancy) and the output of commonly used chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of freeze damages along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠深度を非侵襲的に推定する蛍光センサーと時系列解析のフレームワークを開発し、従来法およびモデルと照合検証しており、表現型取得法が研究の中心である。
abstractWe report on an automatic framework for low-cost, long-term, and scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub. This is a paper-specific computational analysis asset for the chlorophyll fluorescence/JIP-test time-frequency analysis. The full raw data and derived parameters are only available on request, so they are notCode · publicic projects in priority areas of scientific and technological
development (grant number 075-15-2020-774).
Data Availability Statement: The raw data and derived parameters are available from the corre-
sponding author on reasonable request.
Code used in the analysis, accompanied with a subset of the data, is available on GitHub
(https://github.com/Lodinn/PAM-timeseries).
Acknowledgments: The indoors chlorophyll fluorescence measurements were carried out at the
Phototrophic Organisms Phenotyping user facilities of Lomonosov Moscow State University. TheOpen asset ↗Lodinn/PAM-timeseriespdf-layout-page:15 lines:1-58Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.
Why it matches plant phenotyping methodsX線CTの時系列3D画像から根系形態形質を自動抽出するソフトウェア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。
abstractWe have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans.
Reproduction assets foundThe paper's authors explicitly state that the 4DRoot source code (the software performing the root phenotyping analysis) is freely available on GitHub. The X-ray CT scan data themselves are not deposited in a public repository; only the code is. TreeQSM is a cited prior-work dependency, not a paper-specific asset.Code · publicThe open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot .Open asset ↗TIDOP-USAL/4DRootlines:225-297Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat.
Why it matches plant phenotyping methodsコムギの柱頭・子房形態を定量化する高スループット画像・機械学習手法の開発と適用が研究の中心であり、植物表現型取得法として明確に該当する。
abstractwe created a high-throughput phenotyping approach to quantify stigma and ovary morphology
Reproduction assets foundThe paper's authors publicly deposited both the analysis code (CNN training/implementation scripts and R scripts) on GitHub and the carpel image/training/validation datasets on Earlham OpenData, directly reproducing this paper's wheat carpel phenotyping measurements and analysis.Code · publicTraining codes used for the development of the CNNs, adapted stigma and ovary CNNs, and R scripts used for data curation and visualisation can be found at https://github.com/Uauy-Lab/ML-carpel_traitsOpen asset ↗Uauy-Lab/ML-carpel_traitslines:122-188Dataset · publicDatasets for the training and validation of the models and raw images used for the different experimental analyses are freely available at https://opendata.earlham.ac.uk/wheat/under_license/toronto/Millan-Blanquez_etal_2022_machine-learning-carpel-traits/Open asset ↗lines:122-188Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RGB / grayscaleLeafSegmentationGrowth / time-series analysisGrowth / development / phenology
Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.
Why it matches plant phenotyping methods植物画像から成長状態を予測・生成する深層学習フレームワークと、時系列データ拡張手法を開発しており、画像およびインスタンスマスクによる表現型取得・解析が研究の中心である。
abstractTo address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms.
Reproduction assets foundThe paper's plant growth prediction experiments use the public KOMATSUNA plant phenotyping dataset (Uchiyama et al., 2017), which the authors explicitly state is publicly available via the linked IEEE document. No author analysis code, trained models, or paper-specific supplementary data assets are described with a de-Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieeexplore.ieee.org/document/8265449 .Open asset ↗8265449lines:805-840Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
Background information to give context to the studyCanopy gaps are the most evident manifestation of how disturbances disrupt forest landscapes. The size distribution and return frequency of gaps, and subsequent recovery processes, determine whether the old-growth state can be reached. The aim or research questionWe used remote sensing metrics to compare the disturbance regime of four Amazon regions based on the size distribution of gaps, their dynamics and geometric characteristics. A brief summary of the methodology usedWe assessed gap dynamics at four sites in the central, central eastern, southeastern, and northeastern regions of the Brazilian Amazon using repeated airborne laser scanning surveys. We developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering. For that, we overlapped layers of gap locations from two consecutive airborne laser scanning surveys. Key results with some significance measuresThe gap fraction in our study sites varied between 1.26% to 7.84%. All the sites have similar proportion of gaps among size classes. What notably changed between sites was not the gap size-distribution, but the relative importance of stages of gap dynamics. Growing and persisting rates were greatest in the site with the stronger seasonal variation in climate, lower annual precipitation, higher mean wind speed and higher solar radiation. The conclusions, which address the main aimsThe concept of stability reflects the tendency of a system to quickly return to a position of equilibrium when disturbed. We showed that gap dynamics varied among sites, with one example of low recovery rate contrasted to three other sites with faster recovery. Our results support that such as assessing the size distribution of gaps, investigating their return frequency and severity is crucial for understanding forest dynamics at the landscape and regional scales.
Why it matches plant phenotyping methods航空レーザースキャンの反復データを用いて森林キャノピーギャップの動態を定量化する新規解析法を開発しており、植物キャノピー状態の抽出が研究の中心である。
abstractWe developed a novel analysis to quantify four possible stages of gap dynamics: formation, expansion, persisting and recovering.
Reproduction assets foundThe paper's core phenotyping input — repeated airborne laser scanning data for the four Amazon study sites (Ducke, Tapajos, Tanguro, Jari) from the Sustainable Landscapes Brazil project — is explicitly stated to be freely available at the EMBRAPA Paisagens Lidar webgis portal. No author analysis code, scripts, or gap-dDataset · publicThe Sustainable Landscape Brazil Project has repeatedly surveyed Amazonian sites
with airborne laser scanning. This data set gave us a unique opportunity to assess gap
dynamics across Amazonia (data freely available at:
https://www.paisagenslidar.cnptia.embrapa.br/webgis/).Open asset ↗pdf-page:4 lines:1-46Code / 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
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions' respective origins.
Why it matches plant phenotyping methodsロボティクスによる根系画像取得の自動化と画像解析パイプライン開発が中心で、根系構造・成長動態という植物形質を抽出するフェノタイピング基盤を提示している。
abstractwe present the automation of GLO-Roots using robotics and the development of image analysis pipelines
Reproduction assets foundThe paper deposits its root phenotyping imaging data, image analysis pipelines/scripts, RShiny exploration apps, and rhizotron build files on Zenodo, plus robotics software on GitHub — all paper-specific, public, and actionable.Dataset · publicThe raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009 .Open asset ↗Zenodo · 10.5281/zenodo.5709009lines:160-163Code · publicImage analysis pipelines and scripts are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5708430 .Open asset ↗Zenodo · 10.5281/zenodo.5708430lines:224-389Code · publicGeneral code for software operating robotics available: GitHub: https://github.com/rhizolab/rhizo-server .Open asset ↗GitHub · rhizolab/rhizo-serverlines:224-389Code / 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 confirmedEurope PMC · checked 15 Sept 2026
Submergence during germination impedes aerobic metabolisms and limits the growth of most higher plants. However, some wetland plants including rice can germinate under submerged conditions. It has long been hypothesized that the first elongating shoot tissue, the coleoptile, acts as a snorkel to acquire atmospheric oxygen (O 2 ) to initiate the first leaf elongation and seminal root emergence. Here, we obtained direct evidence for this hypothesis by visualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system. In parallel with the O 2 imaging, we tracked the anatomical development of shoot and root tissues in real-time using an automated flatbed scanner. Three hours after the coleoptile tip reached the water surface, O 2 levels around the embryo transiently increased. At this time, the activity of alcohol dehydrogenase (ADH), an enzyme critical for anaerobic metabolism, was significantly reduced, and the coleorhiza covering the seminal roots in the embryo was broken. Approximately 10 h after the transient burst in O 2 , seminal roots emerged. A transient O 2 burst around the embryo was shown to be essential for seminal root emergence during submerged rice germination. The parallel application of a planar O 2 optode system and automated scanning system can be a powerful tool for examining how environmental conditions affect germination in rice and other plants.
Why it matches plant phenotyping methods水中イネ発芽時の酸素動態と器官発達を、平面O2オプトードおよび自動スキャナーで時空間的に可視化・追跡する手法が研究の中心であり、植物の生理状態・形態発達を測定する実質的なフェノタイピング手法である。
abstractvisualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system.
Reproduction assets foundThe paper's phenotyping measurements (time-lapse growth images and planar optode O2 imaging of submerged rice germination) are publicly available as Supplementary Videos 1–3 hosted at the Frontiers supplementary material URL. No author analysis code or trained models are deposited; ImageJ and UWSC are generic third‑dayDataset · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.946776/full#supplementary-material
Supplementary Video 1
Time-lapse images showing the germination process of submerged rice with normoxic or anoxic atmospheres.
Click here for additional data file.
Supplementary Video 2
Time-lapse images of the growth process following a shift from an anoxic to a normoxic atmosphOpen asset ↗lines:107-237Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
Why it matches plant phenotyping methodsUAV画像からイネの形態・生育形質を自動抽出するオープンソース基盤の開発と信頼性検証が中心であり、遺伝解析への応用も含むため採録。
abstractwe present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials.
Reproduction assets foundThe paper's Data Availability statement explicitly provides the AirMeasurer source code, GUI software, and testing 2D/3D aerial images (orthomosaics and point clouds) at a public GitHub release URL, directly supporting this paper's phenotyping analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/The‐Zhou‐Lab/UAV/releases/tag/V2.0.2 .Open asset ↗The‐Zhou‐Lab/UAV · V2.0.2lines:589-611Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determination R 2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.
Why it matches plant phenotyping methodsダイズの花・莢数という植物形質を画像から自動認識・計数する深層学習手法を比較、改良、検証しており、表現型取得法が研究の中心である。
abstractthe use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study
Reproduction assets foundThe authors publicly deposited the soybean flower and pod image datasets (the phenotyping inputs used for detection/counting) in an online repository via a Baidu Netdisk link with password, stated in the Data availability statement. No author analysis code or trained model checkpoints are explicitly shared; LabelImg isDataset · 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://pan.baidu.com/s/1j ZE6BHlpVjGay_JqVmOew:password: ate8 .Open asset ↗pan.baidu.com · ZE6BHlpVjGay_JqVmOewlines:751-822Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models.
Why it matches plant phenotyping methods塩ストレス下のイネに対するHSI画像処理パイプラインとPLSR予測モデルを開発・評価し、Na+・K+という植物生理状態の非破壊フェノタイピングへの適用性と予測性能を検証しているため、方法が中心的である。
abstractAnalysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis.
Reproduction assets foundThe authors deposited the paper's hyperspectral image dataset (rice plants under salinity stress, used for Na+/K+ prediction modeling) in the Dryad Digital Repository, with an explicit availability statement and public DOI.Dataset · publicData Availability: The hyperspectral image dataset used in this study is available through the DRYAD Digital Repository: https://doi.org/10.5061/dryad.2jm63xsrm .Open asset ↗Dryad Digital Repository · 10.5061/dryad.2jm63xsrmlines:140-151Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background and aims Seed germination is strongly influenced by environmental temperatures. With global temperatures predicted to rise, the timing of germination for thousands of plant species could change, leading to potential decreases in fitness and ecosystem-wide impacts. The thermogradient plate (TGP) is a powerful but underutilized research tool that tests germination under a broad range of constant and alternating temperatures, giving researchers the ability to predict germination characteristics using current and future climates. Previously, limitations surrounding experimental design and data analysis methods have discouraged its use in seed biology research. Methods Here, we have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature. We illustrate this analysis framework using three example species: Wollemia nobilis, Callitris baileyi and Alectryon subdentatus. The script generates >40 germination indices including germination rates and final germination across each cell of the TGP. These indices are then used to populate generalized additive models and predict germination under current and future monthly maximum and minimum temperatures anywhere on the globe. Key results In our study species, modelled data were highly correlated with observed data, allowing confident predictions of monthly germination patterns for current and future climates. Wollemia nobilis germinated across a broad range of temperatures and was relatively unaffected by predicted future temperatures. In contrast, C. baileyi and A. subdentatus showed strong seasonal temperature responses, and the timing for peak germination was predicted to shift seasonally under future temperatures. Conclusions Our experimental workflow is a leap forward in the analysis of TGP experiments, increasing its many potential benefits, thereby improving research predictions and providing substantial information to inform management and conservation of plant species globally.
Why it matches plant phenotyping methods熱勾配プレート実験の種子発芽表現型を解析・予測するRスクリプトとワークフローが中心で、観測値との検証も行っているため。
abstractwe have developed a freely available R script that uses TGP data to analyse seed germination responses to temperature.
Reproduction assets foundThe paper's TGP germination data and R analysis workflow are openly deposited on Zenodo (record 5457222), with explicit availability statements in the Methods and Data Availability sections.Dataset · publicThe data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.5457222 .Open asset ↗Zenodo · 10.5281/zenodo.5457222lines:223-251Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge ( Euphorbia virgata ), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites. Worldview‐2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short‐term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview‐2 model. Across models, most false‐positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid‐season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red‐edge and near‐infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large‐scale, remote and data‐sparse areas.
Why it matches plant phenotyping methods衛星画像とCNN/LSTMを用いて侵入植物個体群を直接検出する方法を開発・評価しており、植物状態の取得・抽出が研究の中心である。
abstractWe developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites.
Reproduction assets foundThe article explicitly states that all analysis code for the leafy spurge deep learning models (WV-CNN, PS-CNN, PS-LSTM) is publicly available in the authors' GitHub repository. The 1-m land cover map used as ground truth is a cited prior dataset (Host et al., 2016), not a paper-specific asset, and no trained model or Code · publice trained each model
for 100 epochs, where each epoch comprised 200 samples
per batch with a batch size of eight. We applied random
image augmentations, including 0/90/180/270-degree rota-
tions, which are shown to assist in model generalization
(Cabezas et al., 2020; Shorten & Khoshgoftaar, 2019). All
code is available online (https://github.com/lake-thomas/spurge-remote-sensing).Model performance metrics
We assessed model performance for each class based on
the number of true positives (TP), false positives (FP),
true negatives (TN) and false negatives (FN). We calcu-
lated overall accuracy as the proportion of correctly iden-
tified pixels (TP + TN/TP + TN + FP + FN) to identify
the probaOpen asset ↗https://github.com/lake-thomas/spurge-remote-sensingpdf-raw-page:6 lines:1-93Code / 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 15 Sept 2026
Capturing cell-to-cell and cell-to-environment signals in a defined 3 dimensional (3D) microenvironment is key to study cellular functions, including cellular reprogramming towards tissue regeneration. A major challenge in current culturing methods is that these methods cannot accurately capture this multicellular 3D microenvironment. In this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity. We established long-term cell viability for bioprinted Arabidopsis root cells and soybean meristematic cells. To analyze the large image datasets generated during these long-term viability studies, we developed an open source high-throughput image analysis pipeline. Furthermore, we showed the cell cycle re-entry of the isolated Arabidopsis and soybean cells leading to the formation of microcalli. Finally, we showed that the identity of isolated cells of Arabidopsis roots expressing endodermal markers maintained longer periods of time. The framework established in this study paves the way for a general use of 3D bioprinting for studying cellular reprogramming and cell cycle re-entry towards tissue regeneration.
Why it matches plant phenotyping methods植物細胞の3Dバイオプリンティング系と、長期画像データから細胞生存性・分裂・同一性を抽出するオープンソース解析パイプラインを開発しており、植物状態の取得・解析手法が中心である。
abstractIn this study, we established the framework of 3D bioprinting with plant cells to study cell viability, cell division, and cell identity.
Reproduction assets foundThe paper's authors publicly released their in-house confocal z-stack cell-counting image analysis pipeline (Python scripts wrapped in an R Shiny GUI) on GitHub, which directly reproduces the paper's computational analysis of bioprinted plant cell images. Generic dependencies (pyimageJ, OpenCV, ComDet, R shiny) and theCode · publicpipeline
contained in Python and further developed into an R Shiny application (44) can be easily accessed, along
with the usage instructions, from the Github repository at https://github.com/LisaVdB/Confocal-z-stack-
cell-detection.
Data availability
Scripts for our high-throughput and automatic image analysis are available at
https://github.com/LisaVdB/Confocal-z-stack-cell-detection.Open asset ↗LisaVdB/Confocal-z-stack-cell-detectionpdf-layout-page:10 lines:1-49Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 8 Sept 2026
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner. Significance StatementMany bottlenecks exist in high-throughput phenotyping involving computing power for processing and a lack of focus on abiotic stresses that has prevented an advancement in phenotyping on par with genotyping. Therefore, we create an automated HTP system that performs nutrient studies on Arabidopsis thaliana with cloud-based image processing that quantifies plant traits at a whole and leaf-level.
Why it matches plant phenotyping methodsOPEN leafは、葉単位の形態形質を画像から自動取得・定量するオープンソース高スループット表現型解析システムの設計・実装が中心であり、明確に収載対象です。
abstractHere we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.
Reproduction assets foundThe paper's image-analysis pipeline (SMART) used for rosette and leaf-specific phenotyping is explicitly released as public source code on GitHub and as a prepackaged Docker container. Phenotype data tables are only in supplementary material without a public URL, and other code repos (OPEN Controller, OPEN-leaf-cloud)'Code · public209 available as source code on GitHub (https://github.com/Computational-Open asset ↗pdf-page:8 lines:1-41Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research.
Why it matches plant phenotyping methodsPhenoCam画像から作物種と日次フェノロジーを抽出する深層学習・隠れマルコフモデルのワークフローを開発し、性能評価も行っているため、植物フェノタイピング手法が中心である。
abstractHere, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields.
Reproduction assets foundThe authors deposited all analysis code, data, and final model predictions in a public Zenodo repository (DOI 10.5281/zenodo.5618316), and the paper's phenotyping inputs are PhenoCam images from the public PhenoCam data portal (phenocam.sr.unh.edu). Both are paper-specific, public, and actionable.Code · publicData Availability Statement: All code and data to reproduce this analysis, as well as the final model
predictions, are available in a Zenodo repository [44].Open asset ↗Zenodopdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotFruitGrowth / time-series analysisGrowth / development / phenologyYield / yield components
Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/-2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51.
Why it matches plant phenotyping methodsカカオ果実の成熟・生育（フェノロジー）を気象変数と熱時間から推定する作物モデルを構築・パラメータ化し、観測値と比較検証しており、植物状態の推定手法が研究の中心である。
abstractwe parameterised a crop model to predict the best time for harvest cacao fruits in Colombia
Reproduction assets foundThe authors' cacao crop model (kocolatl), the modified SIMPLE model code used for the paper's phenology/yield simulations, is stated to be available in a public repository with an authors' GitHub URL. The Fedecacao field phenotype data are not independently deposited; the handle.net URL is a cited reference, not a dataCode · publicSoftware will be provided upon user request. The data used in this study can be found as follows: kocolatl is available in the research data repository. https://github.com/anyelacamargo/kocolatl.git accessed on 15 December 2021, UK.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
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References
1. Zuidema P.A., Leffelaar P.A., Gerritsma W., Mommer L., Anten N.P. A physiologicaOpen asset ↗github.com/anyelacamargo/kocolatl.git · kocolatllines:278-301Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.
Why it matches plant phenotyping methods近接カメラ画像から作物種とフェノロジーを日次推定する深層学習・HMMワークフローを開発しており、植物の状態・生育段階の抽出が研究の中心である。
abstractHere we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields.
Reproduction assets foundThe authors state that all analysis code, data, and final model predictions are publicly available in two Zenodo repositories (DOIs 10.5281/zenodo.5618316 and 10.5281/zenodo.5579797), directly reproducing this paper's PhenoCam crop phenology classification and HMM post-processing analysis.Code · public.4 [36], NumPy v.1.20.2 [37], and Pomegranate v0.14.5 [38] in
the Python programming language v3.7 [39]. In the R language v4.1 [40] we used the
zoo v1.8.0 [41], tidyverse v1.3.1 [42], and ggplot2 v3.3.5 [43] packages. All code for the
analysis, as well as the final model predictions, are available in a Zenodo repository ([44],
https://doi.org/10.5281/zenodo.5618316).3. Results
The overall F1 score, a summary statistic which incorporates recall and precision,
was 0.90-0.92 for the training data across the three categories of Dominant Cover, Crop
Type, and Crop Status (Figure 2). The overall F1 score for validation data, which was not
used in the model fitting, was 0.83-0.85 for the three cOpen asset ↗Zenodo · 10.5281/zenodo.5618316pdf-raw-page:7 lines:1-64Code · publicll authors have read and agreed to the published version of the
manuscript.
Funding: D.B. was supported by the United States Department of Agriculture, CRIS:3050-11210-009-
00D
Data Availability Statement: All code and data to reproduce this analysis, as well as the final model
predictions, are available in a Zenodo repository (https://doi.org/10.5281/zenodo.5579797).USC 105 and is also made available for use under a CC0 license.
(which was not certified by peer review) is the author/funder. This article is a US Government work. It is not subject to copyright under 17
The copyright holder for this preprint
this version posted January 5, 2022.
;
https://doi.org/10.1101/2021.10.20.465168
doiOpen asset ↗Zenodo · 10.5281/zenodo.5579797pdf-raw-page:16 lines:1-60Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize ( Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.
Why it matches plant phenotyping methods低コストの温室HTPプラットフォームを構築し、画像から植物高・群落被覆・群落体積・バイオマス指標を抽出して手動測定と検証しており、表現型取得法が研究の中心である。
abstractPlant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development.
Reproduction assets foundThe paper's Data Availability section states that the image data underlying this greenhouse maize phenotyping study are publicly deposited on Mendeley Data (https://doi.org/10.17632/frsfpgnsyz.1), which is an allowed URL. The genotype data deposit (10.17632/5gvznd2b3n.3) is also mentioned but is genomic/omics data and,Dataset · publicestimate genomic heritability
using the following formula:
ℎ2
𝑔 =
σ2
𝑔
σ2
𝑔+
σ2
𝑒
𝑛𝑟
The estimates of genomic correlation were obtained from
the estimated variance-covariance matrix in the bivariate
Bayesian GBLUP model.
2.9 Data availability
The genotype and image data are available at https://doi.org/10.17632/5gvznd2b3n.3 and https://doi.org/10.17632/frsfpgnsyz.1, respectively.
3 RESULTS
3.1 Image processing and data extraction
A total of 756 plots (plants) in each replication across time
were evaluated during plant development. Each collection of
images took approximately 10 min. The ground resolution of
the orthomosaics was approximately 2.30 mm pix–1, and the
GCP error was approximatOpen asset ↗pdf-raw-page:6 lines:1-131Code / 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 9 Sept 2026
Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC ( r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination ( R 2 ) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像によるイネの窒素状態・高NUE表現型の非破壊かつ高スループットな推定手法を開発・検証しており、表現型取得法が研究の中心である。
abstractfew easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD)
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2 ), two main aspects were considered: (1) Many varieties from the 3,000 rice genome project were germplasm collections and not used in field practices because of their lower-yielding, varied GD, and/or weak agricultural traits.Open asset ↗lines:380-387Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 9 Sept 2026
A plant's architecture contributes to its ability to acquire resources and reduce mechanical load. Arabidopsis thaliana is the most common model plant in molecular biology, and there are several mutants and transgenic lines with modified plant architecture regulation, such as lazy1 mutants, which have reversed angles of lateral branches. Although some phenotyping methods have been used in larger agricultural plants, limited suitable methods are available for three-dimensional reconstruction of Arabidopsis, which is smaller and has more uniform surface textures and structures. An inexpensive, easily adopted three-dimensional reconstruction system that can be used for Arabidopsis is needed so that researchers can view and quantify morphological changes over time. We developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method, which uses a fixed camera to capture plant images from multiple directions while the plant slowly rotates. We then developed a script to autogenerate stack images from the obtained input movie and visualized the plant architecture by rendering the output stack image using the general bioimage analysis software. We successfully three-dimensionally and time-sequentially scanned wild-type and lazy1 mutant A. thaliana plants and measured the angles of the lateral branches. This non-contact, non-destructive method requires no specialized equipment and is space efficient, inexpensive and easily adopted by Arabidopsis researchers. Consequently, this system will promote three- and four-dimensional phenotyping of this model plant, and it can be used in combination with molecular genetics to further elucidate the molecular mechanisms that regulate Arabidopsis architecture.
Why it matches plant phenotyping methodsシロイヌナズナの3次元画像再構成と枝角度測定を目的とする、植物表現型取得システムの開発が中心である。
abstractWe developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method
Reproduction assets foundThe authors publicly release their Python scripts for three-dimensional reconstruction, skeletonization, and curvature measurement (the paper's core phenotyping analysis) on their University of the Ryukyus website. No phenotype datasets or raw plant images are stated as deposited.Code · publicThis script was implemented in the Python language ( https://www.python.org/ ) and is executable in Windows and macOS. The script is only for academic purposes and freely available at our website ( https://ie.u-ryukyu.ac.jp/∼kunita/download_plant3d.html ).Open asset ↗lines:64-69Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.
Why it matches plant phenotyping methods衛星データから作物形質を推定し、GPRによる補間・時系列化とGEE上の再利用可能な処理ワークフローを構築・検証しており、フェノタイピング手法が中心である。
abstractthe building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe following link contains a repository with demo codes of the different procedures used in this paper https://github.com/msalinero/GEEGPRPhenoDemos.git .Open asset ↗GEEGPRPhenoDemoslines:353-362Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Determining the grain yield potential contributed by grain number is a step towards advancing the yield of cereal crops. To achieve this aim, it is pivotal to recognize the maximum yield potential (MYP) of the crop. In barley (Hordeum vulgare L.), the MYP is defined as the maximum spikelet primordia number of a spike. Many barley studies assumed the awn primordium (AP) stage to be the MYP stage regardless of genotypes and growth conditions. From our spikelet-tracking experiments using the two-rowed cultivar Bowman, we found that the MYP stage can be different from the AP stage. Importantly, we find that the occurrence of inflorescence meristem deformation and its loss of activity coincided with the MYP stage, indicating the end of further spikelet initiation. Thus, we recommend validating the barley MYP stage with the shape of the inflorescence meristem and propose this approach (named 'spikelet stop') for MYP staging. To clarify the relevance of AP and MYP stages, we compared the MYP stage and the MYP in 27 barley accessions (two- and six-rowed accessions) grown in the greenhouse and in the field. Our results reveal that the MYP stage can be reached at various developmental stages, which greatly depend on the genotype and growth conditions. Furthermore, we propose that the MYP stage and the time to reach the MYP stage can be used to determine yield potential in barley. Based on our findings, we suggest key steps for the identification of the MYP stage in barley that may also be applied in a related crop such as wheat.
Why it matches plant phenotyping methods花序メリステムの形状と活動停止から最大収量ポテンシャル段階を判定する「spikelet stop」手法を提案・検証しており、植物発達形質の取得法が中心である。
abstractThus, we recommend validating the barley MYP stage with the shape of the inflorescence meristem and propose this approach (named 'spikelet stop') for MYP staging.
Reproduction assets foundThe paper's spikelet-tracking phenotype data (spikelet ridge numbers, Waddington stages, GDDs, grain numbers for Bowman experiments and the 27-accession panel) are openly deposited in the Dryad Digital Repository, as stated in the Data availability statement.Dataset · publicThe data that support the findings of this study are openly available in Dryad Digital Repository at https://doi.org/10.5061/dryad.ffbg79cth ; Thirulogachandar and Schnurbusch, (2021) .Open asset ↗Dryad Digital Repository · 10.5061/dryad.ffbg79cthlines:71-101Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Current methods in measuring maize ( Zea mays L.) southern rust ( Puccinia polyspora Underw.) and subsequent crop senescence require expert observation which are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. Vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated the pedigree performances as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores. UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.
Why it matches plant phenotyping methodsUAS高スループット画像、植生指数、機械学習を用いてトウモロコシのさび病と老化を推定する方法が研究の中心であり、植物状態の取得・予測手法を実質的に評価している。
abstractunoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery
Reproduction assets foundThe preprint explicitly states that the authors' phenomic prediction analysis code (machine learning regressions in R caret) is publicly viewable on the first author's GitHub repository, matching an allowed URL. Supplementary Data 1 contains the phenomic data but no public URL is given for it, so only the code asset isCode · publicThe code used in this analysis is viewable at [ https://github.com/alperadak/phenomic-prediction-/blob/main/Phenomic%20prediction ].Open asset ↗alperadak/phenomic-prediction-lines:159-166Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.
Why it matches plant phenotyping methods葉・キャノピーの蛍光、反射、植生指数を用いて植物の光合成機能を測定・統合した再利用可能なデータセットであり、センサー計測系とデータ処理が研究の中心である。
abstractWith this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP).
Reproduction assets foundThis Data in Brief article explicitly deposits its maize leaf/canopy fluorescence, reflectance, VI and GEP time-series dataset (raw and collated files) in Mendeley Data under DOI 10.17632/b84jk376c3.1, and the FloX reflectance/SIF processing was performed with two author-maintained open-source R packages on GitHub (tomDataset · publicral Research Center (BARC)
City/Town/Region: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3Open asset ↗Mendeley Data · 10.17632/b84jk376c3.1lines:36-69Dataset · publicion: Beltsville, Maryland
Country: United States of America
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 39.030686, 76.84546
Timeframe: 2017 growing season (June-October)
Data accessibility
Repository name: Mendeley Data
Data identification number: http://dx.doi.org/10.17632/b84jk376c3.1
https://data.mendeley.com/datasets/b84jk376c3/draft?a=09b70ff8-599e-4405-a0f1-7a0c39e118fd
Related research articles
Campbell, P., K. Huemmrich, E. Middleton, et al. 2019. ``Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales.'' Remote Sensing , 11 (5): 488 [ 10.3390/rs11050488 ]
Yang, P., C. van der TolOpen asset ↗Mendeley Data · b84jk376c3lines:36-69Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
ArabidopsisRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
The plant kingdom contains a stunning array of complex morphologies easily observed above ground, but largely unexplored below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental to determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al. 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time. This dataset describes the developmental dynamics of 93 accessions and reveals highly complex and polygenic RSA traits that show significant correlation with climate variables.
Why it matches plant phenotyping methodsロボティクスによる表現型取得の自動化と画像解析パイプライン開発が中心で、根系構造の時系列形質を抽出するフェノタイピング基盤を提示している。
abstractIn this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the natural variation of RSA in Arabidopsis over time.
Reproduction assets foundThe paper's data availability statement deposits the GLORIAv2 phenotyping robot hardware, the image analysis pipelines/scripts used to extract root traits, the RShiny RSA exploration app, and the raw imaging data/images on Zenodo, all directly reproducing this paper's root phenotyping measurements and analysis.Dataset · public10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of Biological and
Environmental Research (DE-SC0008769 and DE-SC0018277) and the Carnegie Institution for
SOpen asset ↗Zenodo · 10.5281/zenodo.5709009pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5709009
Previously published datasets used: WORLCLIM2: Fick SE, Hijmans RJ, 2017, https://worldclim.org/,
https://doi.org/10.1002/joc.5086
Acknowledgements:
Work in the JRD lab was funded by the U.S. Department of Energy’s Office of BiologOpen asset ↗Zenodo · 10.5281/zenodo.5708422pdf-raw-page:13 lines:1-35Code · publicData availability:
GLORIAv2 is available through Zenodo, DOI: https://doi.org/10.5281/zenodo.5574925
Image analysis pipelines and scripts are available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708430
RShiny App for exploring root system architecture of accessions is available through Zenodo, DOI:
https://doi.org/10.5281/zenodo.5708422
Imaging data and images are available through Zenodo, DOI: https://doi.org/10.528Open asset ↗Zenodo · 10.5281/zenodo.5574925pdf-raw-page:13 lines:1-35Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
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Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
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thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
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experimental resources.
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Data Availability
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All the phenotypic data and images can be viewed and downloaded via the link
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(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
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References
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Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
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Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
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of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
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An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.
Why it matches plant phenotyping methodsRGB画像から果実をセグメンテーションし、画素数で果実サイズと成長を時系列推定する手法開発が中心である。
abstractIn this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net.
Reproduction assets foundThe authors explicitly state that their trained CROP neural network dictionaries and related programs are publicly available on GitHub. The paper's image datasets (Data_Fruit from Pixabay, farm pear images) are described but the annotations/datasets themselves are not deposited at a public URL; the USDA ARS image and CCode · publicthors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Our trained neural network CROP and the related programs are available on GitHub ( https://github.com/MotohisaFukuda/CROP , accessed on 20 October 2021). Some of the images used for the qualitative analysis in this paper came from the image gallery organized by United States Department of Agriculture, Agricultural Research Service ( https://www.ars.usda.gov/oc/images/image-gallery , accessed on 20 October 2021). Data_Fruit the training dataset inOpen asset ↗MotohisaFukuda/CROPlines:95-151Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red–green–blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the “green revolution” in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.
Why it matches plant phenotyping methodsUAV画像からデジタル表面モデルと時系列のイネ群落高を抽出し、遺伝解析に利用する高スループット表現型計測が研究の中心であるため。
abstractHigh-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field.
Reproduction assets foundThe paper's supplementary material explicitly contains the paper-specific phenotyping datasets (Supplementary Data 1: canopy height data; Supplementary Data 2: haplotype data; Supplementary Data 3-4: haplotype counts and time-course effects) used for the haplotype-based GWAS, and is publicly available at the Frontiers'Supplement · publicknowledgments
We thank Emi Abe, Aono Yuko, Terumi Satou, Megumi Suzuki, Yukari Shimazu, Tomomi Koguchi, Miho Shoji, and Mitsue Ito for the field support, and Matthew Shenton for scientific discussion and English editing of the manuscript.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.715184/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Chen Y., Sidhu H. S., Kaviani M., McElroy M. S., Pozniak C. J., Navabi A. (2019). Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat ( TritiOpen asset ↗lines:290-333Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisGrowth / development / phenology
Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.
Why it matches plant phenotyping methodsHTFP時系列から生育段階の時期やキャノピー形質を抽出し、段階的な統計処理を行う汎用データ処理手法の開発・検証が中心である。
abstractwe develop a general plot-level model for repeated measurements
Reproduction assets foundThe paper's Data availability statement explicitly declares that the data and source code supporting the study (the FIP plot-level phenotyping pipeline with P-splines/QMER trait extraction) are openly available in the authors' public ETH GitLab repository, with an archived ETH research-collection DOI.Code · publiconly for the merit of increased scholarly knowledge gen
eration, but in the interest of a more efficient workflow for crop breeding
to improve global nutrition aspects in times of climate change.
Data availability
Data and source code that support the findings of this study are
openly available in the ETH gitlab repository at https://gitlab.ethz.ch
/crop_phenotyping/htfp_data_processing and archived in the ETH
research collection (doi:10.5905/ethz-1007-385).
Authors’ contribution
Lukas Roth: conceptualization, methodology, software, formal
analysis, visualization, writing – original draft. María Xosé Rodríguez-
Álvarez: methodology, software, writing – review & editing.Open asset ↗crop_phenotyping/htfp_data_processingpdf-raw-page:14 lines:79-120Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks ( n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize "true stromata," but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.
Why it matches plant phenotyping methodsRGB画像からトウモロコシ葉のタースポット病徴（病斑・病害強度）を自動定量する輪郭検出アルゴリズムを開発・検証しており、植物病害表現型の取得法が中心である。
abstractHere, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves.
Reproduction assets foundThe paper's data availability statement explicitly states the original contributions (RGB leaf images, image blocks, and SCDA-related data) are publicly available at a Purdue PURR repository URL, which is an allowed URL.Dataset · publicThe original contributions presented in the study are publicly available. This data can be found here: https://purr.purdue.edu/publications/3820/2Open asset ↗purr.purdue.edu · publications/3820/2lines:669-700Code / 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 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 confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
Why it matches plant phenotyping methods植物フェノタイピング用の時系列3D点群データセットを構築し、手動アノテーションと複数の解析ベースラインを提供することが中心であり、再利用可能なデータ基盤として適格です。
abstractwe present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks
Reproduction assets foundThe paper's core contribution is the Pheno4D dataset of labeled maize and tomato plant point clouds, explicitly stated to be freely and publicly available at the authors' Bonn repository. A companion public data-loader API (Python/C++) is also provided on GitHub. Both are paper-specific, public, and directly actionableDataset · publicThe dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗https://www.ipb.uni-bonn.de/data/pheno4d/pdf-page:1 lines:1-65Code · publicWe provide the
code and the examples for loading the data at https://github.com/AIS-Bonn/data_loaders.Open asset ↗https://github.com/AIS-Bonn/data_loaderspdf-page:10 lines:1-57Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP-based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideas A low-cost greenhouse-based HTP platform was developed. Image-derived phenotypes presented moderate to high genomic heritabilities and correlations. Plant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions.
Why it matches plant phenotyping methods低コスト温室HTPプラットフォームを開発し、画像から植物高・キャノピー被覆・体積などの形質を抽出・検証しており、表現型取得手法が研究の中心である。
abstractIn this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes.
Reproduction assets foundThe article describes a low-cost greenhouse HTP platform and image-derived phenotyping of a 360-line tropical maize association panel under PGPB inoculation. The only paper-specific public asset explicitly referenced is a Mendeley Data deposit containing information about the maize panel used in the experiment. No phenDataset · publiclines was used to study the response
172 to PGPB. Of these, 179 inbred lines were from the Luiz de Queiroz College of Agriculture-
173 University of Sao Paulo (ESALQ-USP) and 181 were from the Instituto de Desenvolvimento
174 Rural do Paraná (IAPAR). More information about this panel is available on the Mendeley
175 platform (https://data.mendeley.com/datasets/5gvznd2b3n).
176 The inbred lines were evaluated under two managements: with (B+) and without (B-)
177 PGPB inoculation under nitrogen stress. The B+ management consisted of a synthetic pop-
178 ulation of four PGPB. Bacillus thuringiensis RZ2MS9, Delftia sp. RZ4MS18 (Batista et al.,
179 2018, 2021), Pantoea agglomerans 33.1 (Quecine Open asset ↗Mendeley · 5gvznd2b3npdf-layout-page:8 lines:1-37Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
Why it matches plant phenotyping methodsジャガイモの水ストレス表現型を取得するための非侵襲的イメージング・生理計測手法と装置構成が研究の中心であり、単なる生物学的測定ではない。
abstractThere is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Reproduction assets foundThe paper's MRI phenotyping data (3D images of potato tubers in pots under water deficit) are openly deposited in Data INRAE with an explicit DOI, as stated in the Availability of data and materials section. No author analysis code or trained models are reported.Dataset · publicThe MRI data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ ) repository at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/SFAXAA ).Open asset ↗Data INRAE · doi:10.15454/SFAXAAlines:160-172Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Laboratory / benchtopChlorophyll fluorescenceMicroscopyRootGrowth / time-series analysisVisualization / data management
Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root–microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.
Why it matches plant phenotyping methods植物根系全体を高解像度・経時的に撮像するための新規チャンバーを開発し、その撮像性能と用途を示しており、表現型取得法が研究の中心である。
abstractHere, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period.
Reproduction assets foundThe paper's computational analysis (K-means clustering and segmentation/cell counting of the 40× multispectral root image) is explicitly stated to have its environment, code, and parent data file available in the Supplementary Materials, hosted at the MDPI S1 link. Additionally, the 3D-printing-ready Imaging EcoFAB 3D-Code · publicThe environment, code, and parent data file are available in the Supplementary Materials .Open asset ↗lines:65-81Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modeling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang-Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature-response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose-response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度の温度データと低解像度の草丈データから、作物の温度応答パラメータを抽出するモデル手法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。
abstractIn a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data.
Reproduction assets foundThe paper's data and source code (phenotyping analysis for temperature dose-response extraction) are openly available in the ETH GitLab repository and archived in the ETH research collection.Code · publiconceptualization, Methodology, Writing - Review & Editing. Andreas Hund: Con-
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ceptualization, Supervision, Project administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10Open asset ↗crop_phenotyping/htfp_data_processingpdf-raw-page:16 lines:1-23Code · publicProject administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
16
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10.1101/2021.07.23.453040
doi:
bioRxiv preprintOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:16 lines:1-23Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
Why it matches plant phenotyping methods根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。
abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
Reproduction assets foundThe paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.Code · publicand
online Python image analysis and machine learning tutorials.
Availability of data and materials
The latest versions of ACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (GOpen asset ↗sourceforge.net/projects/acorbapdf-raw-page:17 lines:1-45Dataset · publicACORBA software training annotated libraries, source code, examples,
image pre-processing scripts, deep machine learning model training Jupyter notebooks and user
manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw
microscopy and scanner stacks used in this paper are available at ZENODO
(https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental
data).
Competing interests
The authors declare that they have no competing interests.
Funding
This work was supported by the European Research Council (Grant No. 803048), Charles
University Primus (Grant No. PRIMUS/19/SCI/09).
Author contributions
NBCS and MF conceived the pOpen asset ↗ZENODO · 10.5281/zenodo.5105719pdf-raw-page:17 lines:1-45Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Laboratory / benchtopRoot2D/3D reconstructionSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Why it matches plant phenotyping methods根系画像の深層セグメンテーションと3D装置を開発し、画像から根系形態・成長動態を自動抽出する方法が研究の中心である。
abstractHere we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.
Reproduction assets foundThe paper publicly releases its root segmentation image/annotation datasets, hardware files, and analysis code via GitHub repositories, plus supporting data in GigaDB. Three qualifying paper-specific assets with allowed URLs are listed; the GigaDB deposit (10.5524/100911) is paper-specific but its URL is not in the允许edCode · publicThe source code corresponding to ChronoRoot imaging controller, namely, the web interface to check and set up the image acquisition parameters:
Project name: ChronoRoot: Module Controller
Project home page: https://github.com/ThomasBlein/ChronoRootControlOpen asset ↗https://github.com/ThomasBlein/ChronoRootControllines:185-222Dataset · publicThe 2 datasets of images and annotations described in the Datasets section, as well as the 3D printing and laser cutting files, are publicly available at https://github.com/ThomasBlein/ChronoRootModuleHardware under the CERN Open Hardware License Version 2—Strongly Reciprocal licence.Open asset ↗https://github.com/ThomasBlein/ChronoRootModuleHardwarelines:223-262Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Abstract Chlorophyll fluorescence (ChlF) is a powerful non‐invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high‐throughput field phenotyping capabilities. The light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( ) and the kinetics of electron transport measured by reoxidation rates ( and ). Short‐ and long‐term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, and were little affected, while was consistently accelerated in water‐limited compared to well‐watered plants, increasingly so with rising vapour pressure deficit. This high‐throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.
Why it matches plant phenotyping methodsLIFTセンサーを用いた高スループットな圃場キャノピー蛍光計測が研究の中心であり、光合成形質を定量するフェノタイピング手法を実質的に適用している。
abstractThe light‐induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance.
Reproduction assets foundThe paper's raw and processed/cleaned LIFT chlorophyll fluorescence and spectral phenotyping datasets for both growing seasons are openly deposited on Zenodo (DOI 10.5281/zenodo.4305673), as stated in the methods and data availability statement. TERRA-REF is only cited as the meteorological data provider (infraction: aDataset · public) and 77,946 (97%) ChlF
transients in Y1 and Y2, respectively, were averaged, resulting in one
value per trait per plot per time of measurement (N = 5,544 data
points per trait in Y1; and N = 4,032 data points per trait in Y2). The
raw data and the processed and cleaned datasets for both growing
seasons are publicly accessible (https://doi.org/10.5281/zenodo.4305673).2.9 | Statistical analysis
A linear mixed model (LMM) approach was used to analyse the resolv-
able row-column designs with repeated measures for both Y1 and Y2.
Single-stage analysis models were applied to partition variance com-
ponents and to estimate genotypic effects for all traits based on “Best
Linear Unbiased PredictioOpen asset ↗Zenodo · 10.5281/zenodo.4305673pdf-raw-page:6 lines:1-96Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g., yield). Heritable variation of PHT is determined by many discovered quantitative trait loci; however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome-wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data show recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.
Why it matches plant phenotyping methodsUASを用いた週次測定により、従来法では見落とされるトウモロコシ草丈の時間変動を抽出し、遺伝子効果を検証しており、フェノタイピング手法の適用が研究上の中心的役割を果たしている。
abstractWeekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season.
Reproduction assets foundThe paper's Data Availability statement deposits its own UAS point cloud data, orthomosaic tif files, processing reports, ruler-based plant height measurements, UAV-derived canopy height measurements, field map, and experimental area files on figshare, plus supplementary material on the G3 figshare repository. TheseareDataset · publicRuler-based plant height measurements (Ruler measurement.xlsx), canopy height measurements derived from UAV-point cloud data (Uav-chm.xlsx), Field map (Field Map.xlsx), and Experimental area (Experimental area .pdf) are available at https://doi.org/10.6084/m9.figshare.13046306.v4 .Open asset ↗figshare · 10.6084/m9.figshare.13046306.v4lines:69-72Dataset · publicUAV-point cloud data (.laz files), processing reports (.pdf files), tif files, belonging to 06/19/19, 06/21/19, 06/28/19, 07/02/19, 07/09/19 and 07/12/19 flight dates, are available at https://doi.org/10.6084/m9.figshare.13269953.v1 .Open asset ↗figshare · 10.6084/m9.figshare.13269953.v1lines:69-72Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
Why it matches plant phenotyping methodsUAS画像から植生指数と草冠高を抽出し、機械学習で収量・開花期を推定する高スループット表現型解析が研究の中心です。
abstractUnoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials
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 · publicand PLSR regression. All R codes are available in Github repository (https://github.com/Open asset ↗pdf-page:8 lines:1-175Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ABSTRACT Advancements in the use of genome-wide markers have provided new opportunities for dissecting the genetic components that control phenotypic trait variation. However, cost-effectively characterizing agronomically important phenotypic traits on a large scale remains a bottleneck. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping has recently become a prominent method, as it allows large numbers of plants to be analyzed in a time-series manner. In this experiment, 233 inbred lines from the maize diversity panel were grown in a replicated incomplete block under both nitrogen-limited conditions and following conventional agronomic practices. UAV images were collected during different plant developmental stages throughout the growing season. A pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed. After applying the pipeline, about half a million plot-level image clips were obtained for 12 different time points. High correlations were detected between VIs and ground truth physiological and yield-related traits collected from the same plots, i.e., Vegetative Index (VEG) vs. leaf nitrogen levels (Pearson correlation coefficient, R = 0.73), Woebbecke index vs. leaf area ( R = -0.52), and Visible Atmospherically Resistant Index (VARI) vs. 20 kernel weight – a yield component trait ( R = 0.40). The genome-wide association study was performed using canopy coverage and each of the VIs at each date, resulting in N = 29 unique genomic regions associated with image extracted traits from three or more of the 12 total time points. A candidate gene Zm00001d031997 , a maize homolog of the Arabidopsis HCF244 ( high chlorophyll fluorescence 244 ), located underneath the leading SNPs of the canopy coverage associated signals that were repeatedly detected under both nitrogen conditions. The plot-level time-series phenotypic data and the trait-associated genes provide great opportunities to advance plant science and to facilitate plant breeding.
Why it matches plant phenotyping methodsUAV画像から作物プロットの被覆率・緑色度を抽出するパイプラインを開発し、地上測定との相関で検証した研究であり、フェノタイピング手法が中心です。
abstractA pipeline for extracting plot-level images, filtering images to remove non-foliage elements, and calculating canopy coverage and greenness ratings based on vegetation indices (VIs) was developed.
Reproduction assets foundThe paper's raw UAV RGB imagery used for the maize phenotyping pipeline is publicly deposited on CyVerse (DOI: 10.25739/4t1v-ab64), as stated in the supplied text. No author analysis code or trained models are described with public availability.Dataset · publicThe original UAV images
taken for this study are available at CyVerse (DOI: 10.25739/4t1v-ab64).Open asset ↗CyVerse · 10.25739/4t1v-ab64pdf-page:5 lines:1-38Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
Land surface phenology (LSP) enables global scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle it is necessary to understand limitations in measuring dryland dynamics. Here, using simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands, and highlighted important scale effects driven by within canopy VI variation. With high-resolution imagery the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.
Why it matches plant phenotyping methodsUAV画像とシミュレーションを用いて、乾燥地の植物フェノロジー検出アルゴリズムの実現可能性・精度・バイアスを評価しており、植物状態の測定法が中心です。
abstractusing simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all simulated VI data, simulated UAV pixel time series, and reproduction code in a public Zenodo repository (DOI 10.5281/zenodo.4777207), which directly reproduces this paper's phenotyping analysis.Code · publicAll simulated VI data, simulated pixel time series from UAV imagery, and code for reproducing this
analysis, is available in the Zenodo data repository (https://doi.org/10.5281/zenodo.4777207).Open asset ↗Zenodo · 10.5281/zenodo.4777207pdf-page:14 lines:1-51Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Traditional phenotyping methods, coupled with genetic mapping in segregating populations, have identified loci governing complex traits in many crops. Unoccupied aerial systems (UAS)-based phenotyping has helped to reveal a more novel and dynamic relationship between time-specific associated loci with complex traits previously unable to be evaluated. Over 1,500 maize (Zea mays L.) hybrid row plots containing 280 different replicated maize hybrids from the Genomes to Fields (G2F) project were evaluated agronomically and using UAS in 2017. Weekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year: optimal planting with irrigation (G2FI), optimal dryland planting without irrigation (G2FD), and a stressed late planting (G2LA). Plant height of different flights were ranked based on importance for yield using a random forest (RF) algorithm. Plant heights captured by early flights in G2FI trials had higher importance (based on Gini scores) for predicting maize grain yield (GY) but also higher accuracies in genomic predictions which fluctuated for G2FD (-0.06∼0.73), G2FI (0.33∼0.76), and G2LA (0.26∼0.78) trials. A genome-wide association analysis discovered 52 significant single nucleotide polymorphisms (SNPs), seven were found consistently in more than one flights or trial; 45 were flight or trial specific. Total cumulative marker effects for each chromosome's contributions to plant height also changed depending on flight. Using UAS phenotyping, this study showed that many candidate genes putatively play a role in the regulation of plant architecture even in relatively early stages of maize growth and development.
Why it matches plant phenotyping methodsUASによる生育期間中の植物高の反復取得と、収量予測・遺伝解析への利用が研究の中心であり、実質的な植物表現型取得ワークフローを扱っている。
abstractWeekly UAS flights captured variation in plant heights during the growing season under three different management conditions each year
Reproduction assets foundThe paper's UAS point-cloud/phenotype data are stated to be publicly available on CyVerse (Murray et al., 2019, DOI 10.25739/4ext-5e97), but that DOI is not among the allowed URLs, so it cannot be listed as an asset. Two qualifying paper-specific public assets are present in the allowed URLs: (1) the 2017 agronomic/wh�Dataset · publicAgronomic field data and weather data is available for 2017 (https://doi.org/10.25739/w560‐2114) (McFarland et al., 2020 ).Open asset ↗10.25739/w560‐2114lines:175-178Code / 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 confirmedEurope PMC · checked 9 Sept 2026
Field / plotWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data management
A critical shortage of 'big' agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network.
Why it matches plant phenotyping methods大規模な機械測定フェノタイプデータセットの構築・提供が中心であり、植物フェノタイピング研究用データ基盤に該当する。
abstractproviding a large non-relational database of agronomic trials
Reproduction assets foundThe paper is a data descriptor for a million-phenotype agronomic dataset (Australian National Variety Trials) with associated annotated R processing code, both deposited publicly on figshare as a collection by the authors. The figshare deposit directly contains the paper's phenotype data, environmental time series, andCode · publicCode availability
All data and code is available without restrictions from figshare19 and from the corresponding author on request.Open asset ↗figsharepdf-page:7 lines:1-62Code / dataset availability confirmedCrossref · checked 13 Sept 2026
The ability to quantify changes in the structural complexity of reefs and individual coral colonies that build them is vital to understanding, managing, and restoring the function of these ecosystems. However, traditional methods for quantifying coral growth in situ fail to accurately quantify the diversity of morphologies observed both among and within species that contribute to topographical complexity. Three-dimensional (3D) photogrammetry has emerged as a powerful tool for the quantification of reefscape complexity but has yet to be broadly adopted for quantifying the growth and morphology of individual coral colonies. Here we debut a high-throughput method for colony-level 3D photogrammetry and apply this technique to explore the relationship between linear extension and other growth metrics in Acropora cervicornis . We fate-tracked 156 individual coral transplants to test whether initial growth can be used to predict subsequent patterns of growth. We generated photographic series of fragments in a restoration nursery immediately before transplanting to natural reef sites and re-photographed coral at 6 months and 1 year post-transplantation. Photosets were used to build 3D models with Agisoft Metashape, which was automated to run on a high-performance computing system using a custom script to serially process models without the need for additional user input. Coral models were phenotyped in MeshLab to obtain measures of total linear extension (TLE), surface area, volume, and volume of interstitial space (i.e., the space between branches). 3D-model based measures of TLE were highly similar to by-hand measurements made in the field ( r = 0.98), demonstrating that this method is compatible with established techniques without additional in water effort. However, we identified an allometric relationship between the change in TLE and the volume of interstitial space, indicating that growth in higher order traits is not necessarily a linear function of growth in branch length. Additionally, relationships among growth measures weakened when comparisons were made across time points, implying that the use of early growth to predict future performance is limited. Taken together, results show that 3D photogrammetry is an information rich method for quantifying colony-level growth and its application can help address contemporary questions in coral biology.
Why it matches plant phenotyping methodsサンゴ個体群の3Dフォトグラメトリによる形態・成長形質取得法を開発し、自動処理と既存手法との比較検証を行っているため、植物体（サンゴ）の表現型計測法が中心である。
abstractHere we debut a high-throughput method for colony-level 3D photogrammetry
Reproduction assets foundThe paper's phenotype dataset (TLE, SA, V, Vinter for 156 A. cervicornis colonies) is publicly deposited in the authors' GitHub repository Frontiers3Dmorphology, and the custom Metashape automation scripts are in Coral3DPhotogram; both are paper-specific, public, and actionable.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/wyattmillion/Frontiers3Dmorphology .Open asset ↗Frontiers3Dmorphologylines:321-372Code · publicAll bioinformatic scripts used to run Metashape on the command line can be found at https://github.com/wyattmillion/Coral3DPhotogram .Open asset ↗Coral3DPhotogramlines:271-276Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Monitoring of environmental restoration is essential to communicate progress and improve outcomes of current and future projects, but is typically done in a very limited capacity due to budget and personnel constraints. Unoccupied aerial vehicles (UAVs) have been used in a variety of natural and human-influenced environments and have been found to be time- and cost-efficient, but have not yet been widely applied to restoration contexts. In this study, we evaluated the utility of UAVs as an innovative tool for monitoring tidal marsh restoration. We first optimized methods for creating high-resolution orthomosaics and Structure from Motion digital elevation models from UAV imagery by conducting experiments to determine an optimal density of ground control points (GCPs) and flight altitude for UAV monitoring of topography and new vegetation. We used elevation models and raw and classified orthomosaics before, during, and after construction of the restoration site to communicate with various audiences and inform adaptive management. We found that we could achieve 1.1 cm vertical accuracy in our elevation models using 2.1 GCPs per hectare at a flight altitude of 50 m. A lower flight altitude of 30 m was more ideal for capturing patchy early plant cover while still being efficient enough to cover the entire 25-hectare site. UAV products were valuable for several monitoring applications, including calculating the volume of soil moved during construction, tracking whether elevation targets were achieved, quantifying and examining the patterns of vegetation development, and monitoring topographic change including subsidence, erosion, and creek development. We found UAV monitoring advantageous for the ability to survey areas difficult to access on foot, capture spatial variation, tailor timing of data collection to research needs, and collect a large amount of accurate data rapidly at relatively low cost, though with some compromise in detail compared with field monitoring. In summary, we found that UAV data informed the planning, implementation and monitoring phases of a major landscape restoration project and could be valuable for restoration in many habitats.
Why it matches plant phenotyping methodsUAV画像から植生被覆・発達を定量化するための撮影高度、GCP密度、オルソモザイクおよびSfM手法を最適化・精度評価しており、植物状態の取得技術が実質的に中心である。
abstractWe first optimized methods for creating high-resolution orthomosaics and Structure from Motion digital elevation models from UAV imagery by conducting experiments to determine an optimal density of ground control points (GCPs) and flight altitude for UAV monitoring of topography and new vegetation.
Reproduction assets foundThe paper's data availability statement points to a public Figshare collection (DOI 10.6084/m9.figshare.c.5226785.v1) containing the study's UAV-derived datasets (orthomosaics, elevation models, vegetation analyses) for the Hester Marsh restoration monitoring. This is a paper-specific, publicly accessible asset. No作者分析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/10.6084/m9.figshare.c.5226785.v1 (Figshare).Open asset ↗Figshare · 10.6084/m9.figshare.c.5226785.v1lines:628-638Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.
Why it matches plant phenotyping methods植物の時系列3D点群を登録し、骨格表現に基づいて器官レベルの形質追跡を可能にする新規計算手法を開発・検証しており、フェノタイピング手法が中心である。
abstractIn this paper, we investigate the problem of registering 3D point clouds of the plants over time and space.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides both the 4D plant point cloud datasets (maize and tomato laser-scanner time series used for the phenotyping/registration experiments) and the authors' implementation code, each with a public URL.Dataset · publicavailable at https://www.ipb.uni-bonn.de/data/4d- tems for automated temporal plant-trait analysis by tracking plant traits at an organ level.Open asset ↗pdf-page:1 lines:1-63Code · publicThe code for our approach is
available at https://github.com/PRBonn/4d_plant_
registration.Open asset ↗pdf-page:1 lines:1-63Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (PlatFW) and multi-rotor (PlatMR) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using PlatFW were consistent with those obtained from PlatMR, showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06–0.97). Finally, we also observed high Spearman rank correlations (0.47–0.91) and R2 (0.63–0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.
Why it matches plant phenotyping methodsUAV-RGB画像と3D作物表面モデルによるコムギ草丈推定法を開発・検証し、地上測定との比較、精度評価、複数プラットフォーム間の検証を行っており、表現型取得手法が研究の中心である。
abstractThis study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years.
Reproduction assets foundThe paper's authors publicly deposited the R scripts used for UAV image analysis and plant-height trait extraction on GitHub. The raw phenotyping data are only available on request. Pix4D support articles and the R boot package are generic third-party resources, not paper-specific assets.Code · publicThe PHaerial scripts used to perform the image analyses and trait extract are available at https://github.com/volpatoo/HTP-via-drone-imagery/tree/master/UAV-HTP_PlantHeightOpen asset ↗volpatoo/HTP-via-drone-imagery · UAV-HTP_PlantHeightlines:519-573Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
FlowerGrowth / time-series analysisGrowth / development / phenology
Phenology, the study of the timing of cyclical life history events and seasonal changes, is a fundamental aspect of how individual species, communities, and ecosystems will respond to climate change. Both biotic and abiotic phenological patterns are changing rapidly in response to changing seasonal temperatures and other climate-related drivers, and the consequences of these shifts for individual species and entire ecosystems are largely unknown. Landscape-scale simulations can address some of these needs for better predictions by demonstrating how phenology measures can vary with spatial and temporal grain of observations, and how phenological responses can vary with landscape heterogeneity and climate drivers. To explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities. This enabled us to examine phenology measures and environmental sensitivities along a continuum of spatial and temporal grains, while also controlling other aspects of sampling design. By relating measures of phenology calculated at a given spatiotemporal grain to average environmental conditions at that same grain size, we are able to determine observed environmental sensitivities for multiple phenological metrics at that spatial and temporal scale. We demonstrate that different phenological events change distinctly and predictably with spatial and temporal measurement scale, opening the way to incorporating scaling laws into predictions. Using plant flowering as our example, we identify that the timing of the beginnings or ends of an event (e.g., First Flower date, Last Flower date), can be especially sensitive to the spatial and temporal grain (or resolution) of observations. Our work provides an initial assessment of the role of observation scale in landscape phenology, and a general approach for incorporating scale-dependence into predictions of a variety of phenological time series.
Why it matches plant phenotyping methods植物の開花フェノロジー指標を空間・時間スケール別に算出・評価するシミュレーション手法を中心に扱い、観測スケール依存性を組み込む一般的アプローチを提示している。
abstractTo explicitly examine the spatial and temporal scale-dependence of multiple phenology measures, we constructed simulated landscapes populated by virtual plant species with realistic phenologies and environmental sensitivities.
Reproduction assets foundThe paper's simulation and analysis code is explicitly deposited on GitHub with an authors' public URL; no separate phenotype dataset deposit is stated (field datasets are cited prior work).Code · publicHarte and Newman 2014).For each phenology measure, we determined scaling effects
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by comparing the phenology measures computed at a given scale to the measures taken at the
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finest spatial and temporal scale available: 2m grain size and daily sampling. Code for the
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simulation and detailed methods can be found at:
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https://github.com/ibreckhe/phenoscaling_sims
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Our simulation approach also allowed us to examine the scale-dependence of observed
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environmental sensitivities. The phenology of the virtual species respond to two aspects of the
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environment: the timing of seasonal snowpack disappearance (snow disappearance day, SDD)
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and the accumulation of air temperatOpen asset ↗ibreckhe/phenoscaling_simspdf-raw-page:6 lines:1-61Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F v /F m ) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Why it matches plant phenotyping methods植物病害の症状・重症度・進展を画像から定量化する方法の開発とワークフロー提示が中心であり、植物状態の表現型取得に該当する。
abstractHere, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves.
Reproduction assets foundThe authors explicitly state that all R and ImageJ scripts and the study data are openly available in their public GitHub repository, which directly reproduces this paper's Botrytis symptom phenotyping analysis.Code · publicAll R and ImageJ script generated to process are available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Dataset · publicThe data presented in this study are openly available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.
Why it matches plant phenotyping methods植物HTPデータから形質を抽出・補正する解析パイプラインを開発し、実データとシミュレーションで検証しているため、方法が中心的です。
abstractwe developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment.
Reproduction assets foundThe paper explicitly provides two public GitHub repositories: the SpaTemHTP R pipeline package and a validation repository containing all data, scripts, and functions needed to reproduce the paper's phenotyping analyses. Raw phenotypic data itself is only available on request.Code · publicThe R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP .Open asset ↗ICRISAT-GEMS/SpaTemHTPlines:316-319Code · publicAll data, scripts, and functions required to reproduce the results can be found at: https://github.com/ICRISAT-GEMS/SpaTemHTP_Validation .Open asset ↗ICRISAT-GEMS/SpaTemHTP_Validationlines:457-479Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
BACKGROUND: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. RESULTS: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. CONCLUSION: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data.
Why it matches plant phenotyping methods屋外HTPのロードセルデータから蒸散応答の特徴量とVPDブレークポイントを自動抽出し、遺伝型を識別する解析手法が研究の中心であるため。
abstractoutdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicInterested readers can find the R scripts on the open-source GitHub platform, https://github.com/KSoumya/EZTr .Open asset ↗KSoumya/EZTrlines:185-203Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
This study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system in response to factorial low and high nitrogen (N) and phosphorus (P) application. HTP has not previously been used to examine pasture species in mixture. We used red-green-blue (RGB) imaging to obtain smoothed projected shoot area (sPSA) to predict absolute growth (AG) up to 70 days after planting (sPSA, DAP 70), to identify variation in relative growth rates (RGR, DAP 35-70) and detect overyielding (an increase in yield in mixture compared with monoculture, indicating facilitation) in a grass-legume model pasture. Finally, using principal components analysis we interpreted between species changes to HTP-derived temporal growth dynamics and nutrient uptake in mixtures and monocultures. Overyielding was detected in all treatments and was driven by both grass and legume. Our data supported expectations of more rapid grass growth and augmented nutrient uptake in the presence of a legume. Legumes grew more slowly in mixture and where growth became more reliant on soil P. Relative growth rate in grass was strongly associated with shoot N concentration, whereas legume RGR was not strongly associated with shoot nutrients. High throughput, image-based phenotyping was a useful tool to quantify growth trait variation between contrasting species and to this end is highly useful in understanding nutrient-yield relationships in mixed pasture cultivations.
Why it matches plant phenotyping methodsRGB画像による高スループット表現型計測を用いて、投影シュート面積から成長形質を抽出・定量する手法の実質的な適用研究であり、表現型取得が中心的です。
abstractThis study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system
Reproduction assets foundThe authors deposited the manuscript's underlying phenotype data (sPSA/growth trait measurements from the HTP experiment) in Figshare, with a provisional DOI (10.25909/12895121) and an access link. This is a paper-specific public data asset. The R packages cited (dae, growthPheno, asremlPlus) are generic third-party CRDataset · publicthe associated data for the manuscript has now been placed in a repository and the provisional DOI is 10.25909/12895121, which once published will be linked to the article. Until the article is published, the data can be viewed via this private link: https://figshare.com/s/99e05c190be6cb416164Open asset ↗figshare · 10.25909/12895121lines:657-683Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 9 Sept 2026
ABSTRACT High-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.
Why it matches plant phenotyping methods圃場画像から植物を分割し、草丈などの形質を自動抽出・曲線近似する高スループット表現型解析パイプラインの開発が主題であり、方法的貢献が明確です。
abstractWe propose a pipeline to extract and analyze the plant traits for field phenotyping systems.
Reproduction assets foundThe paper explicitly states that the authors' R pipeline code and sample image data are publicly available on GitHub, directly supporting this paper's plant segmentation, trait extraction, and growth-curve fitting analysis.Code · publicThe R codes of the proposed pipeline, sample image data and description are available on Github at https://github.Open asset ↗pdf-page:17 lines:1-54Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel -1 ) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count ( R 2 = 0.52), but strong for tiller count ( R 2 = 0.86) and GS30 ( R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.
Why it matches plant phenotyping methodsドローン多視点画像と画像解析により、コムギの出芽・分げつ数・茎伸長を推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractWe aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery.
Reproduction assets foundThe paper publicly releases its authors' phenotyping processing code (multiview image generation, segmentation, early growth trait extraction) on ETH GitLab and secondary plot-based phenotype data (BLUEs, BLUPs, repeatability, heritability) on the ETH Research Collection. Raw UAS images are only available upon request,Code · publicfor the field management and freezing damage ratings at site FIP (all persons ETH Zurich, Zürich, Switzerland). We thank the anonymous reviewers for the thorough evaluation and constructive suggestions.
Additional Points
Source Code . Maintained source code for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentatiOpen asset ↗PhenoFly_data_processing_toolslines:218-251Code · publiccode for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GiOpen asset ↗PhenoFly_data_processing_tools/ImageProjectionAgisoftlines:218-251Code · publictools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GitLab repository furthermore includes the following trait extraction method: (4) Early growth trait extraction ( https://gitlab.ethz.ch/crop_pOpen asset ↗PhenoFly_data_processing_tools/ActiveLearningSegmentationlines:218-251Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.
Why it matches plant phenotyping methods自動画像計測による日次シュートバイオマス推定データを用い、動的な植物表現型応答をモデル化する新しいゲノム対応成長モデルを提案しており、表現型解析ワークフローが中心的です。
abstractWe propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability.
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 data and codes used in this study can be accessed at https://github.com/malachycampbell/RiceCGM/tree/master .Open asset ↗malachycampbell/RiceCGMlines:109-215Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.
Why it matches plant phenotyping methods植物の共焦点画像から細胞周期マーカーを自動検出・分割する機械学習手法を開発し、定量評価・比較検証しているため、植物表現型取得が中心である。
abstractAutomating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the confocal image datasets, annotations, and the annotation plugin in a public GitLab repository, matching the allowed URL.Code · publicample. As a pixel-wise segmentation is already produced by the network, but refined in post-processing to a single location in space, the network is already partially capable of generating meaningful 3D shape labels.
Data Availability Statement
The datasets and plugin used for this study can be found in the GitLab repository at https://gitlab.com/faraz.khan1/volumetric-segmentation-of-confocal-images .
Author Contributions
FK designed and implemented the computational algorithms, models and experiments. MP wrote the annotation tool and provided guidance. UV performed biological experiments and annotation. AF managed the project and helped design the approaches, with MP and FK. All authors coOpen asset ↗gitlab.com/faraz.khan1/volumetric-segmentation-of-confocal-imageslines:320-348Code / dataset availability confirmedCrossref · checked 9 Sept 2026
In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.
Why it matches plant phenotyping methods高解像度NDVI時系列から植物の生産性・季節動態を抽出し、教師なしクラスタリングと距離尺度比較で空間ゾーニングする解析手法が研究の中心であるため、植物状態の計測・推定を伴う実質的な方法適用と判断する。
abstractwe use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series.
Reproduction assets foundThe paper's supplementary materials, explicitly hosted at the MDPI supplementary URL, contain the paper-specific phenotyping assets: true-color and NDVI/snow-classified PlanetScope satellite imagery for all dates used, soil moisture sensor time series by zone, and the PlanetScope image IDs. No author analysis code is公开Dataset · publicSupplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/17/2733/s1,
Figure S1: True color satellite imagery of scenes from all dates used in this study, Figure S2: NDVI and snow
classification of satellite imagery from all dates used in this study, Figure S3a,b: Time series of soil volumetric
water content from hourly sensor measurements by zone for 1 June–10 August 2017 and 2018, Table S1: Image IDs
for PlanetScope satellite imagery used in this studyOpen asset ↗mdpi.compdf-page:17 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
BACKGROUND: (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. RESULTS: The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. CONCLUSIONS: DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しているため、方法開発が中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe authors explicitly state their DAPD normalization and segmentation code is publicly available on GitHub.Code · publicOur code and a Python notebook come with a friendly user manual detailing how to use it, and they are available at https://github.com/diloc/DAPD_Normalization.git .Open asset ↗diloc/DAPD_Normalizationlines:73-78Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an Intersection over Union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyse a total of 355027 images, which required 24-36 hours. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.
Why it matches plant phenotyping methods大規模カメラ撮像システムとGreenotyper画像解析パイプラインを開発・評価し、植物位置検出とセグメンテーション精度を検証しているため、植物表現型取得法が中心である。
abstractHere, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1800 white clover (Trifolium repens) plants.
Premise Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods High-throughput time series image data from water-deprived maize ( Zea mays subsp. mays ) and sorghum ( Sorghum bicolor ) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.
Why it matches plant phenotyping methodsLAXは画像から葉角度を抽出・定量するために開発された高スループット画像処理フレームワークであり、植物表現型取得手法が研究の中心です。
abstractA MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.
Reproduction assets foundThe paper's authors explicitly state that the LAX source code and GUI are publicly available on GitHub, and the paper's time-lapse image data (Video S1) is publicly hosted on Vimeo. Both are paper-specific, public, and actionable.Code · publicnowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The source code and GUI interface are available at https://github.com/Kenchanmane‐Raju/Leaf‐Angle‐eXtractor .
LITERATURE CITED
Araus , J. L.
,
S. C.
Kefauver
,
M.
Zaman‐Allah
,
M. S.
Olsen
, and
J. E.
Cairns
. 2018
Translating high‐throughput phenotyping into genetic gain
. Trends in Plant Science
23 ( 5 ): 451 – 466 .
29555431
10.1016/j.tplants.2018.02.001
PMC5931794
Awada , L.
,
P. W. B.
Phillips
, and
S. J.
Smyth
.Open asset ↗Kenchanmane‐Raju/Leaf‐Angle‐eXtractorlines:182-386Dataset · publicgle boxes and leaf number. Clicking the ‘Export Data’ icon at the bottom outputs leaf angle measurements for the selected leaves as a .csv file .
Click here for additional data file.
VIDEO S1. Time‐lapse video showing the drop of maize leaves in response to water deficit stress over a single day. This video is also available at https://vimeo.com/256137800 .
Click here for additional data file.
Acknowledgments
This study was supported by a Science without Borders scholarship (214038/2014‐9) to D.S.C., by the USDA National Institute of Food and Agriculture (award 2016‐67013‐24613) to J.C.S., and by the National Science Foundation (grant no. OIA‐1557417).
Data Availability
The sourOpen asset ↗lines:182-386Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Lignocellulosic biomass (LB) is recalcitrant to enzymatic hydrolysis due to its compact and complex cell wall structure. To identify the parameters behind LB recalcitrance, experimental data over hydrolysis time must be collected. Here, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis. The protocol includes instructions for sample preparation, layout of a custom designed incubation chamber and instructions for confocal time lapse acquisition. The protocol sets out a detailed plan where cross-sections of untreated and pretreated poplar samples are mounted in a sealed frame containing a buffer and an enzymatic cocktail. The sealed frame is then placed into an incubator to maintain the sample at a constant temperature of 50 °C, which is optimal for enzymatic reaction while avoiding enzymatic cocktail evaporation. Using lignin natural autofluorescence, confocal z-stacks of untreated and pretreated samples were acquired at regular time intervals during enzymatic hydrolysis for 24 h. Acquisition parameters were optimized to compromise between image resolution and reduced photo-bleaching. The acquired image might then be processed by further development of algorithms to extract precise quantitative information on cell wall deconstruction. This protocol is an important first step towards elucidating the underlying parameters of LB recalcitrance by allowing the acquisition of high-quality images of LB hydrolysis for extracting quantitative data on LB deconstruction.
Why it matches plant phenotyping methodsポプラ細胞壁の分解状態を時系列の共焦点3D画像で取得するプロトコル自体が中心であり、植物組織状態の定量的表現型抽出を可能にするため。
abstractHere, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis.
Reproduction assets foundThe paper's authors state that the scripts for computing photobleaching signal loss and image registration/analysis are publicly available in the FARE Laboratory GitLab repository, with an explicit URL matching an allowed URL.Code · publicels’ intensity
reduction in confocal image) between successive z-stacks. The signal loss was computed by subtracting
the voxels’ intensities between the registered floating image, It ◦ T It ←It+∆t , and the reference image It+∆t
and summing up the subtracted values (Scripts are available at the FARE Laboratory Gitlab Repository
https://gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020).
4. Results
Using the protocol, we acquired confocal images of pretreated poplar samples during hydrolysis
(Figure 7). We could visually observe that the cell walls gradually degraded over time. To illustrate
the advantages offered by using this protocol to achieve a quantitative characterizaOpen asset ↗gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020pdf-layout-page:8 lines:1-42Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
LettuceAerial / UAVFlowerSegmentationGrowth / time-series analysisGrowth / development / phenology
Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time. One sentence summaryMachine learning and Bayesian analyses of drone-mediated remote phenotyping data revealed two genetic loci regulating differential daily flowering time in lettuce (Lactuca spp.).
Why it matches plant phenotyping methodsドローン画像、機械学習、ベイズ推定を用いた花開花時刻の表現型取得・抽出が研究の中心であり、方法の精度も評価されている。
abstractthe floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping
Reproduction assets foundThe paper's Data Availability statement provides two paper-specific public assets: authors' analysis scripts (machine learning and Bayesian inference) on GitHub, and the GPS-anchored drone aerial image data on HydroShare. Both are directly used for this paper's phenotyping and analysis.Code · public51 2015-51181-24283 to RWM.
452
453 Data Availability
454 GBS data of the RILs and WGS data of the parents are available on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗rkbhan/FloralOpeningpdf-layout-page:20 lines:1-57Dataset · publicavailable on the NCBI SRA database under
455 BioProjects PRJNA642889, PRJNA510128, and PRJNA478460, respectively. Scripts used in the
456 study for machine learning and Bayesian inference are available on GitHub at
457 https://www.github.com/rkbhan/FloralOpening. GPS-anchored aerial image data are available on
458 HydroShare at https://www.hydroshare.org/resource/1c5855dbeb3c49a8b5779300550e08f1/.
20Open asset ↗pdf-layout-page:20 lines:1-57Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
BarleyChickpeaGreenhouseRootGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
SUMMARY The phenotypic analysis of root system growth is important to inform efforts to enhance plant resource acquisition from soils; however, root phenotyping remains challenging because of the opacity of soil, requiring systems that facilitate root system visibility and image acquisition. Previously reported systems require costly or bespoke materials not available in most countries, where breeders need tools to select varieties best adapted to local soils and field conditions. Here, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters. All components of the system are made from locally available commodity components, facilitating the adoption of this affordable technology in low‐income countries. The rhizobox is large enough (approximately 6000 cm 2 of visible soil) to avoid restricting vertical root system growth for most if not all of the life cycle, yet light enough (approximately 21 kg when filled with soil) for routine handling. Support structures and an imaging station, with five cameras covering the whole soil surface, complement the rhizoboxes. Images are acquired via the Phenotiki sensor interface, collected, stitched and analysed. Root system architecture (RSA) parameters are quantified without intervention. The RSAs of a dicot species ( Cicer arietinum , chickpea) and a monocot species ( Hordeum vulgare , barley), exhibiting contrasting root systems, were analysed. Insights into root system dynamics during vegetative and reproductive stages of the chickpea life cycle were obtained. This affordable system is relevant for efforts in Ethiopia and other low‐ and middle‐income countries to enhance crop yields and climate resilience sustainably.
Why it matches plant phenotyping methods土壌栽培植物の根系構造を画像取得・解析する、低コストのrhizoboxおよび多カメラ撮像システムを開発しており、根系形態の定量化が研究の中心である。
abstractHere, we report an affordable soil‐based growth (rhizobox) and imaging system to phenotype root development in glasshouses or shelters.
Reproduction assets foundThe paper's data availability statement deposits software, test data, and rhizobox CAD files publicly at the Edinburgh DataShare DOI 10.7488/ds/2841, and materials are also linked at chickpearoots.org/resourcesandlinks. The analysis pipeline code itself is only available on request.Dataset · public, TB, CC and IR developed the growth conditions
for chickpea growth in rhizoboxes. TB, CC, VG, IR, ST and
PD wrote the paper.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Software, test data for its evaluation and CAD files to con-
struct rhizoboxes have been made available at: https://doi.org/10.7488/ds/2841. Data and code implementing the anal-
ysis pipeline is available on request by emailing the senior/
co-corresponding authors.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. Imaging station for imaging of a rhizobox.
Figure S2. Diagram of image capture anOpen asset ↗10.7488/ds/2841pdf-raw-page:13 lines:1-98Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
SegmentationGrowth / time-series analysisGrowth / development / phenology
High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package "implant" in R for both robust feature extraction and functional data analysis. For image processing, the "implant" package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.
Why it matches plant phenotyping methods植物画像からの特徴抽出と成長曲線解析を一体化したRパッケージを開発しており、表現型取得・解析手法が研究の中心である。
abstractWe develop the package "implant" in R for both robust feature extraction and functional data analysis.
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 package, documentation, and user guide are available online at https://github.com/rwang14/implant .Open asset ↗rwang14/implantlines:109-123Code / dataset availability confirmedCrossref · checked 9 Sept 2026
One of the essential factors in the root zone environment that affects plant growth is temperature. Determining the optimal root zone temperature condition in a hydroponic system during cultivation could lead to an improvement in plant growth. An optimal control strategy can be determined by identifying the eco-physiological process using a dynamic model. However, it is difficult to develop a dynamic model of the responses of plant growth to root zone temperature because the eco-physiological processes of plants are quite complicated. We propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature. The responses of chili pepper plant growth as affected by root zone temperature were measured during 60 days of cultivation inside a growth chamber using a non-destructive and continuous system based on a load cell. Five datasets of dynamic responses of plant growth were obtained for system identification. The results suggest that the application of a neural network is useful for modeling the dynamic response of plant growth to root zone temperature in hydroponic cultivation, with promising performance.
Why it matches plant phenotyping methods植物成長を連続・非破壊に測定するロードセル系と、成長応答を推定するNARXニューラルネットワーク動的モデルが研究の中心であり、植物成長という形質の取得・モデル化手法に該当する。
abstractWe propose an intelligent approach that can deal with this complex system. Non-linear autoregressive with exogenous input (NARX) neural networks were used to develop a dynamic model of the responses of plant growth to root zone temperature.
Reproduction assets foundThe paper cites the authors' own public Matlab program script for the NARX modeling of plant growth response to root zone temperature, hosted on GitHub (reference 47), which qualifies as a paper-specific public analysis code asset. No public phenotype dataset deposit is stated; the five measurement datasets are not明确lyCode · publicAji, G. K.; Hatou, K.; Morimoto, T. Matlab Program Script for Modeling the Dynamic Response of Plant
Growth to Root Zone Temperature in Hydroponic Chili Pepper Plant using Neural Network Available
online: https://github.com/mradjie/narx‐plant‐growthOpen asset ↗mradjie/narx‐plant‐growthpdf-page:14 lines:1-47Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of "affordable phenotyping" solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
Why it matches plant phenotyping methods植物の熱画像ロボット、根・シュート用プレートイメージャ、環境センサープラットフォームを開発しており、表現型取得基盤が研究の中心である。
abstractWe present two robot vectors for automated plant phenotyping under controlled conditions.
Reproduction assets foundThe paper is a hardware/software design paper for low-cost phenotyping vectors and an IoT environmental sensor platform. It contains no public phenotype/trait datasets or plant images, but the authors explicitly deposit all 3D-printed hardware files, microcontroller sketches, LabVIEW control software, PCB schematics/fcCode · publiccontrolled by a program written in the LabVIEW development environment [ 18 ] running on the host computer. This provides a user-friendly graphical interface for control of the vector (distances moved, time-lapse parameters, etc.) and imaging sensor ( Figure 3 ). The microcontroller sketch and LabVIEW software are available at https://github.com/UoNMakerSpace/themal-imager-software . Once acquired, image sets are processed for leaf temperature values at multiple points on each rosette using macros written for the ImageJ/FIJI image analysis platforms [ 19 , 20 ].
2.1.4. Performance and Results
Operating characteristics of the Thermal Imager are given in Table 2 . For comparison, characteristiOpen asset ↗UoNMakerSpace/themal-imager-softwarelines:39-47Code · publicvidual directories for each plate position with unique filenames including acquisition time and date. Experimental settings can be saved as a configuration file and re-loaded on subsequent experimental runs, ensuring that image sets are appended to the same directory. The microcontroller sketch and LabVIEW code are available at https://github.com/UoNMakerSpace/plate-imager-software .
2.2.4. Performance
Characteristics of the Plate Imager are given in Table 3 . For comparison, characteristics of a previously published research system [ 26 ] and a typical commercially-available actuator are also given.
The Plate Imager outperforms the CPIB Imaging Robot (see Table 2 ) in all measured parameterOpen asset ↗UoNMakerSpace/plate-imager-softwarelines:48-56Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV-based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV-based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.
Why it matches plant phenotyping methodsUAV画像とSfMによる圃場作物の草丈推定手法を開発し、手測定との比較で検証した研究であり、表現型取得パイプラインが中心です。
abstractWe have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' image analysis and trait extraction scripts in a public GitHub repository, which directly implements the UAV plant-height phenotyping pipeline described in the paper. No phenotype dataset deposit is stated; supporting information files are not URLCode · publicThe scripts and processes used to perform the image analyses and trait extraction are available at https://github.com/SBTirado/UAV_PH.git .Open asset ↗SBTirado/UAV_PHlines:182-204Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Increasing dry matter yield is the most important objective in perennial ryegrass breeding program. Current yield assessment methods are time-consuming and subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based dry matter yield (DMY) yield estimation of individual plants through normalised difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing fifty advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-correlation between the predicted and measured DMY across three seasons with R² between 0.19- 0.81 and root mean square errors (RMSE) values ranging from 0.09-0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.
Why it matches plant phenotyping methods植物バイオマス収量を推定する画像・センサー・計算ワークフローの開発と検証が研究の中心であり、高スループット表現型解析手法として明確に該当する。
abstractThis study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass
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 · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:407-424Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 9 Sept 2026
Abstract Background Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. Results The DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.
Why it matches plant phenotyping methods植物表現型ハイスループット測定の時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しており、方法が研究の中心である。
abstractWe developed the Digital Adjustment of Plant Development (DAPD) normalization method.
Reproduction assets foundThe paper's DAPD normalization and segmentation analysis code is explicitly stated to be publicly available on the authors' GitHub repository, matching an allowed URL. No phenotype dataset or image deposit is stated; the in-house dataset is not publicly shared.Code · publicOur code is available for reuse at https://github.com/diloc/DAPD_Normalization.git.Open asset ↗diloc/DAPD_Normalizationpdf-page:14 lines:1-67Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus scoparius homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve this task is important for effectively capturing change and evaluating management activities.This study combines traditional field-based measurements with novel Light Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System (UAS). We investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m 2 ). Presence-absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of C. scoparius were harvested for biomass measurements. With a UAS LiDAR system, we collected ultrahigh-density spatial data across the area in October 2017 (leaf-on) and April 2018 (leaf-off). We utilized a 3D point-based classification to distinguish shrub genera based on their structural appearance (i.e., density, light penetration, and surface roughness).From the identified C. scoparius individuals, we related different volume metrics (mean, max, and range) to measured biomass and quantified spatial variation in biomass change from 2017 to 2018. We obtained overall classification accuracies above 86% from point clouds of both seasons. Maximum volume explained 77.4% of the variation in biomass.The spatial patterns revealed landscape-scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas. Further studies are needed to disentangle the causes of the observed decrease, for example, recent winter grazing and/or frost events. Synthesis and applications: We present a workflow for processing ultrahigh-density spatial data obtained from a UAS LiDAR system to detect change in C. scoparius . We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation of management activities initiated to prevent shrub encroachment.
Why it matches plant phenotyping methodsUAS LiDARによる植物群落・低木の3D構造とバイオマスを推定する処理ワークフローを開発・精度評価しており、植物形質取得が研究の中心である。
abstractWe investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh-density point cloud data (>1,000 pts/m 2 ).
Reproduction assets foundThe paper's UAS LiDAR point clouds, field presence-absence records, and harvested shrub biomass measurements are explicitly deposited in the Dryad Data Repository with a public DOI link, making the paper-specific phenotyping data directly actionable.Dataset · publicDATA AVAILABILITY STATEMENT
The data used in this study are available through the Dryad Data Repository: https://doi.org/10.5061/dryad.547d7wm55 .Open asset ↗Dryad Data Repository · 10.5061/dryad.547d7wm55lines:280-396Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Rapeseed is the second most important oil crop species and is widely cultivated worldwide. However, overcoming the 'phenotyping bottleneck' has remained a significant challenge. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In addition, it is important to explore the dynamic genetic architecture underlying rapeseed plant growth and its contribution to final yield. In this work, a high-throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons. We developed an automatic image analysis pipeline to quantify 43 dynamic traits across multiple developmental stages, with 12 time points. The time-resolved i-traits could be extracted to reflect shoot growth and predict the final yield of rapeseed. Broad phenotypic variation and high heritability were observed for these i-traits across all developmental stages. A total of 337 and 599 QTLs were identified, with 33.5% and 36.1% consistent QTLs for each trait across all 12 time points in the two growing seasons, respectively. Moreover, the QTLs responsible for yield indicators colocalized with those of final yield, potentially providing a new mechanism of yield regulation. Our results indicate that high-throughput phenotyping can provide novel insights into the dynamic genetic architecture of rapeseed growth and final yield, which would be useful for future genetic improvements in rapeseed.
Why it matches plant phenotyping methodsアブラナの高スループット表現型解析施設を用い、自動画像解析パイプラインを開発して43の動的形質を定量化しており、表現型取得・抽出手法が研究の中心である。
abstracta high-throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons.
Reproduction assets foundThe paper explicitly deposits its rapeseed phenotyping data (RGB images, genotypic and phenotypic i-trait data for both growing seasons) in the HZAU plant phenomics database, and its image analysis pipeline source code (LabVIEW, DLL, cpp, test images) on the first author's public GitHub repositories. Both are paper-‐‑Dataset · publice points (every ~7 days starting from 53 to 138 days after sowing). The trials were performed using a randomized block design with five replications in each growing season: 2015–2016 and 2016–2017. The screening generated a total of 1.62 terabytes of RGB images (16,986,93 images; PNG format), which are available in a database ( http://plantphenomics.hzau.edu.cn/search_rape.action , 2015‐2016‐QTL and 2016‐2017‐QTL). A movie of the growth of the recurrent parent and two select ISLs is shown in Movies [Link] , [Link] , [Link] . The inspected lines and inspection dates are shown in Table S1 , where T1‐T12 represent the twelve time points. In our greenhouse experiment, the final yield per plant wOpen asset ↗plantphenomics.hzau.edu.cnlines:165-171Code · publiceasons in this study are available at http://plantphenomics.hzau.edu.cn/search_rape.action under the sections 2015‐2016‐QTL and 2016‐2017‐QTL. The phenotypic data are also shown in Table S13 . All the source code, including that of LabVIEW programs, the dynamic link library, cpp documents and test images, can be downloaded from https://github.com/fenghuifh2006?tab=repositories .
Conflicts of interest
The authors declare that they have no conflicts of interest.
Author Contributions
H.L., H.F. and W.Y. performed the experiments, analysed the data and wrote the manuscript. C.G., S.Y., W.H., X.X., J.L. G.C. and Q.L. assisted in the data analysis and database information construction. W.Y., L.X. Open asset ↗github.com/fenghuifh2006lines:189-223Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.
Why it matches plant phenotyping methodsHTPプラットフォームによる時系列画像取得と機械学習解析が、放射線処理の成長表現型を定量化する中心的方法として明示され、別研究での検証も行われている。
abstractIrradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S3. Summary of all phenotyping data from preliminary, main, and validation studies.Open asset ↗lines:77-105Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
SUMMARY The broad host range necrotrophic fungus Sclerotinia sclerotiorum is a devastating pathogen of many oil and vegetable crops. Plant genes conferring complete resistance against S. sclerotiorum have not been reported. Instead, plant populations challenged by S. sclerotiorum exhibit a continuum of partial resistance designated as quantitative disease resistance (QDR). Because of their complex interplay and their small phenotypic effect, the functional characterization of QDR genes remains limited. How broad host range necrotrophic fungi manipulate plant programmed cell death is for instance largely unknown. Here, we designed a time‐resolved automated disease phenotyping pipeline enabling high‐throughput disease lesion measurement with high resolution, low footprint at low cost. We could accurately recover contrasted disease responses in several pathosystems using this system. We used our phenotyping pipeline to assess the kinetics of disease symptoms caused by seven S. sclerotiorum isolates on six A. thaliana natural accessions with unprecedented resolution. Large effect polymorphisms common to the most resistant A. thaliana accessions identified highly divergent alleles of the nucleotide‐binding site leucine‐rich repeat gene LAZ5 in the resistant accessions Rubezhnoe and Lip‐0. We show that impaired LAZ5 expression in laz5.1 mutant lines and in A. thaliana Rub natural accession correlate with enhanced QDR to S. sclerotiorum . These findings illustrate the value of time‐resolved image‐based phenotyping for unravelling the genetic bases of complex traits such as QDR. Our results suggest that S. sclerotiorum manipulates plant sphingolipid pathways guarded by LAZ5 to trigger programmed cell death and cause disease.
Why it matches plant phenotyping methods時間分解型の自動画像フェノタイピングパイプラインを設計し、病斑を高解像度・高スループットで測定して複数の病原系で検証・適用しており、表現型取得法が研究の中心である。
abstractwe designed a time‐resolved automated disease phenotyping pipeline enabling high‐throughput disease lesion measurement with high resolution, low footprint at low cost.
Reproduction assets foundThe paper's INFEST image-analysis pipeline (the computational core of the Navautron phenotyping system) is publicly available on GitHub, along with a tutorial repository containing example phenotyping pictures and grid layout files. No raw phenotype dataset from this study's Sclerotinia experiments appears to be postedCode · publice. Characteristic values are the duration of latency phase and the lesion doubling time (LDT). Data shown correspond to values collected on five leaves of A. thaliana Col‐0, the red curve shows fitted average.
Box 1
Installation guide for INFEST, the Navautron image analysis tools
A complete tutorial and updates can be found at https://github.com/A02l01/INFEST . Infest requires python and conda installed on your machine. Major steps of the installation procedure are:
1
Clone the infest repository
$ git clone https://github.com/A02l01/INFEST.git
2
Create and activate the INFEST conda environment using the yaml file
$ conda env create ‐n INFEST ‐f env_Infest.yml
3
Analyze pictures contOpen asset ↗A02l01/INFESTlines:36-86