Fruit quality is a critical determinant of economic returns in pear production, and maintaining an appropriate fruit load (FL) is essential for achieving high yield and quality. As a direct indicator of canopy photosynthetic capacity and assimilate supply, leaf number constitutes the key biological basis of reasonable FL determination under the leaf-to-fruit ratio concept. However, accurate and efficient estimation of leaf number in mature pear trees remains technically challenging, limiting its practical use in precision FL regulation. Here, we propose a data-driven framework for leaf number and reasonable FL estimation by integrating 3D point cloud-derived canopy structure with machine learning. A pipeline for extracting 3D architectural traits was developed and implemented in the software tool FTPCT, enabling rapid and standardized trait acquisition. Through correlation analysis, multicollinearity diagnosis, and variance inflation factor screening, five key traits strongly associated with leaf number were identified and incorporated into five machine learning models optimized using Bayesian optimization. Among them, the optimized random forest regression model achieved the highest and most stable performance, with R 2 of 0.85, RMSE of 239.74, and MAE of 149.26 for test dataset. SHAP analysis identified tree crown volume as the dominant contributor to leaf number estimation. Field validation demonstrated that FL regulation guided by the proposed framework significantly improved fruit weight and size without reducing yield compared with conventional practices. Notably, the proposed approach avoids explicit leaf-level reconstruction and relies on less canopy-scale traits, substantially reducing data requirements and computational cost, and thereby offering strong potential for rapid, field-deployable FL regulation in large-scale orchards.
Why it matches plant phenotyping methods3D点群から樹冠構造形質を抽出し、葉数と適正着果量を推定する手法およびソフトウェアを開発・検証しており、植物表現型取得が中心である。
abstractA pipeline for extracting 3D architectural traits was developed and implemented in the software tool FTPCT, enabling rapid and standardized trait acquisition.
Reproduction assets foundThe paper's phenotyping analysis assets are the authors' publicly released LeafNumPred source code and trained models, and the FTPCT software for 3D trait extraction from pear tree point clouds. Phenotype/point-cloud datasets are only available on request.Code · public. Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
mmc1.docx (1.6MB, docx)
Data availability
Data will be made available on request. Anyone who wants to obtain other public data can contact us at taost@njau.edu.cn. The source codes and models have been made publicly available at https://github.com/Zhang-Fanhang/LeafNumPred, and the FTPCT software has been released at https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package.
References
1.Tao S., Khanizadeh S., Zhang H., Zhang S. Anatomy, ultrastructure and lignin distribution of stone cells in two Pyrus species. Plant Sci. 2009;176:413–419. [Google Scholar]
2.Zhang F., Wang Q., Yuan K.Open asset ↗Zhang-Fanhang/LeafNumPredhtml-lines:284-315Code · publical variations [34,35]. The method for calculating these traits are shown in the Supplementary information 1.
2.5. Software implementation for 3D trait extraction (FTPCT)
To facilitate efficient and standardized extraction of canopy structural traits from point cloud data, we used a standalone software tool, FTPCT (available at: https://github.com/Zhang-Fanhang/FTPCT/tree/Installation-package), which integrates the trait extraction procedures applied in this study. The software provides a graphical user interface, enabling users to process tree-level point cloud data and extract key 3D structural traits without requiring advanced programming skills.
FTPCT implements a series of predefined proOpen asset ↗Zhang-Fanhang/FTPCThtml-lines:138-149Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Why it matches plant phenotyping methods植物表面の三次元形状を測定するオープンな構造化光プラットフォームと再構成・解析ワークフローを開発し、葉で投影バイアスを評価しており、表現型取得手法が中心である。
abstractHere we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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hereOpen asset ↗Zenodo · 10.5281/zenodo.22167598pdf-raw-page:35 lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
ABSTRACT Leaf shape is a fundamental trait of plant ecological strategies, influencing biotic interactions and ecosystem functioning. However, established quantitative metrics fail to capture subtle variations and irregularities, require user-based reference points or are challenging to compare among taxa with broadly different leaf shapes. In addition, established metrics typically conflate (aggregate) leaf edge complexity and macro-shape complexity, despite their independent functional significance and genetic foundations. Here, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity. Based on three case studies, we show that these metrics outperform aggregate metrics in predicting Quercus robur chemical traits, provide more intuitive interspecific classifications, and strongly align with human perception. In addition, edge and macro-shape complexity show high complementarity, while aggregate metrics are highly redundant and typically strongly related to leaf area. Emerging as the strongest predictor of leaf chemistry and key visual cue for complexity as perceived by humans, the effects of edge complexity highlight the under-appreciated functional significance of leaf margins. Our framework and the proposed entropy-based complexity metrics thus promise to help unlock the potential of growing digital image archives of leaves, including images from herbaria and fossils, and are technically readily applicable to shapes of algae, bacteria, pollen, and beyond. The accompanying package ShapeComplexity enables the broad application of entropy-based metrics, providing a powerful tool to explore how the shape of organisms and biological structures influences ecological strategies, biotic interactions, and ecosystem functioning while tracking spatial and temporal variation.
Why it matches plant phenotyping methods葉の画像からエッジ複雑性とマクロ形状複雑性を定量化する新規指標とソフトウェアを開発しており、植物形質抽出法が研究の中心である。
abstractHere, we introduce an entropy-based framework to quantify two new complexity metrics: edge complexity and macro-shape complexity.
Reproduction assets foundThe paper's authors publicly release their ShapeComplexity analysis code (Rust) on GitHub, used to compute the paper's leaf edge- and macro-shape complexity metrics. Supplementary data/analysis code are on Dryad, but that URL is not in the allowed list. RMBG is a generic third-party background-removal model, not a phenCode · publicThe complete, open-source Rust-code (The Rust Team, 2025 ) is publicly available on GitHub ( https://github.com/Thornbach/ShapeComplexity ), ensuring transparency and reproducibilityOpen asset ↗Thornbach/ShapeComplexitylines:86-94Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Accurate quantification of plant disease severity is essential for evaluating host-pathogen interactions and assessing the effectiveness of disease management strategies. Traditional visual scoring methods and manual estimation of infected tissue are widely used but are often subjective and prone to observer bias. Digital image analysis offers an objective alternative by enabling automated identification and quantification of symptomatic plant tissues based on color and spatial characteristics. Here, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images. The workflow involves acquisition of standardized leaf images, conversion of RGB images into hue-saturation-value (HSV) color space, segmentation of diseased tissue using defined HSV thresholds, refinement of the segmented mask through morphological operations, and extraction of the whole leaf area. The protocol then calculates the diseased area and total leaf area in pixels and computes the percentage of infected tissue. The method uses MATLAB together with the Image Processing Toolbox and can be implemented using simple scripts. This protocol enables rapid and reproducible quantification of disease severity in plant leaves exhibiting visually distinct symptoms such as necrotic lesions or blight patches. By minimizing observer bias and providing quantitative measurements of infected area, the protocol offers a practical and reproducible approach for plant disease phenotyping and evaluation of disease management strategies across diverse plant-pathogen systems where diseased tissues can be clearly distinguished from healthy tissues under reasonably controlled imaging conditions. Key features • A reproducible MATLAB-based workflow for separating diseased and healthy plant tissue using color-space segmentation. • Applicable to plant diseases where symptomatic tissue contrasts clearly with healthy tissue (necrosis, blight lesions, rot patches). • Requires digital leaf images, MATLAB, and the MATLAB Image Processing Toolbox for image processing and disease quantification. • Enables rapid calculation of diseased leaf area and disease severity using automated pixel-based quantification.
Why it matches plant phenotyping methods植物病斑を画像から分割・定量し、感染面積と病害重症度を算出するMATLAB画像解析プロトコルが研究の中心であり、植物病害表現型の取得・抽出手法に該当する。
abstractHere, we present a MATLAB-based image processing protocol for differentiating diseased and healthy plant tissue from digital leaf images.
Reproduction assets foundThe protocol explicitly deposits its authors' MATLAB image-processing workflow (HSV segmentation, mask refinement, pixel-based disease quantification) in a public GitHub repository with README instructions and example images.Code · publicGitHub repository containing the MATLAB source code, README file with installation and execution instructions, and representative example image(s): https://github.com/pankajborahmajuli-source/Leaf-Disease-Detection-MATLAB-Code/blob/main/README.mdOpen asset ↗Leaf-Disease-Detection-MATLAB-Codehtml-lines:112-148Code / dataset availability confirmedCrossref · checked 11 Sept 2026
Abstract Purpose Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024). Methods and Results Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R 2 = 0.81–0.95; RMSE = 0.27–0.77 m 2 /m 2 ), followed by CCC (R 2 = 0.58–0.94; RMSE 2 ), while LCC remained less accurately estimated (R 2 = 0.26–0.78; RMSE 2 ). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits. Conclusions The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.
Why it matches plant phenotyping methodsUAV・近接分光センサーとPROSAIL反転、ANNを用いてLAIや葉・群落クロロフィルを推定し、精度比較と圃場観測との整合性評価を行うことが研究の中心である。
abstractThis study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' UAV image processing code (irradiance normalization, vignetting, exposure compensation, radiometric calibration) in a public GitHub repository under GPL v3.0; other data are available only upon request.Code · publicData availability Code to perform irradiance normalization, vignetting, exposure compensation, and radio-
metric calibration is available at https://github.com/fieldSITES/scripts/tree/main/UAV under GNU General
Public License v3.0. Other data will be made available upon request.Open asset ↗UAVpdf-page:34 lines:1-40Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
TomatoLiDAR / point cloudLeafMorphology / geometry measurementSegmentationLeaf traits
Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best F 1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09 cm, 1.78 cm and 8.98 cm 2 respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.
Why it matches plant phenotyping methodsトマト葉の3D画像取得、器官セグメンテーション、葉インスタンス識別、形態形質推定を統合した自動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す。
abstractproposed a fully automatic pipeline for leaf phenotyping
Reproduction assets foundThe paper's tomato point cloud dataset (with semantic and leaf instance annotations used for the phenotyping pipeline) is publicly available on Kaggle via a footnote. NPEC website is a facility page, and Open3D is a generic library, so neither qualifies.Dataset · public2. ^ The dataset used in this study is available at: https://www.kaggle.com/datasets/xinbolai/vtc-tomatoOpen asset ↗Kaggle · xinbolai/vtc-tomatolines:545-624Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reliable traits are needed for identification of tea ( Camellia sinensis ) cultivars, yet the stability of leaf morphology and color across leaf positions remains unclear. This study evaluated inter-cultivar variation and positional stability in leaf morphological, RGB color, and SPAD traits in six predominant cultivars. One-year-old shoots were sampled in a completely randomized design, and five fully expanded leaves below the apical bud were analyzed. SPAD values were measured with a chlorophyll meter, and scanned images were used to extract contour and RGB traits. Data were analyzed using ANOVA, correlation analysis, PCA, and discriminant analysis. Leaf morphology differed among cultivars and leaf positions, with significant cultivar-by-position interactions; however, the width-to-length ratio differed among cultivars but remained stable across positions in these cultivars. SPAD values increased with leaf position and were strongly associated with RGB components, being negatively correlated with R and G and positively correlated with B. Morphological traits explained 52.988% of total variance in PCA and yielded 64.6% overall classification accuracy, with LaoHan showing the highest accuracy (83.3%). Misclassification was concentrated among genetically similar cultivars. These findings suggest that stable leaf shape proportions and SPAD-RGB relationships provide useful descriptors, whereas genetic relatedness limits morphology-based cultivar identification under the present conditions.
Why it matches plant phenotyping methods茶品種識別のため、葉の形態・RGB・SPAD特性の取得と安定性、分類性能を中心に評価しており、画像由来形質抽出を含む実質的な表現型解析である。
abstractscanned images were used to extract contour and RGB traits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/biology15151283/s1 , Table S1: Original data of leaf morphological traits, RGB values, and SPAD values from six tea cultivars in this study.Open asset ↗lines:368-409Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.
Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。
abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves
Reproduction assets foundThe paper's data availability statement explicitly makes the 3,454-image A. senegal leaf imaging dataset public on Zenodo and the ML implementation source code public on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe plant leaf imaging data used in the work is publicly available at https://doi.org/10.5281/zenodo.16531486.Open asset ↗zenodo · 10.5281/zenodo.16531486html-lines:480-497Code · publicThe source code of the implementation is available at https://github.com/softwareinnovationslabBITS/CDRF_ASenegal_MLImagingOpen asset ↗github · softwareinnovationslabBITS/CDRF_ASenegal_MLImaginghtml-lines:480-497Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Premise Accurate species identification is crucial for ecological restoration and can be especially challenging for understudied non-model species. Quercus garryana is the only native oak species in the Pacific Northwest and is an important component of the endangered oak savanna ecosystem. Quercus robur is an imported ornamental species from Europe and has been found to be mistakenly planted as Q. garryana in habitat restoration projects. Methods We measured leaf morphological traits sampled from herbarium collections in their native ranges using the digital morphometric tools MorphoLeaf and Tomato Analyzer. We then used Lasso logistic analysis to generate a predictive model and tested it on leaves from Portland, Oregon. To streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions. Results Garryanalyzer demonstrated 95% accuracy in predicting the species identity of herbarium specimens of oaks. Garryanalyzer correctly identified all Q. robur individuals sampled in Portland but showed lower accuracy for Q. garryana . Discussion Many existing morphometric software are not open source, which makes them unable to be customized to specific study systems. Garryanalyzer is built upon the widely used open-source ImageJ platform. This study also demonstrates a viable workflow for developing similar tools for other ecologically important non-model plant species.
Why it matches plant phenotyping methods葉の形態形質を自動測定し、種予測まで行うImageJプラグインとワークフローの開発・評価が中心であり、植物フェノタイピング手法として適格です。
abstractTo streamline this species detection process, we developed Garryanalyzer, an ImageJ plug-in that automatically measures leaf traits and outputs species predictions.
Reproduction assets foundThe paper's authors publicly released the Garryanalyzer ImageJ plug-in source code on GitHub, all original and modified leaf images used in the morphometric analyses on Zenodo, and the full leaf morphometric measurement dataset plus R Lasso analysis code in a second Zenodo repository. All are paper-specific, public,可直接Code · publicThe source code and installation instructions for Garryanalyzer can be accessed on GitHub at https://github.com/zxie8561/Garryanalyzer.Open asset ↗https://github.com/zxie8561/Garryanalyzer · zxie8561/Garryanalyzerhtml-lines:210-274Dataset · publicAll images used in the morphometric analyses, both original and modified, are available on Zenodo (https://doi.org/10.5281/zenodo.17462266).Open asset ↗https://doi.org/10.5281/zenodo.17462266 · 10.5281/zenodo.17462266html-lines:210-274Dataset · publicThe full dataset of leaf morphometric measurements of both GBIF and Portland samples, R code for Lasso analysis, and other miscellaneous files are available on a separate Zenodo repository (https://doi.org/10.5281/zenodo.17546152).Open asset ↗https://doi.org/10.5281/zenodo.17546152 · 10.5281/zenodo.17546152html-lines:210-274Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe 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: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R2 rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m2·m−2. Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval.
Why it matches plant phenotyping methodsLiDAR点群からトウモロコシのLAIを推定する手法を、3D放射伝達モデル、PointNet++、転移学習で開発・検証しており、植物形態形質の取得・推定が研究の中心です。
abstractthis work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques.
Reproduction assets foundThe paper's field-measured LiDAR point cloud and LAI data (Yingke Oasis and Huazhaizi sites) come from a publicly accessible TPDC dataset with an explicit URL in the Data Availability Statement. No author analysis code, trained models, or synthetic dataset deposit is stated.Dataset · public2024WX06.
Data Availability Statement: The dataset used in this study was obtained from the National Tibetan
Plateau Data Center (TPDC, https://www.tpdc.ac.cn/ (accessed on 6 September 2025)), a publicly
accessible scientific data platform providing multi-source geoscientific datasets. The specific dataset
can be accessed via: https://www.tpdc.ac.cn/zh-hans/data/4d60d570-0aa9-417b-8a9d-c32b73b564
(accessed on 6 September 2025). The TPDC database integrates long-term observational and remote
sensing data with standardized quality control, ensuring the reliability and consistency of the datasets
for scientific research.
Acknowledgments: The authors would like to acknowledge the National TibetaOpen asset ↗4d60d570-0aa9-417b-8a9d-c32b73b564pdf-raw-page:19 lines:1-51Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitively labor-intensive. We present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters, providing a more comprehensive representation of the plant’s architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we propose a method that generates token sequences containing a procedural definition of the plant architecture. This work uses only synthetic images for training and testing, where “exact” architectural parameters were known, which allowed for testing of the hypothesis that organ-level architectural parameters could be extracted from imagery data using a vision language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. Then, a VLM was trained to predict plant architecture token sequences from images. Our results demonstrate that the model can predict plant architecture tokens with an F1 score of 0.73 in a teacher-forcing method. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. Our model achieves lower MAPE than feature regression-based methods in estimating bulk plant-level traits that require understanding of the occluded 3D structure of the plant, such as leaf count and leaf area. We conclude that generating plant architecture and parameter extraction from synthetic imagery are feasible using a VLM approach, supporting future extension to real imagery.
Why it matches plant phenotyping methods画像から器官レベルの植物構造と形態形質を抽出するVLM手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractWe present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters
Reproduction assets foundThe paper's footnotes explicitly state that the authors' code is available on GitHub and the synthetic cowpea image/XML dataset is available on Hugging Face, both paper-specific and publicly actionable. The Helios URL is a generic third-party simulator library, not a paper-specific asset.Code · public1. ^ Code is available at: https://github.com/GEMINI-Breeding/Image2PlantArchitecture .Open asset ↗GEMINI-Breeding/Image2PlantArchitecturelines:600-676Dataset · public2. ^ Dataset is available at: https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML .Open asset ↗heesup/Cowpea-Architecture-XMLlines:600-676Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R2 = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.
Why it matches plant phenotyping methods植物形質を分光情報から推定し、ハイパースペクトル画像へ展開して可燃性関連の植物状態を評価する手法が研究の中心であり、モデル性能も検証しているため。
abstractSpectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific plant phenotype measurements: sampled species lists, fractional cover, and measured vegetation traits across dates and plots, plus combustion replicate variability and trait–flammability relationship data. The raw underlying data,谱Supplement · publicbroader environmental coverage, improved plant trait retrieval meth-
ods, and independent validation. Future work should also explore non-linear modelling
frameworks to better capture the complexity of vegetation flammability across ecosystems.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs18101546/s1, Supplementary Table S1 provides the list of
sampled plant species and their percentage cover across sites, paddocks, plots, and fuel types; Table
S2 presents the fractional cover of each species and litter component; Figure S1 shows the study-site
vegetation map; Figures S2–S6 show the measured vegetation traits acrosOpen asset ↗pdf-raw-page:22 lines:1-49Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafRootMorphology / geometry measurementLeaf traitsRoot system architecture
Premise Selective breeding over thousands of years has prioritized aboveground yield, with little regard for changes belowground. Roots underpin plant growth and resilience, but our knowledge of these critical structures lags behind that of aboveground structures. Accurately phenotyping root traits is labor-intensive, expensive, and often destructive. High-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs. Methods We used American licorice (Glycyrrhiza lepidota Pursh.), a perennial legume with a rich ethnobotanical history, as a model to investigate root system phenotypes. We assessed root traits across multiple populations, analyzed relationships between above- and belowground phenotypes, and tested the use of multidimensional leaf traits, including spectral reflectance, in predicting root traits. Results Root traits of American licorice varied significantly across source populations. Root traits were strongly intercorrelated and each root trait correlated with an aboveground phenotype. Leaf spectral reflectance and elemental composition predicted belowground traits; however, interpretation of some trait-specific signals were complicated by isometric scaling between plant size and root traits. Conclusions These findings demonstrate the use of high-dimensional leaf traits as a proxy for root traits, with potential applications for understanding foundational questions in plant biology and in breeding programs targeting belowground structures of perennial herbaceous species. Further optimization and larger studies are needed to improve predictive models.
Why it matches plant phenotyping methods葉の高次元形質とスペクトル反射を用いて、測定困難な根形質を非破壊・高スループットに推定する方法が研究の中心である。
abstractHigh-throughput, nondestructive methods are required to advance understanding of the fundamental biology of root systems and to integrate hard-to-measure root traits into breeding programs.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: raw root scans on Zenodo and a Figshare deposit containing RhizoVision Explorer output features, CropReporter data and metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses. NoDataset · publich Center Bioanalytical Chemistry Facility (RRID:SCR_001047). Finally, we thank the reviewers for their careful evaluation of our manuscript and constructive comments, which helped us clarify the conceptual framing and strengthen the overall quality of the work.
DATA AVAILABILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
KatOpen asset ↗Zenodo · 18852041lines:173-419Dataset · publicILITY STATEMENT
Raw root scans can be found on Zenodo ( https://zenodo.org/records/18852041 ). RhizoVision Explorer output features, CropReporter and associated metadata, spectral reflectance data, elemental composition data, and all R code needed to reproduce the analyses presented in this manuscript can be found on Figshare ( https://doi.org/10.6084/m9.figshare.28742870 ).
REFERENCES
Alahmad , S.
,
D.
Smith
,
C.
Katsikis
,
Z.
Aldiss
,
S. M.
Brunner
,
S. V.
Meer
,
L.
Meijer
, et al. 2025 .
Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field
. Journal of Experimental Botany
76 : 5161 ‐ 5178 .
40580084
10.1093/jxb/eraf268
PMC1Open asset ↗Figshare · 10.6084/m9.figshare.28742870lines:173-419Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Wood apple (Feronia limonia L.) is an underutilized perennial fruit tree with substantial ecological, nutritional, and economic potential, yet its phenotypic diversity and trait organization remain poorly characterized. Here, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors. Trait interrelationships were examined through the complementary use of Spearman’s rank correlation and Cramér’s V association analyses, capturing both directional rank-based dependencies and scale-independent categorical linkages. Hierarchical clustering based on Gower distance separated the genotypes into three distinct phenotypic clusters, with inter-cluster dissimilarities (0.92–1.18) consistently exceeding intra-cluster variation (0.42–0.55), indicating well-supported phenotypic stratification based on cluster validation. Multiple Correspondence Analysis (MCA) explained 23.30% of total inertia across the first two dimensions, with tree growth habit, branch angle, tree shape, and fruit color emerging as the principal drivers of phenotypic differentiation. Vegetative and leaf traits formed a tightly integrated module, whereas fruit-related traits displayed weaker monotonic but persistent categorical associations, reflecting partial phenotypic independence. The strong concordance among association analyses, clustering, and MCA indicates structured patterns of coordinated and partially independent trait associations in wood apple. Overall, this study demonstrates the effectiveness of mixed-scale multivariate approaches for resolving complex trait architecture in underutilized perennial fruit crops and provides a quantitative phenotypic framework to support germplasm conservation, parent selection, and ideotype-oriented improvement of wood apple.
Why it matches plant phenotyping methods混合尺度の多変量解析を用いて植物遺伝資源の表現型構造を定量化する手法が研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors.
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 data generated or analyzed during this study are available in the article and the accompanying Supplementary Table S1.Open asset ↗lines:137-161Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Automated quantification of plant-level development from multi-plant greenhouse scenes requires separating individual plants from shared scene-level reconstructions and quantifying organ-level development, a challenge that single-plant acquisition workflows do not directly address. This study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining. LCR-GS integrates zero-shot 2D cues with multi-view lifting, geometric clustering, and chromatic refinement to convert large scene-level reconstructions (~2M Gaussians) into compact per-plant subsets (~16K Gaussians). Experiments on greenhouse-grown muskmelon at the early vegetative stage demonstrate high plant-extraction precision (0.933) and strong organ-level instance segmentation (mean AP50 = 0.924). Plant height and leaf count are validated against manual measurements (height R² = 0.98, RMSE = 1.88 cm; leaf count R² = 0.86), whereas additional morphological traits, including leaf area, leaf area index, mean internode length, and stem node count, are reported as pipeline-derived descriptors for within-cohort comparison. By decoupling semantic inference from reconstruction, the pipeline reduces scene-scale data by over 99% and provides a practical route to derive compact per-plant 3D representations from multi-plant greenhouse imagery for downstream organ-level analysis.
Why it matches plant phenotyping methods3DGS画像から個体・器官を抽出し、植物形質を定量化するフェノタイピング手法の開発と検証が中心である。
abstractThis study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the muskmelon 3DGS phenotyping dataset (scenes, Gaussian-level plant/background annotations, and point-level organ labels) used in this study.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/bblabNTU/3dgs-muskmelon-phenotyping-dataset.Open asset ↗bblabNTU/3dgs-muskmelon-phenotyping-datasethtml-lines:485-547Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
LeafStem / branchPhysiological trait estimationLeaf traitsWater status / transpiration
Leaf and hydraulic traits are key determinants of growth rates, and hence potentially exhibit significant associations with wood density (WD) and its intraspecific variation (ITV). However, the extent to which functional traits could improve WD prediction accuracy, and how ITV in WD correlates with functional traits remain incompletely understood. We investigated WD and its ITV across 10,218 plant species, mapped the global distribution of WD, and analyzed the association of ITV in WD with niche breadth and functional traits. Plant species with an acquisitive resource-use strategy, characterized by higher specific leaf area (SLA), leaf nitrogen concentration (LN), and leaf maximum stomatal conductance (g max ), exhibited lower WD. Associations of WD with hydraulic traits indicated species with greater hydraulic safety exhibited higher WD. Moreover, the integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%. Furthermore, resource-acquisitive species demonstrated higher ITV for WD. ITV was positively related to relative niche breadth concerning both climatic factors and soil properties. Overall, functional traits significantly improve WD prediction accuracy, and plant species with an acquisitive resource-use strategy exhibit lower WD but greater intraspecific variation.
Why it matches plant phenotyping methods木材密度という植物形質の予測モデルを構築し、機能形質・環境因子の統合による予測精度を検証しており、形質推定手法が中心的です。
abstractthe integration of leaf traits (i.e., SLA and LN) and hydraulic traits with environmental factors substantially enhanced WD prediction accuracy in a random forest model, raising the explained variance from 55% to 95%.
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo deposit containing the authors' global wood density distribution data, which directly reproduces this paper's measurements. The TRY Plant Trait Database is a generic third-party database, not a paper-specific asset, and no author analysis code is stated.Dataset · publicData for the global distribution of wood density is available on Zenodo Repository https://sandbox.zenodo.org/records/425279.Open asset ↗Zenodo · 425279html-lines:405-429Code / dataset availability confirmedCrossref · checked 5 Sept 2026
Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.
Why it matches plant phenotyping methods植物形質をハイパースペクトル画像から推定する深層学習について、OOD条件での不確実性推定手法Dis_UNを開発・評価しており、表現型取得・推定手法が中心である。
abstractwe propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty
Reproduction assets foundThe paper's authors publicly released their uncertainty-analysis code (two GitHub repositories) and the study data (Hugging Face dataset) with explicit availability statements and URLs. The EnMAP and NEON hyperspectral scenes are third-party public data sources, not paper-specific deposits, and the supplement is not anCode · publicThe code for this study is available at: https://github.com/echerif18/Multi_trait_Uncertainty/ (last access: 8 March 2026).Open asset ↗echerif18/Multi_trait_Uncertaintylines:449-456Dataset · publicThe data used in this study are available on Hugging Face: https://doi.org/10.57967/hf/7838 (Cherif et al., 2026).Open asset ↗Hugging Face · 10.57967/hf/7838lines:457-483Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 5 Sept 2026
High-density planting is an effective strategy to increase maize yield but imposes greater demands on plant architectural adaptability. To elucidate the structural response mechanisms of maize under varying planting densities, we developed a high-throughput 3D phenotyping system tailored to complex field conditions. High-precision point clouds of field-sampled plants were obtained via multi-view 3D reconstruction. Using a deep learning network, stem and leaf organs were semantically segmented (95.6% accuracy), while leaves were individually separated via clustering (94.8% accuracy). From these data, 31 plant architectural traits and 14 ear-leaf traits were extracted, establishing a hierarchical trait characterization system. Results showed that increased planting density significantly influenced plant architecture reshaping and structural coordination, leading to more compact plant forms and ear height position centralization. Ear leaves exhibited heightened sensitivity to density variation, particularly in leaf area, vertical distribution, and leaf inclination angle, suggesting an early-response role. Principal component analysis and clustering further revealed patterns of structural differentiation and key traits driving these changes under density treatments. The integrated workflow-comprising data acquisition, modeling, segmentation, clustering, trait extraction, and analysis-offers a robust approach for structural phenotyping and intelligent breeding selection in maize and other tall crops. This pipeline provides valuable technical support and data resources for optimizing dense planting strategies and advancing digital agriculture.
Why it matches plant phenotyping methods高スループット3D表現型システムを開発し、点群再構成・器官分割・クラスタリングから多数の植物構造形質を抽出することが中心であるため。
abstractwe developed a high-throughput 3D phenotyping system tailored to complex field conditions
Reproduction assets foundThe paper's authors provide a public GitHub repository for the study's source code (segmentation/trait-extraction pipeline). The phenotype point-cloud dataset itself is only available on request from the corresponding author, so it is not a public asset.Code · publicThe code of this study will be made publicly available upon publication. The source code is available at https://github.com/CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responses .Open asset ↗CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responseslines:330-415Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Recognizing lineages is a central challenge in plant systematics, making it essential to explore multiple analytical tools. In this context, this study investigates how frond shape can assist in discriminating against lineages within the Scaly clade of Microgramma (Polypodiaceae), and tests whether the integration of multiple lines of evidence enables a more consistent recognition of lineages than exclusively macromorphological approaches. We analyzed 271 specimens representing eight species, using Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA). Evolutionary relationships between spectral and morphometric data were assessed through phylogenetic generalized least squares (PGLS) regressions and phylogenetic partial least squares (Phylo-PLS) analyses. Dimorphic species exhibited higher discrimination capacity (average accuracy of 80–83%). Fertile and combined fronds yielded the highest accuracy values. Morphologically similar species, such as M. reptans and M. tobagensis, showed significant overlap, whereas M. percussa achieved the best performance (average accuracy of 80%). Morphometric-spectral integration showed a strong correlation (R² = 0.72; P = 0.003), and both the combined datasets (spectra and outline) and the individual datasets of spectral and shape features revealed a high phylogenetic signal (λ = 1–0.84), indicating partial coevolution between frond shape, chemical composition, and the evolutionary history of the group. Outline morphometry combined with infrared spectroscopy within a phylogenetic framework improves lineage discrimination, although overlap zones persist, reflecting complex evolutionary processes. Our study highlights the potential of integrative systematics to elucidate species boundaries in groups with high morphological disparity, as well as the need for broad sampling and multi-evidence approaches in future systematic reviews.
Why it matches plant phenotyping methodsフロンド形状をElliptical Fourier Analysisで定量化し、赤外分光との統合を用いて系統識別性能を評価しており、植物器官形質の取得・解析手法が研究の中心です。
abstractusing Elliptical Fourier Analysis (EFA) to quantify frond shape, followed by multivariate statistical tests (PCA, MANOVA, LDA).
Reproduction assets foundThe authors state that raw data, processed data, and R analysis code for the frond outline morphometrics are publicly available on GitHub (Microgramma-Outline), and the FT-NIR spectral data repository (Microgramma-FTNIR) is referenced in the methods. Both are paper-specific, public, and actionable.Code · publicSciELO Preprints - Este documento é um preprint e sua situação atual está disponível em: https://doi.org/10.1590/SciELOPreprints.15500
573 The raw data, processed data, and R analysis code are publicly available on GitHub:
574 https://github.com/labevofern/Microgramma-Outline.git.
575
576 REFERENCES
577 Ackerly D.D. (2004) Adaptation, Niche Conservatism, and Convergence: Comparative
578 Studies of Leaf Evolution in the California Chaparral. The American Naturalist, 163, 654–
579 671.
580 Adams D.C., Collyer M.L. (2018) Multivariate Phylogenetic Comparative Methods:
581 Evaluations, Comparisons, and RecoOpen asset ↗labevofern/Microgramma-Outline · Microgramma-Outlinepdf-layout-page:25 lines:1-48Dataset · publicbiting the highest
157 perpendicular distance from the line connecting the first and last bands in the R² × ranking
158 plot (Fig. S2). Following the methods described in Mendonça et al. (2026), spectral data were
159 acquired using a PerkinElmer Frontier™ near-infrared Fourier transform spectrometer (FT-
160 NIR) available at (https://github.com/labevofern/Microgramma-FTNIR).
161 Phylogenetic comparative analyses
162 To provide a phylogenetic framework for comparative morphometric and spectral analyses,
163 we used the pruned version of the Microgramma chloroplast phylogenetic inference from
164 Mendonça et al. (2026). This tree was based on the Bayesian phylogenetic tree published by
165 AOpen asset ↗labevofern/Microgramma-FTNIR · Microgramma-FTNIRpdf-layout-page:8 lines:1-55Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Leaf morphology in tea plants (Camellia sinensis L.) profoundly influences tea quality and agronomic value, yet its genetic basis remains elusive due to labor-intensive phenotyping, foliage architecture, and ecological sensitivity of traits. Moreover, traditional methods forfeit quantitative color gradients and population-level morphological complexity. To address this challenge, we developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ measurements, while offering higher efficiency and color hues quantification. We then estimated 7 key morphological traits focusing on leaves from a collection of ~ 4,200 mature leaves and ~ 5,000 bud-leaf samples across 167 genetically diverse tea accessions by coleaf. While classical understanding suggests leaf shape differentiation between two varieties in genus sinensis assamica (CSA) and sinensis (CSS), our phenotypic clustering revealed incomplete congruence with phylogenetic relationships, suggesting the presence of additional genetic or environmental modulators beyond population divergence. Furthermore, we integrated phenotypic data with whole-genome resequencing for multi-model genome-wide association studies (GWAS). Candidate genes associated with leaf architecture were involved in plant development (e.g., CsFAS2), cell division and elongation (e.g., CsFIP1), and cellular morphogenesis (e.g., CsRLK), whereas those associated with leaf color, regulated pigment accumulation (e.g., ABC transporters, CsMYB113). In conclusion, this study establishes a standardized computational framework validating automated image recognition for plant leaf phenomics. The end-to-end framework from high-throughput phenotyping to gene discovery provides critical genetic targets for tea breeding, demonstrating transformative potential in accelerating the genetic improvement of tea plants.
Why it matches plant phenotyping methods茶葉形態の画像認識ソフトウェアを開発・検証し、高スループットな形質抽出フレームワークとして適用しており、植物フェノタイピング手法が研究の中心である。
abstractwe developed coleaf, an open-source image recognition-based software that demonstrated 97.6% accuracy over conventional ImageJ 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 · publicAll codes and tools used in this study are described in Methods, coleaf is available on github (https://github.com/mengmeng-jiang/coleaf).Open asset ↗mengmeng-jiang/coleafhtml-lines:390-460Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
None
1.
Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Rapid and accurate identification of DUS (Distinctness, Uniformity, and Stability) test traits in lettuce leaves is essential for advancing multi-omics-driven intelligent breeding. It also plays a critical role in germplasm protection and enhancing agricultural competitiveness. However, the phenotypic traits of lettuce leaves are highly diverse and complex due to both genotypic variation and environmental influences, posing significant challenges for precise DUS trait quantification. To address these challenges, we propose a high-precision phenotypic trait extraction pipeline and introduce an interpretable phenotypic fingerprinting framework for lettuce subgroup identification. First, a lightweight semantic segmentation network guided by group attention is developed to extract leaf components. Then, shape, color, and texture traits are comprehensively quantified. Following UPOV (International Union for the Protection of New Varieties of Plants) guidelines, we establish quantitative methods for seven DUS test traits: leaf shape, leaf tip shape, leaf margin shape, leaf vein shape, color hue, brightness, and anthocyanin coloration. Finally, PCA (Principal component analysis) was used to select 13 key traits, capturing over 95.82% of the total variance, for constructing "phenotypic ID" of lettuce varieties. Experiments conducted on 709 lettuce leaf image datasets showed that the accuracy of subgroup identification based on phenotypic fingerprints reached 98.59%. This study offers a scalable approach for automated DUS test trait evaluation and intelligent crop variety identification, providing a novel paradigm with strong potential for application in precision breeding and germplasm resource management.
Why it matches plant phenotyping methodsレタス葉画像からDUS形質を抽出・定量化する画像解析パイプラインを開発し、709画像で評価しており、植物フェノタイピング手法が研究の中心である。
abstractwe propose a high-precision phenotypic trait extraction pipeline and introduce an interpretable phenotypic fingerprinting framework for lettuce subgroup identification.
Reproduction assets foundThe article provides a public GitHub repository containing the authors' source code for the lettuce phenotypic fingerprint pipeline. The 709-image dataset and annotations are only available upon request, so they do not qualify as public assets.Code · publicThe data used to support the findings of this study are available upon request from the corresponding author, and the source code is accessible at https://github.com/qiuguangjie87/PP_Phenotypic_Fingerprint .Open asset ↗PP_Phenotypic_Fingerprintlines:263-278Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Modeling plant growth dynamics plays a central role in modern agricultural research. However, learning robust predictors from multi-view plant imagery remains challenging due to strong viewpoint redundancy and viewpoint-dependent appearance changes. We propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings. Our method aggregates rotational views into angle-invariant representations and conditions visual features on lightweight text priors encoding viewpoint level for stable prediction under incomplete or unordered inputs. On the GroMo25 benchmark, our approach reduces mean age MAE from 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The models and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Why it matches plant phenotyping methods植物画像から葉数・植物齢を推定するマルチビュー表現学習手法を開発し、ベンチマークで性能評価しており、表現型取得・推定が研究の中心である。
abstractWe propose a level-aware vision language framework that jointly predicts plant age and leaf count using a single multi-task model built on CLIP embeddings.
Reproduction assets foundThe paper explicitly states that the model and code are publicly available at the authors' GitHub repository, which qualifies as a paper-specific public code asset.Code · publicm 7.74 to 3.91 and mean leaf-count MAE from 5.52 to 3.08 compared to the GroMo baseline, corresponding to improvements of 49.5% and 44.2%, respectively. The unified formulation simplifies the pipeline by replacing the conventional dual-model setup while improving robustness to missing views. The modela and code is available at: https://github.com/SimonWarmers/CLIP-MVP
Index Terms:
Plant phenotyping, Multi-view learning, Multi-task regression, Precision agriculture
† † address: † Computer Vision Lab, CAIDAS, IFI, University of Würzburg, Germany
‡ Technological University Dublin, Ireland
1 Introduction
Plant phenotyping from multiview imagery is crucial for precision agriculture, enabling non-Open asset ↗SimonWarmers/CLIP-MVPlines:1-53Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / 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.
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It includes TLS point clouds collected at multiple stages and muOpen asset ↗Zenodo · 10.5281/zenodo.16751663lines:1-50Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
1 Abstract Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.
Why it matches plant phenotyping methods高解像度画像とフォーカスブラケティングを用いて植物病害の重症度を定量化し、圃場プロット単位の推定精度と不確実性を評価する手法が研究の中心であるため。
abstractWe combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area.
Reproduction assets foundThe paper states that R code to reproduce the full analysis (Beta-distribution modeling, autocorrelation/AR(1) mixed models, effective sample size estimation for wheat disease severity phenotyping) is publicly available on the authors' GitHub repository. The repository name appears truncated in the supplied text ('plotCode · publicR-code to reproduce the full analysis is available at https://github.com/and-jonas/plot-spot-Open asset ↗and-jonas/plot-spot-pdf-page:9 lines:1-61Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use‐case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methodsPlantCV v4は、画像から植物形質を自動抽出するオープンソースソフトウェアの開発・機能拡張・比較評価を主題としており、植物フェノタイピング手法が中心である。
abstractPlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' analysis scripts on GitHub (danforthcenter/plantcv-4-paper), which directly reproduces this paper's phenotyping analyses.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-97Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Code / dataset availability confirmedCrossref · checked 14 Sept 2026
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
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 · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトルとガス交換・光合成形質を標準化して収録するデータベースを構築し、植物フェノタイピングモデルの開発・検証に供することが中心である。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper describes the GSTI database of paired leaf hyperspectral and gas-exchange measurements, with both the data and R processing/model-fitting code publicly available on GitHub and archived releases on ESS-DIVE.Code · publicThe GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti (last access: 4 January 2026)Open asset ↗https://github.com/plantphys/gstilines:537-549Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Abstract Phenotyping is crucial for understanding crop trait variation and advancing research, but is currently limited by expensive, labor-intensive monitoring. New phenotypic trait monitoring methods are being proposed to reduce this so-called phenotyping bottleneck via automation. These methods are often data-driven, requiring a dataset recorded with a specific sensor and corresponding reference values for developing novel methods. To this end, we present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset, which contains field data from various sensors collected over a growing season, covering six crop species. All data was georeferenced for alignment across sensors and dates. To collect our dataset, we deployed aerial and ground robotic platforms equipped with RGB cameras, LiDARs, and multispectral cameras, aiming to capture a wide variety of modalities and observations from different viewpoints. In addition to sensor data, we also provide manually collected leaf area index and biomass reference measurements. Our dataset enables the development of novel automatic phenotypic trait estimation methods, allows comparisons across different sensors, and generalizability across crop species.
Why it matches plant phenotyping methods複数センサー・ロボットプラットフォームによる圃場フェノタイピング用データセットを構築・提供し、形質推定法の開発、センサー比較、汎化評価を可能にすることが中心的な貢献である。
abstractwe present the MuST-C (Multi-Sensor, multi-Temporal, multiple Crops) dataset
Reproduction assets foundThe paper's MuST-C multi-sensor, multi-temporal crop phenotyping dataset (RGB/multispectral images, LiDAR point clouds, LAI and biomass reference measurements) is publicly available via the authors' project webpage, and the authors' custom Python processing/loading code is publicly available on GitHub.Dataset · publicThe MuST-C dataset is available via our project webpage https://www.ipb.uni-bonn.de/data/MuST-C/or directly via the bonndata public access repository 10.60507/FK2/OX9XTM34Open asset ↗html-lines:421-440Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate and efficient leaf trait measurement is essential for plant phenotyping, agronomy, and ecological studies. In this work, we introduce Leaf Analyzer, a novel open-source, fully automated computer vision-based tool for high-throughput leaf morphological trait measurement such as leaf area, dimensions, perimeter, count, and percent damage. Unlike existing methods that rely on strong foreground-background contrast or controlled imaging conditions, Leaf Analyzer employs an unsupervised clustering approach based on the K-means++ clustering algorithm and a novel Leaf Background Separation (LBS) feature, which combines the L∗ and b∗ channels from CIEL∗a∗b∗ color space and the saturation channel from HSV color space. The proposed method and the LBS feature can effectively distinguish leaves from the background across varying lighting conditions, leaf colors, and camera orientations. To evaluate the performance of the new software, we conducted comprehensive quantitative and qualitative comparison experiments with two widely used software tools - Petiole Pro and LeafByte, demonstrating that Leaf Analyzer achieves superior accuracy and consistency, particularly under challenging imaging conditions. Additionally, we explore methods to further enhance measurement precision, including leaf flattening and the integration of supplementary leaf features such as texture features and color specific features. Beyond leaf trait measurement, we showcase the versatility of Leaf Analyzer in a range of applications, including nondestructive plant phenotyping, seed counting, root trait analysis, leaf area measurement for petri dish-grown plants, plant projected silhouette area or crown projection area estimation, leaf damage assessment, and broader plant science applications, making it a valuable tool for researchers working in laboratory and field environments.
Why it matches plant phenotyping methods葉形態形質を自動抽出するオープンソース画像解析ツールの開発と、既存ツールとの定量比較検証が研究の中心であるため。
abstractIn this work, we introduce Leaf Analyzer, a novel open-source, fully automated computer vision-based tool for high-throughput leaf morphological trait measurement such as leaf area, dimensions, perimeter, count, and percent damage.
Reproduction assets foundThe authors state that the Leaf Analyzer source code, installer files, and all data (including evaluation images) used in this study are publicly available on their GitHub repository.Code · publicThe Leaf Analyzer source code, platform-specific installer files, and all data used in this study are publicly available on our GitHub repository at https://github.com/squashking/Leaf-Analyzer .Open asset ↗squashking/Leaf-Analyzerlines:239-277Dataset · publicAll the images used in the evaluation have been published on our Github repository ( https://github.com/squashking/Leaf-Analyzer ).Open asset ↗squashking/Leaf-Analyzerlines:134-155Code / dataset availability confirmedOpenAlex · bioRxiv · Crossref · checked 14 Sept 2026
Abstract Leaf appearance is a crucial plant phenotype. However, traditional methods for extracting this information are inefficient, limiting its full utilization. Deep learning based on convolutional neural networks (CNNs) enables us to capture previously inaccessible information from images. In this study, we made the surprising discovery that the leaf appearance of each individual plant is unique. Using deep learning, leaves from one plant could be efficiently distinguished from those of another plant of the same species and cultivar. We term this phenomenon the “ Plant Face ” and suggest the potential to develop a “plant face recognition system,” analogous to human facial recognition. We also applied similar methods to study the relationship between leaflet appearance and their position on compound leaves, leaf bilateral symmetry, and differences in leaves from twining stems with different chirality. These results collectively indicate that plant genetic characteristics, growth conditions, and developmental features can be stored within their appearance. With appropriate decoding, leaf appearance is poised to play an increasingly important role in phenomics. Significance The saying “no two leaves in the world are identical” holds philosophical significance, as such variation encompasses considerable contingency and randomness. Here, we assert that no two trees have identical leaves ; meaning that even for plants of the same species and cultivar, the leaf morphology of each individual plant is distinct at the population level, even though single leaves may overlap in appearance. Genetic, environmental, and developmental information is recorded in some manner within the phenotypic appearance of leaves. With advancements in computational technologies like artificial intelligence, this information can now be decoded. Highlights The leaves of each individual plant are statistically unique. The relationship between leaflet appearances in compound leaves hints at their developmental patterns. Leaves are not necessarily bilaterally symmetric in a statistical sense. Leaves from stems with different chirality (twining direction) exhibit distinct appearances.
Why it matches plant phenotyping methods葉画像から植物の個体差や形態情報を深層学習で抽出・識別する手法が研究の中心であり、植物フェノタイピングへの応用を明示している。
abstractLeaf appearance is a crucial plant phenotype. However, traditional methods for extracting this information are inefficient
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 are available at git-hub.Open asset ↗pdf-page:13 lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Code / dataset availability 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 · OpenAlex · Europe PMC · checked 6 Sept 2026
RiceWheatField / plotMesh / voxelNeRF / 3D Gaussian SplattingLiDAR / point cloudPanicle / ear / spikeLeafRootSeed / grain
Advanced plant phenotyping technologies are vital for trait improvement and accelerating intelligent breeding. Due to the species diversity of plants, existing methods heavily rely on large-scale high-precision manually annotated data. For self-occluded objects at the grain level, unsupervised methods often prove ineffective. This study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method. It utilizes radiance field information to lift 2D masks, segmented by SAM2 (Segment Anything Model 2), into 3D space for target point cloud extraction. A multi-target collaborative optimization strategy addresses the challenge of segmenting multiple targets from a single interaction. On a rice dataset, IPENS achieves a grain-level segmentation mean Intersection over Union (mIoU) of 63.72%. For phenotypic trait estimation, it achieves a grain voxel volume coefficient of determination R 2 = 0.7697 (Root Mean Square Error, RMSE = 0.0025), leaf surface area R 2 = 0.84 (RMSE = 18.93), and leaf length and width prediction accuracies of R 2 = 0.97 and R 2 = 0.87 (RMSE = 1.49 and 0.21). On a wheat dataset, IPENS further improves segmentation performance to a mIoU of 89.68%, with exceptional phenotypic estimation results: panicle voxel volume R 2 = 0.9956 (RMSE = 0.0055), leaf surface area R 2 = 1.00 (RMSE = 0.67), and leaf length and width predictions reaching R 2 = 0.99 and R 2 = 0.92 (RMSE = 0.23 and 0.15). Without requiring annotated data, IPENS rapidly extracts grain-level point clouds for multiple targets within three minutes using single-round image interactions. These features make IPENS a high-quality, non-invasive phenotypic extraction solution for rice and wheat, offering significant potential to enhance intelligent breeding.
Why it matches plant phenotyping methods植物形質抽出のためのNeRF-SAM2融合手法を開発し、作物データセットで分割性能と形質推定精度を検証しているため、方法開発・検証が中心である。
abstractThis study proposes IPENS, an interactive unsupervised multi-target point cloud extraction method.
Reproduction assets foundThe paper's analysis code is publicly available on GitHub. The rice/wheat MMR/MMW phenotype datasets (multi-view images, point clouds, annotations) are only available upon reasonable request, so they are not public.Code · publicCode is available at https://github.com/Vincent-Songwentao/IPENS-Code.git .Open asset ↗https://github.com/Vincent-Songwentao/IPENS-Code.gitlines:472-496Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
In modern agricultural production, accurate monitoring of maize growth and leaf counting is crucial for precision management and crop breeding optimization. Current UAV-based methods for detecting maize seedlings and leaves often face challenges in achieving high accuracy due to issues such as low spatial-resolution, complex field environments, variations in plant scale and orientation. To address these challenges, this study develops an integrated detection and visualization software, DP-MaizeTrack, which incorporates the DP-YOLOv8 model based on YOLOv8. The DP-YOLOv8 model integrates three key improvements. The Multi-Scale Feature Enhancement (MSFE) module improves detection accuracy across different scales. The Optimized Spatial Pyramid Pooling-Fast (OSPPF) module enhances feature extraction in diverse field conditions. Experimental results in single-plant detection show that the DP-YOLOv8 model outperforms the baseline YOLOv8 with improvements of 3.9% in Precision (95.1%), 4.1% in Recall (91.5%), and 4.0% in mAP50 (94.9%). The software also demonstrates good accuracy in the visualization results for single-plant and leaf detection tasks. Furthermore, DP-MaizeTrack not only automates the detection process but also integrates agricultural analysis tools, including region segmentation and data statistics, to support precision agricultural management and leaf-age analysis. The source code and models are available at https://github.com/clhclhc/project.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ個体数と葉数を抽出するソフトウェアを開発しており、植物形質取得が研究の中心です。
abstractthis study develops an integrated detection and visualization software, DP-MaizeTrack
Reproduction assets foundThe paper explicitly states that the authors' source code and trained models for DP-MaizeTrack/DP-YOLOv8 are publicly available on GitHub. No public dataset deposit is stated; the UAV image dataset is described but not declared publicly available.Code · publicThe source code and models are available at https://github.com/clhclhc/project .Open asset ↗https://github.com/clhclhc/projectlines:224-300Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLeafMorphology / geometry measurementLeaf traits
We present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species representing two distinct leafing phenologies and three growth forms from the central Western Ghats of India. Quantitative assessments were conducted for nine key traits: leaf area (LA), mean thickness (LTH), specific leaf area (SLA), leaf dry matter content (LDMC), leaf tissue density (LTD), leaf nitrogen concentration (Leaf N), carbon-to-nitrogen ratio (C/N), and phytolith yield, following standard protocols. For each species, 30 leaves were sampled from a minimum of five individuals, totalling 2790 leaf samples. Qualitative traits, including leaf shape, margin, surface, texture, apex, base, type, and latex presence, were recorded in the field and validated using field manuals. The majority of species sampled were evergreen (74 %), with deciduous species comprising the remainder. Given the growing importance of plant functional traits in ecological research, this dataset offers valuable species-level leaf trait information at the regional scale. The phytolith yield data, in particular, represent one of the few globally available datasets, providing essential baselines for palaeoecological research and enabling quantitative reconstruction of vegetation composition and environmental change over millennial timescales.
Why it matches plant phenotyping methods植物の葉形質を標準化プロトコルで体系的に収集した再利用可能なデータセット論文であり、データセット自体が中心的な成果である。
abstractWe present a comprehensive dataset of qualitative and quantitative leaf functional traits for 93 dominant woody species
Reproduction assets foundThe paper's own leaf functional trait dataset (2790 leaves, 93 woody species, central Western Ghats) is publicly deposited on Zenodo with an explicit DOI/URL given in the article.Dataset · publicduals per species. Quantitative leaf functional traits were analyzed following the standard protocol [ 1 , 2 ].
Data source location
Country: India
Sampling site: Gerusoppa Reserve Forest, Central Western Ghats (14°12′ N to 14°24′ N and 74°36′ E to 74°48′ E)
Data accessibility
Repository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.16717435
Direct URL to data: https://doi.org/10.5281/zenodo.16717435
Related research article
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Value of the Data
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This dataset provides high-resolution leaf-level data ( n = 2790) on 17 functional traits for 93 dominant woody species of the central Western Ghats, supporting trait-based ecological research.
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It enables assessmentOpen asset ↗Zenodo · 10.5281/zenodo.16717435lines:1-54Code / dataset availability confirmedCrossref · checked 6 Sept 2026
ABSTRACT As an essential species across European forests, Scots pine ( Pinus sylvestris L.) plays a vital ecological and economic role, yet its physiological variability underlying its adaptive potential remains underexplored. Understanding this intraspecific variability is crucial for uncovering the genetic basis of adaptation. Traditional genetic evaluations require large sample sizes and are time‐consuming, whereas hyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals, facilitating more efficient exploration of adaptive variation. We assessed needle functional traits (NFTs) linked to foliar structure, water content, and pigment composition in clonal seed orchards over two seasons, integrating hyperspectral measurements at needle and canopy levels with genotyping using a new 50 K single‐nucleotide polymorphism (SNP) array. Linear mixed models revealed substantial genetic variation, with the carotenoid‐to‐total‐chlorophyll ratio showing the highest heritability (0.29) among pigment traits, and structural/water‐related traits reaching heritability values up to 0.38. Significant genetic correlations were observed between stress‐related traits (pigment content, equivalent water thickness) and reflectance, suggesting that spectral traits could serve as proxies for indirect selection of adaptive traits or in breeding programs. Low genotype‐by‐environment interaction and stable clonal performance across years further underscore the reliability of these traits for identifying resilient genotypes. Overall, our findings highlight hyperspectral phenotyping and NFTs as promising tools for accelerating climate‐adaptive breeding in Scots pine.
Why it matches plant phenotyping methods針葉および林冠レベルのハイパースペクトル測定を用いて植物の機能形質を評価し、育種への再利用可能性を検討しており、フェノタイピング手法の適用が中心的です。
abstracthyperspectral sensing/imaging enables rapid, nondestructive assessment of physiological traits across many individuals
Reproduction assets foundThe paper's Data Availability Statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.27134907.v2) containing the data supporting the study's hyperspectral phenotyping and genetic analyses. This URL is in the allowed list and the identifier occurs verbatim in the quote. No separate author analysis code, Dataset · publicThe data supporting the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.27134907.v2 .Open asset ↗Figshare · 10.6084/m9.figshare.27134907.v2lines:454-598Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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 6 Sept 2026
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy ( R2>0.95 ) for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R 2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.
Why it matches plant phenotyping methods葉の光学特性と表現型形質を取得・予測する測定機器、BRDF解析ソフトウェア、機械学習モデルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractthe ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits.
Reproduction assets foundThe paper's BRDF analysis code (adaptive grid search fitting and Roughness Calculator) is publicly available at github.com/PlantSystemsBiology/brdf, and the modified fastTracer ray tracing software used for canopy light simulations is at github.com/PlantSystemsBiology/fastTracerPublic. Phenotype/measurement data are '…Code · publicAn adaptive grid search algorithm was developed in this study, and this algorithm utilized a 2-layered grid (step sizes of 1 × 10 − 2 and 1 × 10 − 4 respectively) structure to incrementally optimize each parameter, providing a more precise approximation of true values. By iteratively narrowing the search range and increasing resolution, this method gradually converges on the optimal solution. The source code of Python for adaptive grid search algorithm was available at https://github.com/PlantSystemsBiology/brdf .Open asset ↗PlantSystemsBiology/brdflines:212-227Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Introduction: Leaf phenotypes are key indicators of plant growth status. Existing deep learning-based leaf skeletonization typically requires extensive manual labeling, long training, and predefined keypoints, which limits scalability. We developed a training-free and label-free approach that connects spontaneously detected keypoints to generate leaf skeletons for leafy plants. Methods: The method comprises random seed-point generation and adaptive keypoint connection. For plants with random leaf morphology, we determine a threshold for the angle difference among any three consecutive adjacent points and iteratively identify keypoints within circular search neighborhoods to trace leaf skeletons. For plants with regular leaf morphology, we fit the skeleton trajectory by minimizing curvature. We validated the approach on vertical and front-view images of orchids (covering random and regular morphological cases) and extracted five phenotypic parameters from the resulting skeletons. Generalization was further assessed on a maize image dataset. Results: On orchid images, the proposed approach achieved an average curvature fitting error of 0.12 and an average leaf recall of 92%. Five orchid phenotypic parameters were accurately derived from the skeletons. The method also showed effective skeletonization on maize, indicating cross-species applicability. Discussion: By eliminating manual labels and training, this approach reduces annotation effort and computational overhead while enabling precise geometric phenotype calculation from skeleton-based keypoints. Its effectiveness on both randomly distributed and regularly shaped leafy plants suggests suitability for high-throughput plant phenotyping workflows.
Why it matches plant phenotyping methods葉の骨格化とフェノタイプ抽出のための画像解析手法を開発し、複数植物種で検証しているため、植物フェノタイピング手法が中心的である。
abstractWe developed a training-free and label-free approach that connects spontaneously detected keypoints to generate leaf skeletons for leafy plants.
Reproduction assets foundThe paper uses a publicly available single-plant maize image dataset (hosted on datasetninja) as its generalization-test input; the orchid images and the authors' algorithm code have no stated public deposit in the supplied blocks.Dataset · publicUsing the publicly available single-plant maize dataset (Dataset URL: https://datasetninja.com/maize-whole-plant-image-dataset ), which captured images of a single maize plant over 113 days—with 1 vertical view and 12 front view images taken each day—the spontaneous keypoints connection algorithm was applied to both a front view ( Figure 9 ) and a vertical view image ( Figure 9 ) from 10 different daysOpen asset ↗datasetninja · maize-whole-plant-image-datasetlines:429-438Code / 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 confirmedEurope PMC · checked 6 Sept 2026
Rapid prediction and control of flowering time is essential for breeding crops resilient to changing climates. Current models often fail to predict flowering time in new cultivars because molecular models lack integration of environmental signals, while physiological models inadequately capture the interactions of vernalization, photoperiod and temperature. This leads to mischaracterized genotypes and inaccurate forecasts. A new Cereal Anthesis Molecular Phenology (CAMP) model was developed for wheat. It explicitly integrates the regulatory roles of three major 'virtual' flowering genes (Vrn1, Vrn2, and Vrn3) with environmental cues. A novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration. CAMP predicted flowering time within 4-7 d across 64 genetically diverse wheat cultivars grown under contrasting environments. The leaf-number phenotyping method reduced phenotyping time by more than 80%, offering a practical alternative to resource-intensive field trials. Together, these advances enable accurate cultivar characterization and scalable prediction of flowering behaviour. CAMP enables the ability to predict flowering time directly from genotypic data (e.g. SNPs), eliminating the need for costly controlled-environment experiments. This represents a step change in molecular-physiological modelling, supporting faster deployment of new cultivars and more effective design of wheat for future climates.
Why it matches plant phenotyping methods主茎葉数に基づく新規フェノタイピング手法とCAMPモデルを開発し、多様なコムギ品種・環境で開花期予測を検証している。表現型取得の効率化が中心的貢献である。
abstractA novel phenotyping strategy based on main stem leaf number was introduced to shorten the time required for data collection and model calibration.
Reproduction assets foundThe paper's Data availability statement explicitly provides public repositories containing the CAMP model source code and analysis scripts used for the phenotyping data analysis and flowering-time prediction: the APSIM Next Generation framework repository, the standalone Python CAMP model and analysis scripts, and theCCode · publicAll the data and the source code of the model are freely accessible for research use through the APSIM General Use License at: https://github.com/apsimInitiative/apsimxOpen asset ↗apsimInitiative/apsimxlines:295-475Code · publicPython code and analysis scripts can be found at https://github.com/HamishBrownPFR/CAMPOpen asset ↗HamishBrownPFR/CAMPlines:295-475Code · publicC# implementation is available at https://github.com/APSIMInitiative/ApsimX/tree/master/Models/PMF/Phenology/CAMPOpen asset ↗APSIMInitiative/ApsimXlines:295-475Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 15 Sept 2026
Understanding how plant populations respond to environmental variation through functional leaf traits remains challenging due to limitations of traditional phenotyping approaches. Hyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation and detect signatures of local adaptation across populations. We combined hyperspectral data, inverse modeling, and network analysis to investigate population-level variation in Streptanthus tortuosus. Using a common garden experiment with four geographically distinct populations, we applied partial least square discriminant analysis (PLS-DA) and ridge regression for population discrimination, inverse PROSPECT modeling to estimate leaf biochemical traits, and canonical correlation analysis to examine trait-climate relationships across historical (1900-1994) and recent (1995-2024) periods. We developed a spectral network approach treating wavelength correlations as biologically meaningful trait networks. Populations showed distinct, heritable spectral signatures with high classification accuracy. Significant population differences emerged in anthocyanins, carotenoids, chlorophyll, and water content. Trait-climate correlations shifted between time periods, consistent with historical climate adaptation. Network analysis revealed population-specific integration patterns, with more variable environments displaying greater spectral modularity. Hyperspectral signatures provide a high-throughput tool for detecting population-level adaptation and trait coordination. Our findings provide a framework to investigate how plant populations respond to climate change through evolved shifts in trait networks rather than isolated traits alone.
Why it matches plant phenotyping methodsハイパースペクトル計測と逆モデリングを用いて葉の機能形質を推定し、集団間比較・適応評価を行う手法が研究の中心であるため。
abstractHyperspectral reflectance offers a rapid, non-destructive and high-throughput method to capture functional trait variation
Reproduction assets foundThe paper's Data availability statement explicitly deposits raw hyperspectral data and source code in a public GitHub repository, which is an allowed URL.Code · publicRR, JL; Formal Analysis:
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RR, JNM, TSM; Funding acquisition: JRG, JNM, TSM; Investigation: RR, JNM, TSM;
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Raw data and source code are available in the following Github repository.
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https://github.com/rishavray/spectral-network
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References
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Albert R, Barabási A-L. 2002. Statistical mechanics of complex networks. Reviews of Modern
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Physics 74: 47–97.
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Anderson JT, DeMarche ML, Denney DA, Breckheimer I, Santangelo J, Wadgymar SM.
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2025. Adaptation and gene flow are insufficient to rescue a montane plant under climate change.
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ScieOpen asset ↗rishavray/spectral-networkpdf-raw-page:23 lines:1-60Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Understanding how plants capture light and maintain their energy balance is crucial for predicting how ecosystems respond to environmental changes. By monitoring leaf inclination angle distributions (LIADs), we can gain insights into plant behaviour that directly influences ecosystem functioning. LIADs affect radiative transfer processes and reflectance signals, which are essential components of satellite-based vegetation monitoring. Despite their importance, scalable methods for continuously observing these dynamics across different plant species throughout day-night cycles are limited. We present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery. We compiled a dataset of over 4,500 images across 200 globally distributed species to facilitate generalization across taxa. Moreover, we developed a method to simulate pseudo-NIR imagery from RGB imagery to enable an efficient training of a deep learning model for tracking LIADs across day and night. The model is based on a vision transformer architecture with mixed-modality training using the RGB and the synthetic NIR images. AngleCam V2 achieved substantial improvements in generalization compared to AngleCam V1 (R 2 = 0.62 vs 0.12 on the same holdout dataset). Phylogenetic analysis across 100 genera revealed no systematic taxonomic bias in prediction errors. Testing against leaf angle dynamics obtained from multitemporal terrestrial laser scanning demonstrated the reliable tracking of diurnal leaf movements (R 2 = 0.61-0.75) and the successful detection of water limitation-induced changes over a 14-day monitoring period. This method enables continuous monitoring of leaf angle dynamics using conventional cameras, enabling applications in ecosystem monitoring networks, plant stress detection, interpreting satellite vegetation signals, and citizen science platforms for global-scale understanding of plant structural responses.
Why it matches plant phenotyping methods葉の傾斜角分布という植物形質を画像から推定する深層学習手法を開発し、大規模データセット、既存モデル比較、レーザースキャンによる検証、水ストレス下での追跡評価まで実施しており、フェノタイピング手法が研究の中心です。
abstractWe present AngleCam V2, a deep learning model that estimates LIADs from both RGB and near-infrared (NIR) night-vision imagery.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides public access to the authors' analysis code (Anonymous GitHub), the phenotyping image/trait dataset (Zenodo), and the pretrained AngleCam V2 model weights (Zenodo). All three are paper-specific, public, and actionable.Code · publicLK and TK conceived the ideas, designed the methodology, and led the analysis. TK, JP, RR, JF, LK,
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
Conflicts of Interest
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All authors declare that they have no conflicts of interest.
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perpetuity. It is made available underOpen asset ↗anonymous.4open.science/r/AngleCamV2-2B38pdf-raw-page:2 lines:1-30Dataset · publicK, JP, RR, JF, LK,
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at (https://doi.org/10.5281/zenodo.17101166).32
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All authors declare that they have no conflicts of interest.
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preprint (which was not certified by peer review) is the author/funder, whoOpen asset ↗zenodo · 10.5281/zenodo.17086253pdf-raw-page:2 lines:1-30Model / weights · publicanuscript. All authors contributed
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Data Availability Statement
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The code is available here (https://anonymous.4open.science/r/AngleCamV2-2B38). The data
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is available at (https://doi.org/10.5281/zenodo.17086253). The pretrained model is available
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at (https://doi.org/10.5281/zenodo.17101166).32
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All authors declare that they have no conflicts of interest.
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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 tOpen asset ↗zenodo · 10.5281/zenodo.17101166pdf-raw-page:2 lines:1-30Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone varieties is an essential alternative to traditional defoliant spraying. Nevertheless, it is hampered by low throughput and artificial error in manual field surveys. In this study, a framework for collecting high-throughput defoliation data in large fields was established. Three spectral indices (MTCI, VDVI, CI) and leaf area index (LAI) were first screened as core predictors through hierarchical segmentation analysis in three levels: leaf number (LN), leaf number difference (LND), and defoliation rate (DR). Four deep learning architectures (CNN, BiGRU, CNN-BiGRU, and CNN-BiGRU-Attention) were developed, and the CNN-BiGRU-Attention hybrid model demonstrated superior performance at all three levels, with R 2 values exceeding 0.85. Importantly, the inversion accuracy of this model at the LN and LND levels was superior to that at the DR level, which was also confirmed by the results of the genome-wide association study (GWAS). We combined GWAS and transcriptome results to identify a new gene, GhDR_UAV1 , associated with defoliation traits. The overexpression of GhDR_UAV1 significantly promoted the wilting of cotton leaves, indicating that GhDR_UAV1 plays a positive regulatory role in cotton defoliation. This study proposed a strategy to invert cotton defoliation data at three levels using deep learning fusion of UAV remote sensing data and LAI data and confirmed that LND can provide accurate phenotypic data for GWAS analysis. This study provides a new theoretical basis for cotton defoliation regulation and genetic improvement by integrating cotton high-throughput defoliation phenomics and genomics from an innovative perspective.
Why it matches plant phenotyping methodsUAVリモートセンシング、LAI、深層学習を統合し、ワタの落葉形質を高スループット推定する方法を開発・評価しており、表現型取得が研究の中心である。
abstracta framework for collecting high-throughput defoliation data in large fields was established
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's data and code (UAV/LAI defoliation phenotyping data and analysis code) in a public GitHub repository, which matches an allowed URL.Code · publicThe data and code utilized in this study are available at GitHub ( https://github.com/xbw322/Data_upload.git ).Open asset ↗https://github.com/xbw322/Data_upload.gitlines:169-205Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Quantifying crop responses to increasing temperatures is critical for predicting the productivity and sustainability of agricultural systems under environmental change. Physiological trait data associated with maximum Rubisco carboxylation ( V cmax ) and maximum electron transport ( J max ) rates are especially important predictors of crop response to elevated temperatures. However, when generating V cmax and J max data, steady-state methods of gas exchange measurements are time-consuming; thus, non-steady-state methods have been developed to obtain these measurements faster, prospectively allowing for trait data collection of considerably more varieties of crops. Globally important and geographically widespread vineyards are of particular interest due to the high economic value and the susceptibility of these managed systems to climate warming, especially in Canada, where the annual rate of warming far exceeds global averages. In this study, we examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes. Specifically, we measured V cmax and J max (alongside leaf nitrogen [N] concentrations and leaf mass per unit area [LMA]) across seven of the world's most common wine grape ( Vitis vinifera L.) varieties, namely, Cabernet franc, Cabernet sauvignon, Merlot, Pinot noir, Riesling, Sauvignon blanc, and Viognier. Our results show that V cmax and J max estimates derived from the DAT were strongly correlated to those obtained through the steady-state method ( r 2 = 0.748 and 0.908, respectively), and J max did not differ significantly between the two methods. Additionally, leaf N explained 43%-46% and 56%-58% of the variation in V cmax and J max , respectively, across both methods. Our results suggest that the DAT represents a viable tool for rapidly estimating intraspecific variation in important physiological traits and allows for increased replication and the inclusion of additional varieties when evaluating the responses of wine grape and other crops to climate warming.
Why it matches plant phenotyping methodsワインブドウの生理形質を高速取得する動的同化技術（DAT）を定常法と比較検証しており、植物表現型の測定法が中心的である。
abstractwe examined the efficacy of the high-throughput, non-steady-state dynamic assimilation technique (DAT) for obtaining V cmax and J max data from wine grapes.
Reproduction assets foundThe paper's physiological trait data (Vcmax, Jmax, leaf N, LMA for seven wine grape varieties) are openly deposited in the University of Toronto Borealis Dataverse, per the Data Availability Statement. No author analysis code or trained models are reported.Dataset · publicThe data that support the findings of this study are openly available in the Borealis Repository—University of Toronto Dataverse at https://doi.org/10.5683/SP3/URPVFF .Open asset ↗Borealis Repository—University of Toronto Dataverse · 10.5683/SP3/URPVFFlines:277-347Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
WheatGrowth chamberLeafPhysiological trait estimationGrowth / development / phenologyLeaf traits
Summary Disentangling genotype × environment (G×E) effects is critical to understand the performance of wheat across different environments. A framework for doing this was previously presented in a model that integrated knowledge of crop physiology and the Vrn gene feedback loop to explain and predict the time of anthesis. The aims of this study were: 1) provide an updated description of the Cereal Anthesis Molecular Phenology (CAMP) model; 2) to verify the model’s assumptions regarding the relationship between Vrn gene expression and the timing of phenological stages in a set of diverse genotypes and environments; 3) to use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation. Six wheat genotypes with a range of cool temperature and photoperiod sensitivities were evaluated. Apical development, final leaf number (FLN) and temporal expression of Vrn1, Vrn2 and Vrn3 were compared with model predictions. There was a clear relationship between FLN responses to cool temperature and photoperiod, the timing of phenological events and the patterns of Vrn gene expression for all genotypes. There was general agreement between the temporal patterns of foliar gene expression observed with those assumed by CAMP, but some obvious discrepancies. These may be related to differences between gene expression in foliar (observed) and apical (assumed by the model) parts of the plant, or differences in the way observed and modelled gene expression are scaled. Overall, the model described all the observed development responses to environment and provides a basis for building quantitative predictions of field-based development from genotypic and environmental data. A protocol is presented for phenotyping wheat using FLN measured in specific combinations of temperature and photoperiod. It allows easy and unconfounded measure of key developmental phenotypes that clearly relate to the genetic make-up of the plants and underlying gene expression profiles.
Why it matches plant phenotyping methodsCAMPモデルの更新・検証と、FLNを用いた小麦発育形質のフェノタイピングプロトコル提示が研究の中心であり、単なる生物学的測定ではない。
abstractto use the CAMP model to establish a phenotyping strategy for use in genetic studies and model parameterisation.
Reproduction assets foundThe paper's CAMP model code, analysis scripts, and data are explicitly stated as publicly available on the authors' GitHub repository, with specific URLs for the model notebook and the test/plotting script.Code · publicwere also validated and the best-performing sets selected. A
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description of each of the primers used in this study is given in the supplementary material
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(Table SA1).
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2.9 Verification of CAMP predictions
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2.9.1 Model set-up and operation.
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The CAMP model was coded into a Python script which is available at
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https://github.com/HamishBrownPFR/CAMP/blob/master/CAMP.ipynb. A formal
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description of the code and parameterisation scheme is given in the supplementary material.
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The FLN developmental phenotypes measured for each genotype (Section 3.1) were used to
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derive the Vrn expression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP wOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicpression parameters needed for CAMP. Each of the treatments was
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simulated using CAMP with its corresponding daily temperature and Pp, so its predictions of
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Vrn gene expression could be compared with those observed. The script running the CAMP
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code and producing the graphs displayed in this paper can be viewed at
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https://github.com/HamishBrownPFR/CAMP/blob/master/Tests/CAMPCETests.py.360
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CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted September 12, 2025.
;
https://doiOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:14 lines:1-70Code · publicnd testing of the model in
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broader contexts. EW contributed substantially to the improvement of model concepts and the
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manuscript and all authors provided final checking.
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8. Data Availability
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All the data and scripts used to analyse data and produce graphs as well as CAMP model code are
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publicly available at https://github.com/HamishBrownPFR/CAMP/694
9. References
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Allard V, Otto V, Bela K, Rousset M, Le Gouis J, Martre P. 2012. The quantitative
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response of wheat vernalization to environmental variables indicates that vernalization is not
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a response to cold temperature. Journal of Experimental Botany 63: 847–857.
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Baumont M, Parent B, Manceau L, Brown HE, DOpen asset ↗https://github.com/HamishBrownPFR/CAMP/pdf-raw-page:31 lines:1-68Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The vertical distribution of leaves plays a crucial role in the growth process of maize. Understanding the vertical spectral characteristics of maize leaves is crucial for monitoring their growth. However, accurate estimation of the vertical distribution of leaf area remains a significant challenge in practical investigations. To address this, we used a 3D RTM to simulate the layered canopy spectra of maize, revealing the impact of canopy structure on remote sensing penetration depth across different growth stages and planting densities. The results of this study revealed differences in detection depth across growth stages. During the early growth stage, the depth was concentrated in the bottom 1 to 3 leaves of the canopy, reaching 1 to 4 leaves at the ear stage and 1 to 7 leaves during the grain-filling stage. The planting density had a notable effect on the detection depth at the bottom of the canopy. Moreover, compared with the other spectral bands, the near-infrared spectral range exhibited greater sensitivity to density variations. In terms of LAI inversion, a FuseBell-Hybrid model was constructed. We analyzed VIs across different planting density and canopy structural scenarios and found that compared with lower layers, increased density reduced the relative change rate in the upper leaf layers. The sensitivity patterns differed between plant architectures: VIred exhibited density-dependent sensitivity, with distinct responses between plant types, and MTVI2 demonstrated optimal performance for mid-canopy monitoring. This study highlights the influence of the heterogeneous structural characteristics of maize canopies on remote sensing detection depth during different phenological stages, providing theoretical support for enhancing multilayer crop monitoring in precision agriculture.
Why it matches plant phenotyping methods分光計測と3D放射伝達モデルを用いてトウモロコシ冠層の検出深度およびLAI推定法を構築・評価しており、植物形質取得手法が研究の中心である。
abstractTo address this, we used a 3D RTM to simulate the layered canopy spectra of maize
Reproduction assets foundThe paper states its analysis code was uploaded to a public GitHub repository, which qualifies as an authors' public code asset for the LAI phenotyping analysis. No separate phenotype dataset or model checkpoint deposit is explicitly stated.Code · publicData availability
The code have been uploaded to Github: https://github.com/aaawitch/code .Open asset ↗https://github.com/aaawitch/codelines:290-311Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Leaf area is a critical trait in plant physiology and agronomy, yet conventional measurement approaches such as those using ImageJ remain labor-intensive, user-dependent, and difficult to scale for high-throughput phenotyping. To address these limitations, we developed a fully automated, open-source Python tool for quantifying citrus leaf area from scanned images using multi-mask HSV segmentation, contour-hierarchy filtering, and batch calibration. The tool was validated against ImageJ across 11 citrus cultivars (n = 412 leaves), representing a broad range of leaf sizes and morphologies. Agreement between methods was near perfect, with correlation coefficients exceeding 0.997, mean bias within ±0.14 cm2, and error rates below 2.5%. Bland–Altman analysis confirmed narrow limits of agreement (±0.3 cm2) while scatter plots showed robust performance across both small and large leaves. Importantly, the Python tool successfully handled challenging imaging conditions, including low-contrast leaves and edge-aligned specimens, where ImageJ required manual intervention. Processing efficiency was markedly improved, with the full dataset analyzed in 7 s compared with over 3 h using ImageJ, representing a >1600-fold speed increase. By eliminating manual thresholding and reducing user variability, this tool provides a reliable, efficient, and accessible framework for high-throughput leaf area quantification, advancing reproducibility and scalability in digital phenotyping.
Why it matches plant phenotyping methods柑橘葉面積の画像ベース測定ツールを開発し、ImageJとの比較検証と高スループット性能評価を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe developed a fully automated, open-source Python tool for quantifying citrus leaf area from scanned images using multi-mask HSV segmentation, contour-hierarchy filtering, and batch calibration.
Reproduction assets foundThe paper's authors publicly released the Python leaf-area analysis tool (source code and documentation) on GitHub with an archived citable version on Zenodo, as stated in the Data Availability Statement.Code · publich received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The Python-based tool created in this study for automated leaf area
analysis, along with its source code and documentation, is publicly available on GitHub and Zenodo
at: https://github.com/esuarez-12/Leaf-Area-Analyzer, accessed on 26 August 2025, and a perma-
nent, citable version of the tool, corresponding to version v1.0.0, has been archived on Zenodo with
the following DOI: https://doi.org/10.5281/zenodo.16951132. These materials are openly accessible
and provided under an open-source license to support reproducibility and furtherOpen asset ↗esuarez-12/Leaf-Area-Analyzer · Leaf-Area-Analyzerpdf-raw-page:16 lines:1-45Code · publicmated leaf area
analysis, along with its source code and documentation, is publicly available on GitHub and Zenodo
at: https://github.com/esuarez-12/Leaf-Area-Analyzer, accessed on 26 August 2025, and a perma-
nent, citable version of the tool, corresponding to version v1.0.0, has been archived on Zenodo with
the following DOI: https://doi.org/10.5281/zenodo.16951132. These materials are openly accessible
and provided under an open-source license to support reproducibility and further research.
Acknowledgments: The authors would like to thank Jake Price and the UGA Cooperative Extension
Lowndes County Office for the use of their citrus trees. The UGA Citrus Lab is committed to
advancing citOpen asset ↗10.5281/zenodo.16951132pdf-raw-page:16 lines:1-45Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Field / plotMultispectral / hyperspectralLeafSegmentationLeaf traits
Abstract Accurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture, as it directly impacts yield estimation, disease monitoring, and weed management. Conventional Convolutional Neural Networks (CNNs), such as UNet and its variants, often struggle with capturing long range contextual dependencies and preserving fine structural boundaries, while pure transformer based architectures like the Vision Transformer (ViT) suffer from poor inductive bias and limited data efficiency. To overcome these challenges , we propose a SegFormer inspired model that integrates Edge Gated Multi Head Spectral Attention (EG MHSA) for robust leaf area segmentation. The spectral attention mechanism captures discriminative frequency domain representations across spectral bands, while the edge gating module enhances boundary preservation by adaptively fusing multiscale edge features. Evaluated on the benchmark CWFID dataset, the proposed model achieves superior performance with an F1score of 97.33%, IoU of 95.84%, and the lowest loss of 0.0395, outperforming UNet variants and transformer based baselines. Qualitative analysis further demonstrates its effectiveness in accurately delineating fine leaf boundaries under complex field conditions. The ablation results highlight the complementary contributions of spectral attention and edge gating in boosting segmentation performance. With its lightweight architecture, edge focused refinement, and strong generalization capability, the proposed approach sets a new benchmark for leaf area segmentation and provides a practical, scalable solution for agricultural applications.
Why it matches plant phenotyping methods葉面積の画像セグメンテーション手法を開発・ベンチマーク評価しており、植物フェノタイピングにおける形態形質抽出が中心である。
abstractAccurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture
Reproduction assets foundThe paper evaluates its leaf area segmentation model on the public CWFID dataset (60 field images with pixel-level annotations), and the authors explicitly state the datasets are publicly available at the cwfid GitHub repository. No author analysis code or trained model checkpoints are reported.Dataset · publicThe datasets used in the study are publicly available in the repository: https://github.com/cwfid/Open asset ↗https://github.com/cwfid/pdf-page:22 lines:1-27Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Published8 Aug 2025Journal of Telecommunications and Information TechnologyCited by 2 · OpenAlex ↗
Accurate segmentation of leaf regions plays a vital role in plant phenotyping and agricultural analysis. This paper presents AKDUNet, a lightweight UNet-based architecture that integrates attention gates and knowledge distillation to improve segmentation performance while minimizing computational complexity. The architecture replaces traditional skip connections with attention gates to focus on salient spatial features and employs a two-stage training pipeline, where a compact student model learns from a deeper teacher model using a tailored distillation loss function. AKDUNet is evaluated on two benchmark datasets (CWFID and Sunflower) and outperforms a range of state-of-the-art models, including UNet++, Inception UNet, VGG-based UNets, SDUNet, INSCA UNet, and SegFormer. Ablation studies confirm the advantages of attention modules, and qualitative analyses using Grad-CAM visualizations reveal the model's ability to effectively focus on crucial leaf structures. The results demonstrate that AKDUNet is not only computationally efficient but also highly accurate, making it suitable for real-time deployment in resource-constrained agricultural environments.
Why it matches plant phenotyping methods植物の葉領域を抽出する画像セグメンテーション手法の開発とベンチマーク評価が中心であり、葉面積などの表現型取得に直接利用できる。
abstractAccurate segmentation of leaf regions plays a vital role in plant phenotyping and agricultural analysis.
Reproduction assets foundThe paper evaluates AKDUNet leaf segmentation on the public CWFID dataset (and a Sunflower dataset), and the acknowledgments explicitly state the datasets are publicly available at the authors' cited repository URL. No author analysis code or trained model checkpoints are released.Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提示し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を直接支援するため、方法論が中心である。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicIn addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUROpen asset ↗WUR-ABE/TomatoWURlines:1-45Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice.
Why it matches plant phenotyping methodsイネ分げつの3D再構成とScaleCalculatorによるスケール校正を開発し、7つの形態形質を抽出するフェノタイピング手法が研究の中心であるため。
abstracta 3D model reconstruction method was developed specifically for rice tillers
Reproduction assets foundThe paper's 3D tiller models for 231 rice landraces are publicly deposited on Zenodo, and the authors' ScaleCalculator phenotyping source code is publicly available on GitHub, both explicitly stated in the Data Availability Statement. SNP genotype data are unpublished and excluded.Code · publicvelopment Co. LTD, and
Jiangsu Collaborative Innovation Center for Modern Crop Production.
Data Availability Statement: The 3D tiller models created in this study are available for research pur-
poses at https://zenodo.org/records/16080993 (accessed on 18 July 2025).The source code of ScaleCal-
culator is available on GitHub at https://github.com/ganlab/OSTRA/tree/master/ScaleCalculator
(accessed on 18 July 2025).
Acknowledgments: We thank Jianmin Wan for their valuable suggestions and Jiaqi Deng for their
technical help.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this article.
References
1. Food and Agriculture OrganizatOpen asset ↗github · ganlab/OSTRApdf-raw-page:16 lines:1-50Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The digitization of vast herbarium collections has made millions of plant specimen images freely available online, which can now be used to generate phenotypic datasets of unprecedented scope. Here, we assess the potential of computer vision tools to automate the extraction of predicted leaf mass per area (LMA pred ) from digitized herbarium specimens. We use an automated pipeline to extract leaf area and petiole width from 22 680 leaves, representing a phylogenetic informed sample of 1580 species of woody angiosperms. LMA pred is estimated using a proxy equation that models the scaling relationship between petiole width and leaf mass. We assess potential sources of error in LMA pred estimates and evaluate whether documented LMA-climate patterns are recovered using this dataset and phylogenetic comparative methods. Our LMA pred dataset responds mainly to temperature and solar radiation and presents a positive correlation with latitude. The proxy equation, not the automated pipeline, is responsible for most of the error in LMA pred estimates. Our pipeline underscores the power of combining herbarium digitization with new techniques for automated trait scoring. The increased size of datasets generated using this tool allows investigation of potential LMA-climate relationships with a geographically balanced sample while also utilizing comprehensive phylogenetic information.
Why it matches plant phenotyping methodsデジタル標本画像から葉面積・葉柄幅を自動抽出し、LMAを推定するコンピュータビジョン・パイプラインが中心であり、植物形質データセットの生成と誤差評価も行っている。
abstractHere, we assess the potential of computer vision tools to automate the extraction of predicted leaf mass per area (LMA pred ) from digitized herbarium specimens.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits all data and code (the leaf_vision pipeline for automated LMA extraction from herbarium images) at the authors' public GitHub repository, which is an allowed URL. Supporting Information also contains the full LM2 measurement results (Table S2).Code · publicAll data and code used here are available on https://github.com/tncvasconcelos/leaf_vision and in the Supporting Information . GBIF DOI is available in the reference list.Open asset ↗tncvasconcelos/leaf_visionlines:358-360Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Today, leaf trait estimation remains a labor-intensive process. The effort to obtain ground truth measurements limits how accurately this task can be performed automatically. Traditionally, plant scientists manually measure the traits of harvested leaves and associate them with sensor data, which is key for training machine learning approaches and to automate the processes. In this paper, we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping. We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton, which is automatically extracted. We use the generated leaves to fine-tune different leaf trait estimation methods. We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software. Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves. Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves' traits, making our data crucial for developing and testing data-driven trait estimation methods. Accurate trait estimation is key to understanding crop growth, productivity, and pest resistance, as leaf size directly influences photosynthesis, yield potential, and vulnerability to insects and fungal growth.
Why it matches plant phenotyping methods葉の形質推定を支援するため、形質付き合成3D点群を生成するニューラルネットワーク手法とデータセットを開発・評価しており、フェノタイピング手法が中心である。
abstractwe propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.
Reproduction assets foundThe paper uses two public 3D plant point-cloud datasets (Pheno4D and BonnBeetClouds3D) as real-world inputs for training/evaluating its leaf trait estimation and generation pipeline; both have explicit public URLs. The authors' code is only promised ('We plan to make our code publicly available'), so it is not yet an aDataset · publicWe use two publicly available datasets. Pheno4D [43] is available at the url: https://www.ipb.uni-bonn.de/data/pheno4d/index.html. It contains maize and tomato plants measured daily, over 12 and 20 days respectively.Open asset ↗Pheno4Dhtml-lines:401-424Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Plant phenotyping has become increasingly important for advancing plant science, agriculture, and biotechnology. Classic manual methods are labor-intensive and time-consuming, while existing computational tools often require advanced coding skills, high-performance hardware, or PC-based environments, making them inaccessible to non-experts, to resource-constrained users, and to field technicians. RESULTS: To respond to these challenges, we introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping. The platform is designed for ease of use, enabling users to phenotype plant traits quickly and efficiently with only a smartphone at hand. We currently instantiate the use of the platform with tools such as SeedPheno, WheatHeadPheno, LeafAnglePheno, SpikeletPheno, CanopyPheno, TomatoPheno, and CornPheno; each offering specific functionalities such as seed size and count analysis, wheat head detection, leaf angle measurement, spikelet counting, canopy structure analysis, and tomato fruit measurement. In particular, OpenPheno allows developers to contribute new algorithmic tools, further expanding its capabilities to continuously facilitate the plant phenotyping community. CONCLUSIONS: By leveraging cloud computing and a widely accessible interface, OpenPheno democratizes plant phenotyping, making advanced tools available to a broader audience, including plant scientists, breeders, and even amateurs. It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. Its integration with smartphones also positions OpenPheno as a powerful tool in the growing field of mobile-based agricultural technologies, paving the way for more efficient, scalable, and accessible agricultural research and breeding.
Why it matches plant phenotyping methodsスマートフォンで植物形質を取得・解析するソフトウェアプラットフォームの開発が中心であり、複数の具体的な表現型解析ツールを提供している。
abstractwe introduce OpenPheno, an open-access, user-friendly, and smartphone-based platform encapsulated within a WeChat Mini-Program for instant plant phenotyping.
Reproduction assets foundThe paper's authors publicly release the OpenPheno platform code (GitHub repository) and the evaluation sample data used for algorithm validation and demonstration (dataset subdirectory). Both are paper-specific, public, and actionable.Dataset · publicEvaluation sample data used for algorithm validation and demonstration has been made publicly available at out GitHub repository: https://github.com/openpheno/OpenPheno/tree/main/dataset .Open asset ↗openpheno/OpenPheno · tree/main/datasetlines:171-191Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralLeafVisualization / data managementLeaf traitsPhotosynthesis / fluorescence
Abstract. Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.
Why it matches plant phenotyping methods葉のハイパースペクトル計測とガス交換による光合成形質を結合したデータベースで、植物形質推定モデルの開発・検証を主目的とするため、フェノタイピング手法・データセットとして中心的です。
abstractHere we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems.
Reproduction assets foundThe paper's paired leaf spectroscopy–trait database and its R processing/fitting workflow are explicitly released in a public GitHub repository, with published versions archived on ESS-DIVE.Dataset · publicts of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maintain data quality, contributions should adhere to the standards and guidelines outlined in this paOpen asset ↗ESS-DIVE · doi:10.15485/2530733pdf-raw-page:22 lines:1-36Code · publicgoing refinement of spectra-trait models as new datasets are
incorporated. Future developments of the GSTI will focus on expanding data coverage, incorporating data from under-
represented biomes and plant functional types.
6. Data and code availability
495
The GSTI data and code are available in the public GitHub repository at https://github.com/plantphys/gsti, and published
versions of GSTI are released to ESS-Dive (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025).
7. How to contribute to future versions of the GSTI
We encourage the community to contribute new datasets to expand the scope and utility of the GSTI project. To ensure
consistency and maiOpen asset ↗GitHubpdf-raw-page:22 lines:1-36Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R 2 and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R 2 and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.
Why it matches plant phenotyping methods作物生育情報を定量化するUAVマルチスペクトルセンサー、補正法、処理システムを開発し、LAIと地上部バイオマス推定を検証しており、植物フェノタイピング手法が中心である。
abstractThis paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS)
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe figures, tables and data mentioned in the article can be downloaded from https://github.com/ikjkj2/Plant-Phenomics .Open asset ↗ikjkj2/Plant-Phenomicslines:395-413Code / dataset availability 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 · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.
Why it matches plant phenotyping methodsRGB-D画像からレタスの構造・体積・葉密度を抽出し、生体重推定を検証する手法開発が研究の中心であり、データセットも構築している。
abstractIn this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce.
Reproduction assets foundThe paper's own lettuce 3D point cloud dataset (Pii, 402 point clouds with fresh weight references) is deposited on Zenodo, and the vacuum-package surface reconstruction code plus data processing scripts are publicly available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicData used in this study and developed models are available on Zenodo storage service https://zenodo.org/records/8410252 .Open asset ↗Zenodo · 8410252lines:158-220Code · publicThe code used at this study is available at https://github.com/VicB18/LettuceFW (accessed on 1 November 2024).Open asset ↗GitHub · VicB18/LettuceFWlines:158-220Code · publicThe code for the vacuum package method, along with the data processing scripts used in this study, are available in the Supplementary Information.Open asset ↗lines:98-114Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
1
Value of the Data
•
The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
•
Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
4
Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Code / 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 · Europe PMC · checked 6 Sept 2026
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness.
Why it matches plant phenotyping methodsブドウ葉の毛密度という形態形質を画像とCNNで自動定量する高スループット手法を開発し、専門家評価および大規模集団で検証しており、表現型取得・抽出法が研究の中心である。
abstractTherefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN).
Reproduction assets foundThe authors publicly released the ResNet CNN training code, the leaf disc image datasets, and the full leaf hair quantification pipeline in a GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicAll datasets and the code to train the CNNs are available in the GitHub repository.Open asset ↗lines:91-99Dataset · publicThe script of the ResNet CNN along with the images are available in the GitHub repository: https://github.com/1708nagarjun/ResNet-CNN-Leaf-hair.Open asset ↗1708nagarjun/ResNet-CNN-Leaf-hairlines:143-183Code / 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 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 confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R2 of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.
Why it matches plant phenotyping methodsRGB・深度画像を融合した深層学習によるレタス形質推定手法を開発し、交差検証で性能評価しており、フェノタイピング手法が中心である。
abstracta deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately
Reproduction assets foundThe paper's RGB-D lettuce images and trait measurements come from the publicly available Third Autonomous Greenhouse Challenge dataset deposited at 4TU.ResearchData, with an explicit availability statement and URL matching an allowed URL. No author analysis code or trained model is disclosed.Dataset · publicThis study used the Third Autonomous Greenhouse Challenge: Online Challenge Lettuce Images dataset publicly available at 4TU.ResearchData [ 36 ].Open asset ↗4TU.ResearchDatalines:819-832Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The present dataset combines transcriptomic and microscopic analyses to investigate the responses of winter oilseed rape (WOSR, Brassica napus L., cultivar Aviso) to soil drought, with a focus on differences between young and early-senescent old leaves. For microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens (Pannoramic Confocal, 3DHistech), capturing a large field of view (8-mm-long observed leaf tissue). The raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository. These high-quality scans enable the differentiation of mesophyll cells and tissues. Software analysis yielded a dataset with 54 selected cross-sectional areas, 291 delimited surfaces of palisade, spongy, and vessel tissues, and 11,136 individually delimited cells from the palisade and spongy layers. For transcriptomics, an Illumina Novaseq sequencer was used to generate 390 Gb of mRNA paired-end reads. The raw reads were filtered, mapped, and assigned to genes from the Brassica napus reference genome Darmor-bzh v10, which were subsequently used to identify differentially expressed genes (DEGs) and to perform gene ontology enrichment analysis. The raw reads are accessible under accession PRJNA939927 at the NCBI Sequence Read Archive (SRA). This high-quality dataset provides insights into the molecular mechanisms underlying oilseed rape's response to soil drought and may aid in the development of drought-tolerant cultivars. A total of 17,975 DEGs were identified between well-watered and severe drought conditions across the contrasted leaf developmental stages.
Why it matches plant phenotyping methods葉の断面画像を取得・解析し、組織面積や個別細胞などの植物形態形質を構造化した再利用可能なデータセットを提供しており、画像ベースの表現型取得が実質的な構成要素である。
abstractFor microscopy, 36 scans of 1 to 5 leaf cross-sections were acquired from paraffin-embedded leaf disc samples using a scanner with a 40x lens
Reproduction assets foundThe article deposits its own plant-phenotyping assets publicly: raw and analyzed leaf cross-section microscopy scans (Recherche Data Gouv, doi:10.57745/RK5PM3) and the transcriptomic dataset (Recherche Data Gouv doi:10.57745/7HQSM3, mirrored at NCBI SRA under PRJNA939927). The analysis pipelines cited (nf-core/rnaseq, Dataset · publicThe raw scanned cross-sections and analyzed images are available under doi.org/10.57745/RK5PM3 in the Recherche Data Gouvrepository.Open asset ↗Recherche Data Gouv · 10.57745/RK5PM3lines:1-41Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.
Why it matches plant phenotyping methods森林の機能形質を推定するための大規模ハイパースペクトルLUTデータセットを構築しており、形質取得・推定基盤そのものが研究の中心である。
abstractwe present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral LUT dataset (3.5 million trait/structural combinations) deposited in the Czech National Repository, including the authors' processing codes (merge_images.m, LUT_processing.py) within the deposit. The direct repository URL is given in the text and isDataset · publiceaf pixels.
Data source location
Institutions: Institute of Computer Science, Masaryk University; Global Change Research Institute of the Czech Academy of Sciences
City: Brno
Country: Czech Republic
Data accessibility
Repository name: National Repository
Data identification number: 10.48700/datst.bcnpf-47q73
Direct URL to data: https://data.narodni-repozitar.cz/general/datasets/4y0sy-qh735
1.
Value of the Data
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Look-Up Tables (LUT) are considered important training datasets for machine learning models to predict leaf traits.
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To date, only a limited number of LUT datasets have been developed for forest sites, particularly for Central European temperate broadleaf forests. Most of them are lOpen asset ↗National Repository · 10.48700/datst.bcnpf-47q73lines:36-69Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Why it matches plant phenotyping methods葉表皮細胞の形態形質を画像から抽出・定量するコンピュータビジョン／画像処理ワークフローの開発と適用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収載する。
abstractwe developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology.
Reproduction assets foundThe paper's quantitative phenotyping measurements (trichome types and morphometric parameters of leaf epidermal cells for nine wild potato species) are publicly available as Supplementary Tables S1 and S2 at the MDPI supplementary URL. Microscopy images are not publicly deposited and are available only upon request; noSupplement · publicObjects of the Institute of Cytology and Genetics SB RAS.
Abbreviations
The following abbreviations are used in this manuscript:
LSM
Laser scanning microscopy
PI
Propidium iodide
DAPI
4′,6-diamidino-2-phenylindole
PCA
Principal component analysis
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13213084/s1 , Table S1: Trichome types for the studied wild potato species; Table S2: Morphometric parameters for leaf epidermal cells of the studied wild potato species, including Area, Length, Width, Elongation, Circularity, Rectangularity, Perimeter, Convex Hull Area, Convex Hull Perimeter, and Convex Hull Coverage.
Author Open asset ↗lines:139-176Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network-ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.
Why it matches plant phenotyping methods衛星スペクトル指数と解析手法によりダイズの生育ステージを推定し、空間解像度や分類性能を評価しており、植物フェノタイピング手法の検証・適用が研究の中心です。
abstractThe aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops.
Reproduction assets foundThe authors state all relevant data (soybean phenology/vegetation index measurements from Sentinel-2 and Amazonia-1) are publicly available in their GitHub repository.Dataset · publicat the difference in spatial resolution of the two sensors evaluated, 10 meters per pixel of Sentinel-2 and 65 meters per pixel of Amazônia-1, is not a determining factor in the correct identification of soybean phenological stages.
Data Availability
All relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2 .
Funding Statement
This study was funded by the College of Food and Agriculture Sciences, King Saud University, RSPD2024R678 (to Mohamed A. El-Tayeb). This study was also funded by a scholarship from CAPES, Coordination for the Improvement of Higher Education Personnel, 88887.677482/2022-00 (to Airton Andrade Open asset ↗FSilva-826/DADOS---AMAZONIA1-CENTINEL2lines:240-262Code / dataset availability 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 confirmedEurope PMC · checked 14 Sept 2026
Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.
Why it matches plant phenotyping methodsトウモロコシの葉巻きという植物形質を画像から検出する深層学習法を開発・評価しており、表現型取得手法が研究の中心である。
abstractIn this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model.
Reproduction assets foundThe authors explicitly state that source code for the LRD-YOLO leaf rolling detection method is publicly available on GitHub. The maize leaf rolling image dataset itself is only available from the corresponding author upon reasonable request, so it does not qualify as a public asset.Code · public00501 and no.32300239), Shenzhen Science and Technology Program (Grant No. RCBS20210609103819020), the Innovation Program of Chinese Academy of Agricultural Sciences, National Key R&D Program of China (Grant No. 2023ZD04076).
Data availability
Some of the data, source codes and more details about our project are in the GitHub ( https://github.com/WangYH1740/LRD-YOLO ). In addition, the original datasets are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
The authors declare no conflicts of interest.
References
Bänziger M Edmeades GO Beck D Bellon M
Breeding for drought and nitrogen stress tolerance in maize: from theory to practice 2000
MeOpen asset ↗WangYH1740/LRD-YOLOlines:662-712Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.
Why it matches plant phenotyping methods画像解析と深層学習により、葉姿勢の定量化および干ばつストレス同定手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for conducting the proposed poplar plant image generation method and for annotation format conversion were uploaded to the GitHub platform ( https://github.com/L-Zhou17/Plant-Image-Generation ). Other codes and datasets are available upon request.Open asset ↗L-Zhou17/Plant-Image-Generationlines:269-294Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Introduction In strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date, strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely “YOLOv4” and “U-net” integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images. Methods Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method. Results The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92). Discussion The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.
Why it matches plant phenotyping methods画像ベースでイチゴの複数形質を抽出・測定する深層学習ツールを開発し、精度と実測値との相関を検証しており、植物表現型取得手法が研究の中心である。
abstractIn this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely “YOLOv4” and “U-net” integrated into a single system.
Reproduction assets foundThe authors publicly released the strawberry image datasets, annotations, and trained YOLOv4/U-net deep-learning models (SPT V1/V2) on GitHub, and deployed the V2 tool as a web service. Both are paper-specific, public, and actionable.Dataset · publicThe images, annotation results and DL models subjected to V1 and V2 of STP are available at https://github.com/kist-smartfarm/SPT .Open asset ↗kist-smartfarm/SPTlines:355-404Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors, which has important implications for crop breeding. Among these phenotypic characteristics, the number of leaves and growth trajectory of the plant are most accessible. Nonetheless, obtaining these phenotypes is labor intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding. However, it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments. To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture, in this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks. The experimental results demonstrate that the object detection accuracy (mAP50) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and stalks, we introduced a new lightweight "keypoint" detection branch that achieved a magnitude of 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for datasets with dot and line annotations.
Why it matches plant phenotyping methodsトウモロコシの葉・茎の数と成長軌跡をRGB画像から抽出する深層学習手法を開発し、精度評価も行っており、植物表現型取得が研究の中心である。
abstractin this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.
Reproduction assets foundThe authors explicitly deposit the code (and data) supporting this maize leaf/stalk trajectory phenotyping study in a public GitHub repository, matching an allowed URL.Code · publicThe computer code and data that support the findings of this study are deposited in a GitHub repository at https://github.com/VEGETALOADING/Point-Line-Net .Open asset ↗VEGETALOADING/Point-Line-Netlines:319-524Code / dataset availability confirmedarXiv · checked 15 Sept 2026
High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.
Why it matches plant phenotyping methods植物画像からラベル情報を読み取り、画像分割・機械学習で葉形、色、斑点などの形態形質を抽出・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・適用に該当する。
abstractimage segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications
Reproduction assets foundThe paper's authors explicitly state that all analysis code (OCR label reading, leaf segmentation, morphology classification, treatment prediction) is publicly available under the MIT License on their GitHub repository. The underlying ORNL image dataset is not stated to be publicly available, so only the code asset is.Code · publicSince a pre-trained segmentation model (the SAM) was used in this study, researchers could attempt to build segmentation models fine-tuned to only recognize leaves, which could increase model efficiency and provide more consistent results.
6 Code Availability
All code is publicly available under the MIT License on GitHub here: https://github.com/vivaansinghvi07/smoky-mountain-data-comp .
Acknowledgements
We thank Dr. Ty Frazier at Oak Ridge National Laboratory for his helpful suggestions and mentoring throughout this project.
References
Arya et al. (2022)
Arya, S., Sandhu, K.S.,
Singh, J., Kumar, S.,
2022.
Deep learning: As the new frontier in high-throughput
plant phenotyping.
Euphytica 218Open asset ↗vivaansinghvi07/smoky-mountain-data-complines:272-401Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.
Why it matches plant phenotyping methods高スループット表現型プラットフォームによる時系列の植物形質取得が研究の中心的データ基盤であり、複数形質の縦断測定と予測への応用を評価している。
abstractwe combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons
Reproduction assets foundThe paper's Data Availability Statement points to a public figshare deposit containing all phenotypic data (temporal HTP traits: plant height, LAI, spectral vegetation indices), genomic data, and GWAS results for this sesame study. No author analysis code repository is explicitly deposited.Dataset · publicfor longitudinal traits derived from single time points (green) and random regression (orange) analysis.
Figure S3 . Phenotypic (green) and genetic (orange) correlations between longitudinal traits at each time point and seed‐yield.
Data Availability Statement
All phenotypic data, genomic data, and GWAS results can be found at https://doi.org/10.6084/m9.figshare.24961491 .Open asset ↗figshare · 10.6084/m9.figshare.24961491lines:1055-1061Code / dataset availability confirmedOpenAlex · 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 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 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 15 Sept 2026
Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Why it matches plant phenotyping methods植物の葉縁形態を3D再構築して定量化する手法を開発し、仮想葉と実葉で性能評価・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets and analysis code for this 3D leaf edge reconstruction study in a public GitHub repository (MorphometricsGroup/Murata-2024), and the virtual-leaf simulation inputs (Sketchfab 3D leaf models) are publicly available. Generic libraries (Detectron2, 3Dataset · publicto S4
Data Availability Statement
The datasets used and/or analyzed during the current study are available in the repositories on Zenodo (10.5281/zenodo.10836254, 10.5281/zenodo.10836258, 10.5281/zenodo.10836260, 10.5281/zenodo.10065546, 10.5281/zenodo.10828962, 10.5281/zenodo.10121073, and 10.5281/zenodo.10829007) and GitHub ( https://github.com/MorphometricsGroup/Murata-2024 ).Open asset ↗MorphometricsGroup/Murata-2024lines:311-320Code / dataset availability confirmedCrossref · checked 7 Sept 2026
Abstract. Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structures to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait maps. Here, six key plant functional traits were selected as they can reflect plant resource acquisition strategies and ecosystem functions, including specific leaf area (SLA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), leaf area (LA) and wood density (WD). A total of 34 589 in situ trait measurements of 3447 seed plant species were collected from 1430 sampling sites in China and were used to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait maps. The models showed good accuracy in estimating WD, LPC and SLA, with average R2 values ranging from 0.48 to 0.68. In contrast, both the models had weak performance in estimating LDMC, with average R2 values less than 0.30. Meanwhile, LA showed considerable differences between the two models in some regions. Climatic effects were more important than those of edaphic factors in predicting the spatial distributions of plant functional traits. Estimates of plant functional traits in northeastern China and the Qinghai–Tibetan Plateau had relatively high uncertainties due to sparse samplings, implying a need for more observations in these regions in the future. Our spatial trait maps could provide critical support for trait-based vegetation models and allow exploration of the relationships between vegetation characteristics and ecosystem functions at large scales. The six plant functional trait maps for China with 1 km spatial resolution are now available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).
Why it matches plant phenotyping methods植物機能形質を機械学習で推定・検証し、空間形質マップとデータセットを作成することが研究の中心であり、単なる生態学的な形質測定ではない。
abstractused to generate spatial plant functional trait maps (∼1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees).
Reproduction assets foundThe paper's in situ plant functional trait dataset (34,589 measurements) and the six 1-km trait maps are publicly deposited on figshare by the authors, as stated in the Data availability section.Dataset · publicThe original plant functional trait data collected in this study that were used for machine learning models (named by the data file used for machine learning models.csv) and the final maps of plant functional traits in GeoTIFF format (named by the plant functional trait category) are available at https://doi.org/10.6084/m9.figshare.22351498 (An et al., 2023).Open asset ↗figshare · 10.6084/m9.figshare.22351498lines:350-355Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO 2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO 2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.
Why it matches plant phenotyping methods低コスト2D画像フェノタイピングを用いて、シュート構造やキャノピー被覆を定量化し、圃場での植物形態評価法としての有用性を扱っているため、フェノタイピング手法が実質的に中心である。
abstractIn addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Material 4: Table S3: (download XLSX )
Reliability estimates, BLUPs calculated for the traits measured in this study and data associated with each trait measured in the study.Open asset ↗lines:429-492Code / dataset availability confirmedCrossref · OpenAlex · checked 7 Sept 2026
Abstract Optical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectance dataset. We propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval. A standard (nadir) and a multi-angular, 10-band multispectral dataset was collected for maize using a UAV on two different days. Reflectance data was grouped by VZA and VAA (on average 2594 spectra/plot/day for the multi-angular data and 890 spectra/plot/day for nadir flights only, 13 spectra/plot/day for a standard orthomosaic), serving as predictor variables for leaf chlorophyll content (LCC), leaf area index (LAI), green leaf area index (GLAI), and nitrogen balanced index (NBI) classification. Results consistently showed higher accuracy using grouped VZA/VAA reflectance compared to the standard orthomosaic data. Pooling all reflectance values across viewing directions did not yield satisfactory results. Performing multiple flights to obtain a multi-angular dataset did not improve performance over a multi-angular dataset obtained from a single nadir flight, highlighting its sufficiency. Our openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groups, benefiting cross-disciplinary and agriculture scientists in harnessing the potential of multi-angular datasets. Graphical abstract
Why it matches plant phenotyping methodsUAVマルチアングル反射データから植物形質を抽出・分類する方法を提案し、標準オルソモザイクと精度比較しているため、表現型取得手法が中心である。
abstractWe propose a method to extract reflectance data, and their associated distinct view zenith angles (VZA) and view azimuth angles (VAA), from UAV-mounted optical cameras; enhancing plant parameter classification compared to standard orthomosaic reflectance retrieval.
Reproduction assets foundThe authors explicitly share their custom Python workflow for extracting multi-angular VZA/VAA reflectance data and reproducing the maize trait classification analysis via a public GitHub repository, referenced multiple times in the article.Code · publicOur openly shared code ( https://github.com/ReneHeim/proj_on_uav ) facilitates access to reflectance data from pre-defined VZA/VAA groupsOpen asset ↗ReneHeim/proj_on_uavlines:1-64Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldLeaf traitsPigment / colour / senescenceWater status / transpiration
Mediterranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. Optical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. However, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. Thus, the objective of the SENTHYMED/MEDOAK experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi-platform sensors having different spectral characteristics and spatial resolutions. Seven monthly data collections were performed between April and October 2021 (with a complementary one in June 2023) over two forests in the north of Montpellier, France, comprised of two oak endemic species with different phenological dynamics (evergreen: Quercus ilex and deciduous: Quercus pubescens ) and a variability of canopy cover fractions (from dense to open canopy). These collections were coincident with satellite multispectral Sentinel-2 data and one with airborne hyperspectral AVIRIS-Next Generation data. In addition, satellite hyperspectral PRISMA and DESIS were also available for some dates. All these airborne and satellite data are provided from free online download websites. Eight datasets are presented in this paper from thirteen studied forest plots: (1) overstory and understory inventory, (2) 687 canopy plant area index from Li-COR plant canopy analyzers, (3) 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from ASD spectroradiometers, (4) 92 soil moistures and temperatures from IMKO and Campbell probes, (5) 747 leaf-clip optical data from SPAD and DUALEX sensors, (6) 2594 in-lab leaf directional-hemispherical reflectances and transmittances from ASD spectroradiometer coupled with an integrating sphere, (7) 747 in-lab measured leaf water and dry matter content, and additional leaf traits by inversion of the PROSPECT model and (8) UAV-borne LiDAR 3-D point clouds. These datasets can be useful for multi-scale and multi-temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. Other targeted applications can be forest 3-D modelling, biodiversity assessment, fire risk prevention and globally vegetation monitoring.
Why it matches plant phenotyping methods森林の葉からキャノピーまでの植物形質データとマルチプラットフォーム光学・LiDARデータを体系的に整備し、衛星植生形質プロダクトの較正・検証に用いるデータセット研究であり、形質取得と再利用可能な検証基盤が中心です。
titleMulti-scale datasets for monitoring Mediterranean oak forests from optical remote sensing during the SENTHYMED/MEDOAK experiment in the north of Montpellier (France).
Reproduction assets foundThis Data in Brief article deposits the paper's own SENTHYMED/MEDOAK plant-phenotyping measurements (forest inventory, canopy plant area index, forest/leaf optical properties, soil moisture, leaf-clip sensor data, leaf traits, UAV-borne LiDAR point clouds) in the public SEDOO repository with explicit DOIs and a direct,Dataset · publicat https://eoweb.dlr.de/egp/ (image rasters, .tif for GeoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
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Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UOpen asset ↗SEDOO · 10.6096/8005lines:31-86Dataset · publicoTIFF format)
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Sentinel-2 data can be downloaded from the THEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.154Open asset ↗SEDOO · 10.6096/8007lines:31-86Dataset · publicTHEIA portal at https://www.theia-land.fr/en/product/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to aOpen asset ↗SEDOO · 10.6096/8006lines:31-86Dataset · publicoduct/sentinel-2-surface-reflectance (image rasters, .tif for GeoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
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Forest inventory: https://doi.org/10.6096/8005
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Canopy plant area index: https://doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/Open asset ↗SEDOO · 10.6096/8001lines:31-86Dataset · publiceoTIFF format)
Data accessibility
Repository name: SEDOO
Data identification number:
•
Forest inventory: https://doi.org/10.6096/8005
•
Canopy plant area index: https://doi.org/10.6096/8007
•
Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the daOpen asset ↗SEDOO · 10.6096/8002lines:31-86Dataset · public//doi.org/10.6096/8007
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Forest optical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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These datasets were collected to provide calibratioOpen asset ↗SEDOO · 10.15454/AGBW7Glines:31-86Dataset · publictical properties : https://doi.org/10.6096/8006
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Soil moisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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Value of the Data
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These datasets were collected to provide calibration/validation data for methods aimiOpen asset ↗SEDOO · 10.15454/DMYWPBlines:31-86Dataset · publicoisture : https://doi.org/10.6096/8001
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Leaf-clip optical sensor data : https://doi.org/10.6096/8002
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Leaf optical properties: https://doi.org/10.6096/8004
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Leaf traits : https://doi.org/10.6096/8003
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UAV-borne LiDAR 3-D point clouds : https://doi.org/10.15454/AGBW7G , https://doi.org/10.15454/DMYWPB
Direct URL to all data: https://remotetree.sedoo.fr/catalogue/ Instructions for accessing the datasets on the website: the datasets are visible under the search menu through projects and then by selecting FOREST/SENTHYMED
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Value of the Data
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These datasets were collected to provide calibration/validation data for methods aiming at linking ground observations on Mediterranean forests withOpen asset ↗SEDOOlines:31-86Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.
Why it matches plant phenotyping methods小麦の3D形態情報をMVS-Phenoと点群・深層学習で取得し、器官分割、形態パラメータ抽出、精度評価を行う手法研究であり、フェノタイピング手法が中心です。
abstractwe propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale.
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code used in the article are publicly available on GitHub at the authors' repository, which matches an allowed URL. This qualifies as a paper-specific public asset covering the wheat 3D reconstruction/phenotyping analysis.Code · publicThe data and code used in this article are available on GitHub, at https://github.com/lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatOpen asset ↗lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatlines:280-436Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world’s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L’Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.
Why it matches plant phenotyping methodsドローン・衛星リモートセンシングとNDVI等を用いて除草剤効果を評価し、その手法を有効な評価ツールとして検証しているため、植物状態の取得・評価方法が中心である。
abstractTherefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia.
Reproduction assets foundThe article states 'Data are contained within the article' and provides no author code, model, or dataset deposit. The only paper-specific public asset is the MDPI supplementary file, which contains Figure S1 showing the control subplots affected by Echinochloa spp. (field imagery related to the phenotyping experiment,Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s24030804/s1 . Figure S1. Control subplots affected by Echinochloa spp. (Own elaboration).
Click here for additional data file.
Author ContributionsOpen asset ↗lines:90-101Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background: Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in an quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, it provides additional information on the spatial localization of pathogenic microbes in different plant tissues. Results: Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from diseased (yellow) leaf area. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar way as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. Conclusion: Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software (https://github.com/MolPlantPathology/ScAnalyzer) has the potential to standardize the analysis of disease assays between different groups.
Why it matches plant phenotyping methods植物葉の病徴面積と病原体拡散を画像解析で自動定量するソフトウェアを開発・提示しており、植物表現型の取得・抽出が研究の中心である。
abstractimage processing provides a more accurate and objective quantification of plant disease symptoms
Reproduction assets foundThe preprint states that all code and raw images generated during the study are available at the authors' GitHub repository (https://github.com/MolPlantPathology/ScAnalyzer), which contains the ScAnalyzer Python/R analysis pipeline; the repository also hosts the printable leaf-sampling grid (grid.pdf) used as the phenpCode · publicThe code is available on GitHub ( https://github.com/MolPlantPathology/ScAnalyzer ).Open asset ↗MolPlantPathology/ScAnalyzerlines:85-109Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.
Why it matches plant phenotyping methodsロゼット植物の葉数という表現型を画像から推定するCNN手法を開発し、既存モデルおよび標準データセットで比較評価しており、方法が研究の中心である。
abstractTherefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net.
Reproduction assets foundThe paper's leaf-counting experiments were run on the CVPPP and KOMATSUNA plant image datasets, which the authors explicitly state are publicly available with download links. No author code or trained model is released.Dataset · publication, Investigation, Validation. L.A.: Supervision, Validation, Writing-Reviewing and Editing. A.G.: Software, Visualization, Writing-Reviewing.
Funding
Open access funding provided by Linköping University.
Data availibility
The datasets that support the findings of this study are publicly available. Link for CVPPP dataset is: http://www.plant-phenotyping.org/datasets. Link for KOMATSUNA dataset is: https://limu.ait.kyushu-u.ac.jp/ agri/komatsuna/.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Furbank RT, Tester M. Open asset ↗plant-phenotyping.org · CVPPPlines:275-305Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and results We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. Conclusions CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.
Why it matches plant phenotyping methods葉表皮細胞の画像セグメンテーションと形態計測を自動化するFiji/Rツールの開発・比較検証であり、植物形質取得法が研究の中心です。
abstractCuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe paper's authors publicly release their Fiji macros and R filtering notebook (CuticleTrace toolkit) on GitHub, and the 50-image test set derives from the public Cuticle Database. Both are paper-specific, public, and actionable.Code · publicAll generated and analyzed data from this study are included in the published article and its Supporting Information (Appendix S1 ). The CuticleTrace User Manual, code for the Fiji macros, and the R Notebook for filtering cells are available in the GitHub repository ( https://github.com/benjlloyd/CuticleTrace ). A video tutorial is available at https://youtu.be/XLhWd-tpU70 .Open asset ↗benjlloyd/CuticleTracelines:354-354Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MaizeLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsWater status / transpiration
Advanced smartphone technology now integrates sophisticated sensors, increasing access to high-precision data acquisition. This study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays). 3D point cloud models enabled non-destructive data collection, and four methods for canopy area extraction were evaluated in relation to plant transpiration rates. Results showed a strong correlation (R 2 =0.92, RMSE=49.78) between manually scanned and iPhone-estimated plant surface areas. Additionally, the stem-to-plant surface area ratio was found to be 12.3% (R 2 =0.9, RMSE=28.42). Using this ratio to predict canopy area showed a significant correlation (R 2 =0.83) with actual canopy measurements. The iPhone’s surface area measurement tool offers an advantage by scanning the entire plant surface, unlike traditional leaf area index measurements, which often cannot penetrate the canopy. Moreover, real-size surface measurement of the canopy correlated strongly (R 2 =0.83) with whole canopy transpiration rates measured gravimetrically. This study introduces a novel method for analyzing 3D plant traits using a portable, affordable, and accurate tool, which has the potential to enhance plant breeding and agricultural practices. 0. How to Use This Template The template details the sections that can be used in a manuscript. Note that each section has a corresponding style, which can be found in the “Styles” menu of Word. Sections that are not mandatory are listed as such. The section titles given are for articles. Review papers and other article types have a more flexible structure. Remove this paragraph and start section numbering with 1. For any questions, please contact the editorial office of the journal or support@mdpi.com .
Why it matches plant phenotyping methodsiPhoneのLiDARと3D点群を用いてトウモロコシの葉・植物表面積を推定する手法を開発・検証しており、植物形質の取得が研究の中心である。
abstractThis study tested the hypothesis that the iPhone 13-Pro camera, with LiDAR technology, can accurately estimate maize leaf surface area (Zea mays).
Reproduction assets foundThe paper states that all statistical code and data files for the maize 3D leaf phenotyping analysis are publicly available in the authors' GitHub repository.Code · publicconducted using the “scipy” package’s “f_oneway”
12 function [18]. The Python packages “pandas” [19] and “numpy” [20] were used to arrange the
13 data before plotting. The Python packages “matplotlib”, “seaborn” [21] were used for data
14 visualization. All statistical code and data files needed are available to download
15 at https://github.com/gavrielbs/3D_Corn_Phenotype.
16
17 PlantArray System by Plant-DiTech LTD
18 PlantArray is a high-throughput, multi-sensor physiological phenotyping gravimetric
19 platform. This plant phenotyping system performs quick plant screening based on precise
20 physiology traits measurements that are great indicators for yield potential with proven high
21 cOpen asset ↗gavrielbs/3D_Corn_Phenotypepdf-layout-page:4 lines:1-44Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.
Why it matches plant phenotyping methods葉面積という植物形質を機械学習で推定するソフトウェアパッケージを開発・評価しており、形質取得・推定手法が研究の中心である。
abstractThis research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species
Reproduction assets foundThe paper's leaf photographs dataset is deposited on figshare (CC-BY) and the LeafArea R analysis package is open-source on GitHub; both are paper-specific, public, and actionable.Code · publiccurrently for six plant species. We encourage researchers to provide sufficient data to expand both
the number of species and the number of observations, thereby continually enhancing the predictive
power of our models. This includes broadening the range of plant species that can be studied. The
LeafArea package is open-source (https://github.com/velasquez-vasconez/LeafArea), and any
contributions to the database or code will be greatly appreciated.
Conclusions
The LeafArea package introduces four invaluable functions for precise leaf area estimation in six
Andean fruit species. It incorporates the optimal GLM and GLMM models, alongside the powerful
Random Forest and XGBoost algorithms, resuOpen asset ↗github · velasquez-vasconez/LeafAreapdf-raw-page:7 lines:1-52Code / 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 confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
The strong societal demand to reduce pesticide use and adaptation to climate change challenges the capacities of phenotyping new varieties in the vineyard. High-throughput phenotyping is a way to obtain meaningful and reliable information on hundreds of genotypes in a limited period. We evaluated traits related to growth in 209 genotypes from an interspecific grapevine biparental cross, between IJ119, a local genitor, and Divona, both in summer and in winter, using several methods: fresh pruning wood weight, exposed leaf area calculated from digital images, leaf chlorophyll concentration, and LiDAR-derived apparent volumes. Using high-density genetic information obtained by the genotyping by sequencing technology (GBS), we detected 6 regions of the grapevine genome [quantitative trait loci (QTL)] associated with the variations of the traits in the progeny. The detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine. Heritabilities calculated with LiDAR-derived total canopy and pruning wood volumes were high, above 0.66, and stable between growing seasons. These variables provided genetic models explaining up to 47% of the phenotypic variance, which were better than models obtained with the exposed leaf area estimated from images and the destructive pruning weight measurements. Our results highlight the relevance of LiDAR-derived traits for characterizing genetically induced differences in grapevine growth and open new perspectives for high-throughput phenotyping of grapevines in the vineyard.
Why it matches plant phenotyping methodsLiDARによるブドウ樹冠・剪定木体積の取得を、従来法との相関、遺伝率、QTL解析で評価しており、植物形質の高スループット計測法が研究の中心です。
abstractThe detection of statistically significant QTLs, as well as correlations ( R 2 ) with traditional methods above 0.46, shows that LiDAR technology is effective in characterizing the growth features of the grapevine.
Reproduction assets foundThe paper's Data availability statement provides a public repository deposit (DOI 10.57745/PETTGY) for the study data and an authors' public ImageJ script for estimating foliage coverage used in the RGB-image phenotyping analysis.Dataset · publictyping but also his expertise and helped with the manuscript review. D.M. supervised the program and helped with manuscript writing. É.D. supervised the whole study and wrote the first draft of the manuscript.
Competing interests: The authors declare that they have no competing interests.
Data availability
Data are available at https://doi.org/10.57745/PETTGY . ImageJ script for estimating foliage coverage: https://forgemia.inra.fr/eric.duchene/image-analysis-scripts/-/blob/main/FoliageCoverage_PC_EN.txt
Supplementary Materials
Supplementary 1
Fig. S1
Tables S1 to S5
Click here for additional data file.
References
1.
Carvalho
LC , Goncalves
EF ,
da
Silva
JM , Costa
JM
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Potential phOpen asset ↗10.57745/PETTGY · 10.57745/PETTGYlines:825-1015Code / 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
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Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
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dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
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Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Functional plant ecology aims to understand how functional traits govern the distribution of species along environmental gradients, the assembly of communities, and ecosystem functions and services. The rapid rise of functional plant ecology has been fostered by the mobilization and integration of global trait datasets, but significant knowledge gaps remain about the functional traits of the ∼380,000 vascular plant species worldwide. The acquisition of urgently needed information through field campaigns remains challenging, time-consuming and costly. An alternative and so far largely untapped resource for trait information is represented by texts in books, research articles and on the internet which can be mobilized by modern machine learning techniques. Here, we propose a natural language processing (NLP) pipeline that automatically extracts trait information from an unstructured textual description of a species and provides a confidence score. To achieve this, we employ textual classification models for categorical traits and question answering models for numerical traits. We demonstrate the proposed pipeline on five categorical traits (growth form, life cycle, epiphytism, climbing habit and life form), and three numerical traits (plant height, leaf length, and leaf width). We evaluate the performance of our new NLP pipeline by comparing results obtained using different alternative modeling approaches ranging from a simple keyword search to large language models, on two extensive databases, each containing more than 50,000 species descriptions. The final optimized pipeline utilized a transformer architecture to obtain a mean precision of 90.8% (range 81.6-97%) and a mean recall of 88.6% (77.4-97%) on the categorical traits, which is an average increase of 21.4% in precision and 57.4% in recall compared to a standard approach using regular expressions. The question answering model for numerical traits obtained a normalized mean absolute error of 10.3% averaged across all traits. The NLP pipeline we propose has the potential to facilitate the digitalization and extraction of large amounts of plant functional trait information residing in scattered textual descriptions. Additionally, our study adds to an emerging body of NLP applications in an ecological context, opening up new opportunities for further research at the intersection of these fields.
Why it matches plant phenotyping methods植物機能形質を非構造化テキストから自動抽出するNLPパイプラインを開発し、複数モデルと大規模データベースで性能評価しており、形質取得・推定手法が研究の中心である。
abstractHere, we propose a natural language processing (NLP) pipeline that automatically extracts trait information from an unstructured textual description of a species and provides a confidence score.
Reproduction assets foundThe paper explicitly states that the code to train, evaluate, and use the NLP trait-extraction models is publicly available in the authors' GitHub repository. The POWO, Wikipedia, and GIFT data sources are third-party databases/cited resources rather than paper-specific deposits, so only the code repository qualifies.Code · public1 The code to train, evaluate and use the models is available at
https://github.com/ViktorDomazetoski/NLP-Plant-TraitsOpen asset ↗ViktorDomazetoski/NLP-Plant-Traitspdf-page:5 lines:1-50Code / dataset availability confirmedbioRxiv · checked 14 Sept 2026
The evolutionary histories of species have been shaped by genomic, environmental, and morphological variation. Understanding the interactions among these sources of variation is critical to infer accurately the biogeographic history of lineages. Here, using the geographically widely distributed plum genus (Prunus, Rosaceae) as a model, we investigate how changes in genomic and environmental variation drove the diversification of this group, and we quantify the morphological features that facilitated or resulted from diversification. We sequenced 587 nuclear loci and complete chloroplast genomes from 99 species representing all major lineages in Prunus, with a special focus on the understudied tropical racemose group. The environmental variation in extant species was quantified by synthesizing bioclimatic variables into principal components of environmental variation using thousands of georeferenced herbarium specimens. We used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images. Our phylogenomic and biogeographic analyses revealed that ancient hybridization and/or allopolyploidy spurred the initial rapid diversification of the genus in the early Eocene, with subsequent diversification in the north temperate zone, neotropics, and paleotropics. This diversification involved successful transitions between tropical and temperate biomes, an exceedingly rare event in woody plant lineages, accompanied by morphological changes in leaf and reproductive morphology. The machine learning approach detected morphological variation associated with ancient hybridization and quantified the breadth of morphospace occupied by major lineages within the genus. The paleotropical lineages of Prunus have diversified steadily since the late Eocene/early Oligocene, while the neotropical lineages diversified much later. Critically, both the tropical and temperate lineages have continued to diversify. We conclude that the genomic rearrangements created by reticulation deep in the phylogeny of Prunus may explain why this group has been more successful than other groups with tropical origins that currently persist only in either tropical or temperate regions, but not both.
Why it matches plant phenotyping methods機械学習を用いて標本画像から植物の形態変異を分類・測定し、葉および生殖形態の表現型を定量化しており、形態計測ワークフローが研究の中心的要素である。
abstractWe used machine learning algorithms to classify and measure morphological variation present in thousands of digitized herbarium sheet images.
Reproduction assets foundThe paper's herbarium image data and supplementary material are deposited in Dryad; trained Prunus ML classifiers are on Hugging Face; author Jupyter notebooks for the ML workflow are on GitHub. All are paper-specific, public, and actionable.Dataset · public1126 Data Availability
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1128 Data available from the Dryad Digital Repository: DOI: 10.5061/dryad.x95x69pwr; Reviewer
1129 URL: http://datadryad.org/share/L-QUcxrgnpTr6l0Td3MxdfJDCUT1iRbPmT4SzA2LwMA.
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1131 Supplementary material, image data, and DNA sequence matrices are available from the Dryad
1132 Digital Repository. Raw sequence data were submitted to NCBI GenBank (SUB13638423).
1133 Machine learning models are hosted on Hugging Face with temporary URLs
1134 (https://huggingface.co/richiehodel/Prunus_lineage_classiOpen asset ↗Dryad Digital Repository · 10.5061/dryad.x95x69pwrpdf-layout-page:54 lines:1-31Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Premise Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets. Methods LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms. Results LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types. Discussion LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.
Why it matches plant phenotyping methodsLeafMachine2は、植物画像から葉などの形態形質を自動抽出・計測する機械学習／コンピュータビジョン手法およびツールの開発が中心であり、植物フェノタイピング方法論に明確に該当する。
abstractWe present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.
Reproduction assets foundThe paper's authors publicly release the LeafMachine2 source code, trained machine learning networks, and user manual on GitHub, plus sample images from the test data sets on Zenodo. Both are paper-specific, public, and actionable.Code · publicThe LeafMachine2 source code, examples, machine learning networks, and user manual are available at https://github.com/Gene-Weaver/LeafMachine2 and https://www.LeafMachine.orgOpen asset ↗https://github.com/Gene-Weaver/LeafMachine2lines:424-512Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise Because of the trade-off between water loss and carbon dioxide assimilation, the conductivity of the transpiration path in a leaf is an important limit on photosynthesis. Closely packed veins correspond to short paths and high assimilation rates while widely spaced veins are associated with higher resistance to flow and lower maximum photosynthetic rates. Vein length per area (VLA) has become the standard metric for comparing leaves with different vein densities; its measurement typically utilizes digital image processing with varying amounts of human input. Methods and results Here, we propose three new ways of measuring vein density using image analysis that improve on currently available procedures: (1) areole area distributions, (2) a sizing transform, and (3) a distance map. Each alternative has distinct practical, statistical, and biological limitations and advantages. In particular, we advocate the log-transformed modal distance map of a vein mask as an estimator to replace VLA as a standard metric for vein density. Conclusions These methods, for which open-source code appropriate for high-throughput automation is provided, improve on VLA by producing determinate measures of vein density as distributions rather than point estimates. Combined with advances in image quality and computational efficiency, these methods should help clarify the physiological and evolutionary significance of vein density.
Why it matches plant phenotyping methods葉脈密度という植物形態形質を画像解析で測定する新手法を開発し、既存指標と比較・置換提案しており、フェノタイピング手法が中心である。
abstractHere, we propose three new ways of measuring vein density using image analysis that improve on currently available procedures
Reproduction assets foundThe paper's batch analysis was applied to 230 cleared-leaf images from the authors' prior study, whose image archive and metadata are publicly deposited on Dryad (Green et al. 2015). The paper-specific analysis code (Appendices S1–S2) and raw images (S5–S6) exist as supplements but no explicit public URL for them is inDataset · publicfunded by the Spanish Ministry of Science and Innovation, through the Agencia Estatal de Investigación (reference PID2021‐129074O‐BI00).
DATA AVAILABILITY STATEMENT
All data analyzed are provided in the Supporting Information (Appendices S1 – S6 ), or are available in the supplementary archive to Green et al. ( 2014 ), found at https://datadryad.org/stash/dataset/doi:10.5061/dryad.8h022 (Green et al., 2015 ), which contains images and their related metadata from a prior study. All analysis was done with the open source software R (R Core Team, 2018 ) and the library EBImage (Pau et al., 2010 ).
REFERENCES
Blonder , B.
,
C.
Violle
,
L. P.
Bentley
, and
B. J.
Enquist
. 2011 .
Venation networksOpen asset ↗Dryad · 10.5061/dryad.8h022lines:107-403Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.
Why it matches plant phenotyping methods葉の表皮細胞形態を画像からセグメンテーション・測定するソフトウェアを開発し、代替手法および専門家の手トレースと比較検証しており、植物表現型取得法が研究の中心です。
abstractCuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.
Reproduction assets foundThe authors publicly release the CuticleTrace FIJI macros and R filtering notebook used for the paper's epidermal cell phenotyping analysis on GitHub, with explicit availability language and URL.Code · publicSB and SWP supervised and
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directed the research.
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DATA AVAILABILITY
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All generated and analyzed data from this study are included in the published article and its
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Supporting Information (Fig. S2). The code for the FIJI macros as well as the R notebook for
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filtering cells is available in the GitHub repository: (https://github.com/benjlloyd/CuticleTrace).296
REFERENCES
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Aono, A. H., J. S. Nagai, G. da S. M. Dickel, R. C. Marinho, P. E. A. M. de Oliveira, J. P. Papa,
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and F. A. Faria. 2021. A stomata classification and detection system in microscope
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images of maize cultivars. PLOS ONE 16: e0258679.
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Barclay, R., J. Mcelwain, D. Dilcher, and B. Sageman. 2007. The COpen asset ↗benjlloyd/CuticleTracepdf-raw-page:13 lines:1-61Code / 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 14 Sept 2026
Abstract Feature selection, reducing number of input variables to develop classification model, is an important process to reduce computational and modelling complexity and affects the performance of image process. In this paper, we have proposed new statistical approaches for feature selection based on sample selection. We have applied our new approaches to grapevine leaves data that possesses properties of shape, thickness, featheriness, and slickness are investigated in images. To analyze such kind of data by using image process, thousands of features are created and selection of features plays important role to predict the outcome properly. In our numerical study, Convolutional Neural Networks (CNNs) have been used as feature extractors and then obtained features from the last average pooling layer to detect the type of grapevine leaves from images. These features have been reduced by using our suggested four statistical methods: Simple random sampling (SRS), ranked set sampling (RSS), extreme ranked set sampling (ERSS), Moving extreme ranked set sampling (MERSS). Then selected features have been classified with Artificial Neural Network (ANN) and we have obtained the best accuracy of 97.33% with our proposed approaches. Based on our empirical analysis, it has been determined that the proposed approach exhibits efficacy in the classification of grapevine leaf types. Furthermore, it possesses the potential for integration into various computational devices.
Why it matches plant phenotyping methodsCNN特徴抽出と新規特徴選択法を用いてブドウ葉画像の形態的な葉タイプ分類を行う手法開発が中心であり、画像から植物器官の表現型を推定する研究と判断します。
abstractwe have proposed new statistical approaches for feature selection based on sample selection
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThis dataset was created by Koklu et al. ( 2022 ) and obtained from the website http://www.muratkoklu.com/datasets/Grapevine_Leaves_Image_Dataset.rar .Open asset ↗http://www.muratkoklu.com/datasets/Grapevine_Leaves_Image_Dataset.rar · Grapevine_Leaves_Image_Dataset.rarlines:151-284Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands (e.g., hydraulic conductivity, transpiration efficiency, and tolerance to damage and blockage) constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. To better understand the morphological diversities and constraints on leaf vein networks, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images. Because the one-dimensional distribution patterns align with the Pareto front that optimizes transport efficiency, construction cost, and robustness to damage, as predicted by the earlier theoretical study, our findings suggested that venation patterns are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins.
Why it matches plant phenotyping methods葉画像の取得、深層学習による葉脈セグメンテーション、ネットワーク抽出、特徴量計算から成る高スループット表現型解析ワークフローを開発しており、植物形態の定量化が研究の中心である。
abstractwe developed a simple, high-throughput phenotyping workflow for the quantification of vein networks
Reproduction assets foundThe paper's Data Availability statement and Methods text explicitly deposit the untreated leaf image dataset on Zenodo (10.5281/zenodo.7070266) and the analysis code and trained U-Net model weights on Zenodo (10.5281/zenodo.8020856) and GitHub (MorphometricsGroup/iwamasa-2022). These are paper-specific, public, and可直接可Dataset · publicData Availability: All relevant data and code are available on Zenodo at links https://doi.org/10.5281/zenodo.7070266 and https://doi.org/10.5281/zenodo.8020856 , and on GitHub at links https://github.com/MorphometricsGroup/iwamasa-2022 .Open asset ↗Zenodo · 10.5281/zenodo.7070266lines:104-116Code · publicThe analysis code and model weights have been publicly available at Zenodo [ 40 ] and GitHub ( https://github.com/MorphometricsGroup/iwamasa-2022 ).Open asset ↗Zenodolines:145-157Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
KEY MESSAGE: Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
Why it matches plant phenotyping methodsドローン画像から植物高・葉面積・分げつ密度を推定し、これらを用いたフェノミック選抜モデルを評価しており、形質取得と解析ワークフローが研究の中心である。
abstractThe dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the study's datasets (phenotypic/trait data from drone-based wheat phenotyping) in the ETH Research Collection and the phenomics data processing source code in a public ETH GitLab repository. Both are paper-specific, public, and actionable.Dataset · publicThe datasets generated and analyzed during the current study are openly available in the ETH Research Collection repository, http://doi.org/10.3929/ethz-b-000566864 .Open asset ↗ETH Research Collection · 10.3929/ethz-b-000566864lines:210-223Code · publicSource code for the phenomics data processing methods used in this study are openly available in the ETH gitlab repository, https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing .Open asset ↗ETH gitlab · crop_phenotyping/htfp_data_processinglines:210-223Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
C 4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C 4 plants compared with the ancestral C 3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C 4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C 4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C 4 species demonstrating natural variation for this trait.
Why it matches plant phenotyping methodsC4植物の葉脈密度を高速・高スループットに定量する染色、画像取得、画像解析手法を開発しており、植物形態形質の抽出が研究の中心です。
abstractHere we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C 4 species.
Reproduction assets foundThe paper's Starch4Kranz vein-density analysis pipeline (MATLAB and Python scripts) is explicitly deposited publicly on GitHub by the authors. No separate phenotype dataset or image deposit is stated in the supplied text.Code · publicfunction, blur.m contains the blurring function, and running_Starch4Kranz.m contains a short script that runs Starch4Kranz.m while saving results to a table easily transferable to software for further data analysis. For Python, just two scripts are required, Starch4Kranz.py and running_Starch4Kranz.py. Scripts ara available at https://github.com/plycs5/Starch4Kranz . To run the script in MATLAB the user must have the ImageProcessingToolbox activated and in Python they must have the dependent libraries installed into their environment. There are 14 inputs the user can supply (Supporting Information: Table S1 ), five of which are necessary; filename, trim_factor (see Results), pixel_length_Open asset ↗plycs5/Starch4Kranzlines:94-103Code / 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
Premise The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification. Methods and results MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold-based methods or color correction cards typically required in other software methods. The leaf morphometric parameters measured by this software, especially leaf aspect ratio, were able to distinguish between large populations of different accessions for the same species in a high-throughput manner. Conclusions MuLES provides a simple method for the rapid measurement of leaf morphometric parameters in large plant populations from digital images and demonstrates the ability of leaf aspect ratio to distinguish between closely related plant types.
Why it matches plant phenotyping methods葉のデジタル画像から形態形質を自動・高スループットに抽出するソフトウェア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。
abstractThe Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.
Reproduction assets foundThe paper's authors publicly release the MuLES and imgSplit macro scripts (the analysis code used for the leaf phenotyping measurements) on GitHub, along with sample leaf images and video walkthroughs on YouTube.Code · publicIra A. Herniter, Email: ira.herniter@rutgers.edu.
DATA AVAILABILITY STATEMENT
The MuLES and imgSplit scripts are open source and freely available on GitHub ( https://github.com/0cb/mules ), along with a detailed introduction (“Introduction to MuLES”) and sample leaf images from each species tested. Video demonstrations of the MuLES macro script (Video 1 ) and imgSplit macro script (Video S1 ) are included with the article and are also available on YouTube (MuLES: https://www.youtube.com/watch?v=vtj93rbDO28 ; imOpen asset ↗0cb/muleslines:143-204Code · publicailable on GitHub ( https://github.com/0cb/mules ), along with a detailed introduction (“Introduction to MuLES”) and sample leaf images from each species tested. Video demonstrations of the MuLES macro script (Video 1 ) and imgSplit macro script (Video S1 ) are included with the article and are also available on YouTube (MuLES: https://www.youtube.com/watch?v=vtj93rbDO28 ; imgSplit: https://www.youtube.com/watch?v=9HVsNvAWPjE ).
REFERENCES
Biot, E.
, Cortizo M., Burguet J., Kiss A., Oughou M., Maugarny‐Calès A., Gonçalves B., et al. 2016. Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis. Development
143: 3417–3428.
Bonhomme, V.
, Picq S., Gaucherel C., aOpen asset ↗lines:143-204Code · publicg with a detailed introduction (“Introduction to MuLES”) and sample leaf images from each species tested. Video demonstrations of the MuLES macro script (Video 1 ) and imgSplit macro script (Video S1 ) are included with the article and are also available on YouTube (MuLES: https://www.youtube.com/watch?v=vtj93rbDO28 ; imgSplit: https://www.youtube.com/watch?v=9HVsNvAWPjE ).
REFERENCES
Biot, E.
, Cortizo M., Burguet J., Kiss A., Oughou M., Maugarny‐Calès A., Gonçalves B., et al. 2016. Multiscale quantification of morphodynamics: MorphoLeaf software for 2D shape analysis. Development
143: 3417–3428.
Bonhomme, V.
, Picq S., Gaucherel C., and Claude J.. 2014. Momocs: Outline analysis using R. JoOpen asset ↗lines:143-204Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant species complexes represent a particularly interesting example of taxonomically complex groups (TCGs), linking hybridization, apomixis, and polyploidy with complex morphological patterns. In such TCGs, mosaic-like character combinations and conflicts of morphological data with molecular phylogenies present a major problem for species classification. Here, we used the large polyploid apomictic European Ranunculus auricomus complex to study relationships among five diploid sexual progenitor species and 75 polyploid apomictic derivate taxa, based on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles), genomic data (97,312 RAD-Seq loci, 48 phased target enrichment genes, 71 plastid regions) from 220 populations. We showed that (1) observed genomic clusters correspond to morphological groupings based on basal leaves and concatenated traits, and morphological groups were best resolved with RAD-Seq data; (2) described apomictic taxa usually overlap within trait morphospace except for those taxa at the space edges; (3) apomictic phenotypes are highly influenced by parental subgenome composition and to a lesser extent by climatic factors; and (4) allopolyploid apomictic taxa, compared to their sexual progenitor, resemble a mosaic of ecological and morphological intermediate to transgressive biotypes. The joint evaluation of phylogenomic, phenotypic, reproductive, and ecological data supports a revision of purely descriptive, subjective traditional morphological classifications.
Why it matches plant phenotyping methods幾何学的形態計測を用いて葉や花托の形態形質を大規模に取得・解析し、ゲノムパターンとの比較に研究の中心的役割を持たせているため、植物フェノタイピング手法の実質的な適用に該当する。
abstractbased on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles)
Reproduction assets foundThe paper's geometric morphometric phenotyping inputs (leaf/receptacle images and landmark files) are publicly deposited on FigShare, directly reproducing this paper's plant-phenotyping measurements. Flow cytometric ploidy/reproduction-mode data and the MDPI supplement with morphometric tables are also paper-specific; Dataset · public3), respectively. More detailed data, tables, and figures concerning (phylo)genomic analyses are deposited on FigShare ( https://doi.org/10.6084/m9.figshare.14046305 ) (accessed on 7 March 2023). Flow cytometric (FC) and flow cytometric seed screening (FCSS) data (ploidy levels, reproduction modes) are also stored in Figshare ( https://doi.org/10.6084/m9.figshare.13352429 ) (accessed on 7 March 2023). We deposited image data processed for geometric morphometric analyses on FigShare upon publication ( https://doi.org/10.6084/m9.figshare.21393375 ) (accessed on 31 January 2023).
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded by theOpen asset ↗FigShare · 10.6084/m9.figshare.13352429lines:135-158Supplement · publice thank Esther Philine Zieschang, Anne-Sophie Burmeister, and Jennifer Krüger for processing leaf scans for geometric morphometric analyses, and Michael Kloster for providing scripts to automatically cut leaf scans into basal and stem leaf parts.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology12030418/s1 , Figure S1: Landmark digitization of the taxonomically most informative Ranunculus auricomus traits; Figure S2: Correlation plot concerning non-autocorrelated (r < 0.8), standardized (0 mean, unit variance) abiotic environmental factors; Figure S3: Correlation plot concerning standardized axis shape scores of alOpen asset ↗lines:122-134Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · checked 7 Sept 2026
Abstract C 4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C 4 plants compared with the ancestral C 3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intra-specific variation in traits such as vein density in C 4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species. Starch staining allowed high-contrast images to be acquired that permitted image analysis using a MATLAB-based program. The method works for the dicotyledon Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and the monocotyledon Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C 4 species demonstrating natural variation for this trait. One sentence summary Preferential accumulation of starch in bundle sheath cells of C 4 plants allows high-throughput phenotyping of vein density.
Why it matches plant phenotyping methodsC4植物の葉脈密度を定量する高スループット染色・画像解析法の開発と適用が研究の中心であり、植物形質の取得手法を扱っている。
abstractHere we report a high-throughput method to quantify vein density that leverages the bundle sheath specific accumulation of starch found in C 4 species.
Reproduction assets foundThe paper's Starch4Kranz MATLAB pipeline for quantifying vein density in C4 plants is explicitly stated to be publicly available on GitHub at the authors' repository.Code · publicScript is available at https://github.com/plycs5/Starch4Kranz.Open asset ↗plycs5/Starch4Kranzpdf-page:11 lines:1-28Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks (CNNs) to segment the leaf body and visible venation of 2,906 P. trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (i) does not require experimental or image pre-processing, (ii) uses the raw RGB images at full resolution, and (iii) requires very few samples for training (e.g., just eight images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.
Why it matches plant phenotyping methods葉画像から形態・葉脈形質を抽出する少数ショット学習手法を開発し、実測値で検証するとともに、大規模データセットを提供しており、植物フェノタイピング手法が中心である。
abstractwe address these challenges by leveraging few-shot learning with convolutional neural networks (CNNs) to segment the leaf body and visible venation of 2,906 P. trichocarpa leaf images obtained in the field.
Reproduction assets foundThe paper publicly releases its few-shot leaf/vein segmentation code and all phenotyping assets (2,906 leaf images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and SNPs) via two ORNL DOI repositories cited as [31] and [36].Code · publicIn addition to releasing all of the segmentation code on a public GitHub repository [ 31 ] , we are also releasing all of the images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes on the Oak Ridge National Laboratory Constellation Portal (a public DOI data server) [ 36 ] .Open asset ↗lines:249-258Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.
Why it matches plant phenotyping methods画像分割、機械学習、OCRを用いて植物の葉形・色・斑点などの形態形質を抽出・分類し、精度も評価しており、植物フェノタイピング手法が中心である。
titleHigh-Throughput Phenotyping using Computer Vision and Machine Learning
Reproduction assets foundThe paper's authors publicly release all analysis code (OCR label reading, leaf segmentation, morphology classification, treatment prediction) under the MIT License on GitHub; the underlying ORNL image dataset itself is not stated as publicly available.Code · publicSince a pre-trained segmentation model (the SAM) was used in this study, researchers could attempt to build segmentation models fine-tuned to only recognize leaves, which could increase model efficiency and provide more consistent results.
6 Code Availability
All code is publicly available under the MIT License on GitHub here: https://github.com/vivaansinghvi07/smoky-mountain-data-comp .
Acknowledgements
We thank Dr. Ty Frazier at Oak Ridge National Laboratory for his helpful suggestions and mentoring throughout this project.
References
Arya et al. (2022)
Arya, S., Sandhu, K.S.,
Singh, J., Kumar, S.,
2022.
Deep learning: As the new frontier in high-throughput
plant phenotyping.
Euphytica 218Open asset ↗vivaansinghvi07/smoky-mountain-data-complines:272-401Code / 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 confirmedEurope PMC · checked 8 Sept 2026
Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.
Why it matches plant phenotyping methods植物形質の大規模再利用可能データセットを構築し、確率的品質管理と外部情報による検証を実施しており、形質データ基盤が研究の中心である。
abstractHere we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits.
Reproduction assets foundThe paper's core asset is the 'Global Spectrum of Plant Form and Function Dataset' (species mean values for six plant traits plus categorical traits and references), explicitly deposited publicly under a CC-BY license in the TRY File Archive with DOI 10.17871/TRY.81. This is a paper-specific, publicly actionable trait/Dataset · publicThe dataset is available under a CC-BY license at the TRY File Archive (https://www.try-db.org/TryWeb/Data.php):
Díaz, S. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. TRY File
Archive https://doi.org/10.17871/TRY.81 (2022)244Open asset ↗TRY File Archive · 10.17871/TRY.81pdf-page:5 lines:1-62Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The spatial morphological structure of plant leaves is an important index to evaluate crop ideotype. In this study, we characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib, of which 13 phenotypic traits were firstly proposed for featuring plant leaf spatial structure. Cluster analysis results showed that the 13 traits could be divided into four groups, Group I, -II, -III and -IV. Group I contains HorizontalLength, OutwardGrowthMeasure, LeafAngle and DeviationTip; Group II contains DeviationAngle, MaxCurvature and CurvaturePos; Group III contains LeafLength and ProjectionArea; Group IV contains TipTop, VerticalHeight, UpwardGrowthMeasure, and CurvatureRatio. To investigate the genetic basis of the ear leaf midrib curve, 13 traits with high repeatability were subjected to genome-wide association study (GWAS) analysis. A total of 828 significantly related SNPs were identified and 1365 candidate genes were annotated. Among these, 29 candidate genes with the highest significant and multi-method validation were regarded as the key findings. In addition, pathway enrichment analysis was performed on the candidate genes of traits to explore the potential genetic mechanism of leaf midrib curve phenotype formation. These results not only contribute to further understanding of maize leaf spatial structure traits but also provide new genetic loci for maize leaf spatial structure to improve the plant type of maize varieties.
Why it matches plant phenotyping methodsトウモロコシ葉脈の3Dデジタル化により空間形態の表現型形質を取得・定量化することが研究の中心であり、GWASだけでなく新規形質の抽出を扱っている。
abstractwe characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib
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 confirmedEurope PMC · Crossref · checked 8 Sept 2026
Objectives A well-known drawback to the implementation of Convolutional Neural Networks (CNNs) for image-recognition is the intensive annotation effort for large enough training dataset, that can become prohibitive in several applications. In this study we focus on applications in the agricultural domain and we implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves, which can be used as a virtually unlimited dataset to train or validate specialized CNN models or other image-recognition algorithms. Methods Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. Second, a translation via Pix2pix framework based on conditional generator adversarial networks (cGANs) reproduces the color distribution of the leaf surface, by preserving the underneath venation pattern and leaf shape. Results The L2L algorithm generates synthetic images of leaves with meaningful and realistic appearance, indicating that it can significantly contribute to expand a small dataset of real images. The performance was assessed qualitatively and quantitatively, by employing a DL anomaly detection strategy which quantifies the anomaly degree of synthetic leaves with respect to real samples. Finally, as an illustrative example, the proposed L2L algorithm was used for generating a set of synthetic images of healthy end diseased cucumber leaves aimed at training a CNN model for automatic detection of disease symptoms. Conclusions Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples. Our focus was to dispose of synthetic leaves images for smart agriculture applications but, more in general, they can serve for all computer-aided applications which require the representation of vegetation. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.
Why it matches plant phenotyping methods植物葉画像を生成し、葉形状・葉脈・表面色を再現する画像生成手法を開発・評価しており、植物フェノタイピング関連の画像解析ワークフローが中心である。
abstractwe implement Deep Learning (DL) techniques for the automatic generation of meaningful synthetic images of plant leaves
Reproduction assets foundThe paper explicitly states that the authors' code and data for the Leaf2Leaf generative leaf-image synthesis pipeline are publicly available on GitHub, which is a paper-specific, actionable asset.Code · publicData Availability: The code and data for reproducibility are available on GitHub ( https://github.com/AleBenfe/Leaf2Leaf ).Open asset ↗AleBenfe/Leaf2Leaflines:135-147Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The leaf epidermis is the interface between a plant and its environment. The epidermis is highly variable in morphology, with links to both phylogeny and environment, and this diversity is relevant to several fields, including physiology, functional traits, palaeobotany, taxonomy and developmental biology. Describing and measuring leaf epidermal traits remains challenging. Current approaches are either extremely labour-intensive and not feasible for large studies or limited to measurements of individual cells. Here, we present a method to characterise individual cell size, shape (including the effect of neighbouring cells) and arrangement from light microscope images. We provide the first automated characterisation of cell arrangement (from traced images) as well as multiple new shape characteristics. We have implemented this method in an R package, epidermalmorph, and provide an example workflow using this package, which includes functions to evaluate trait reliability and optimal sampling effort for any given group of plants. We demonstrate that our new metrics of cell shape are independent of gross cell shape, unlike existing metrics. epidermalmorph provides a broadly applicable method for quantifying epidermal traits that we hope can be used to disentangle the fundamental relationships between form and function in the leaf epidermis.
Why it matches plant phenotyping methods葉の表皮細胞の形状・配置などの植物形質を顕微鏡画像から自動定量する手法とRパッケージを開発しており、方法が研究の中心である。
abstractHere, we present a method to characterise individual cell size, shape (including the effect of neighbouring cells) and arrangement from light microscope images.
Reproduction assets foundThe paper's authors publicly released their analysis code, the epidermalmorph R package, on GitHub with documentation and tutorials, including example data ('podocarps') in the package. The expanded phenotype dataset is not yet available (request_only), but the code asset qualifies as public and paper-specific.Code · publicThe R package (including installation instructions) is on GitHub ( https://github.com/matildabrown/epidermalmorph ) with accompanying documentation and tutorials ( https://matildabrown.github.io/epidermalmorph/ ). Example data are available in the R package as the dataset ‘ podocarps ’. Figs 7 and 8 are examples of the output produced by this package; the expanded dataset used to generate these figures forms part of a forthcoming study (exOpen asset ↗matildabrown/epidermalmorphlines:188-571Code · publicThe R package (including installation instructions) is on GitHub ( https://github.com/matildabrown/epidermalmorph ) with accompanying documentation and tutorials ( https://matildabrown.github.io/epidermalmorph/ ). Example data are available in the R package as the dataset ‘ podocarps ’. Figs 7 and 8 are examples of the output produced by this package; the expanded dataset used to generate these figures forms part of a forthcoming study (expected publication in early 2023) and will be made available with this future paper. Please contaOpen asset ↗lines:188-571Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. Methods We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. Results The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28. Discussion These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.
Why it matches plant phenotyping methodsArabidopsis幼苗のロゼット面積という植物形質を画像から推定するPython画像処理ワークフローを開発・検証しており、表現型取得手法が研究の中心である。
abstractWe developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes.
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 workflows for each image set can be found at https://github.com/massivejords/Agar-plate-leaf-area as Jupyter Notebooks (Kluyver et al., 2016 ), along with the batch analysis Python script used to process the large image set.Open asset ↗massivejords/Agar-plate-leaf-arealines:85-97Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers ( Capsicum annuum var. annuum ); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R 2 values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses.
Why it matches plant phenotyping methods作物の生体重と葉面積を非破壊推定する計測システムの開発が中心であり、画像とCNNによる形質抽出および検証を行っている。
abstractTherefore, a non-destructive measurement method with high versatility is essential.
Reproduction assets foundThe paper's Supplementary Materials (hosted by MDPI at the allowed URL) explicitly contain sample crop images used as phenotyping inputs, trained-model validation results, model architectures, training parameters, and leaf-area regression coefficients — directly reproducing this paper's fresh-weight and leaf-area phenySupplement · publicowth can be found in the raw data containing changes in the image and weight. Therefore, a monitoring system that can collect both factors can be widely applied for data analyses, such as machine learning, crop modeling, and data standardization.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s22207728/s1 , Figure S1: Sample images collected from the camera. Images were cropped and resized into 128 × 128, and the resized images were augmented using flipping and shifting; Figure S2: Validation accuracies of the trained deep learning models for estimating the calculated fresh weight.; Figure S3: Validation accuracy of theOpen asset ↗lines:129-147Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Despite substantial variation in leaf vein architectures among angiosperms, a typical hierarchical network pattern is shared within clades. Functional demands constrain the network structure of leaf venation, generating a biased distribution in the morphospace. Although network structures and their diversity are crucial for understanding angiosperm venation, previous studies have relied on simple morphological measurements (e.g., length, diameter, branching angles, and areole area) and their derived statistics to quantify phenotypes. In this study, we developed a simple, high-throughput phenotyping workflow for the quantification of vein networks and identified leaf venation-specific morphospace patterns. The proposed method involves four processes: leaf image acquisition using a feasible system, leaf vein segmentation based on a deep neural network model, network extraction as an undirected graph, and network feature calculation. To demonstrate the proposed method, we applied it to images of non-chemically treated leaves of five species for classification based on network features alone, with an accuracy of 90.6%. By dimensionality reduction, a one-dimensional morphospace, along which venation shows variation in loopiness, was identified for both untreated and cleared leaf images, suggesting that patterns of venation are determined by a functional trade-off. The proposed network feature-based method is a useful morphological descriptor, providing a quantitative representation of the topological aspects of venation and enabling inverse mapping to leaf vein structures. Accordingly, our approach is promising for analyses of the functional and structural properties of veins. Author Summary Leaf venation exhibits diverse network structures among taxa and conservation within taxa, reflecting complex evolutionary processes involving functional, developmental, and structural constraints. We used network features to characterize hierarchical and complex venation patterns. We analyzed 479 non-chemically treated leaves of five species and demonstrated that network features contain sufficient information for species classification. Furthermore, we identified biased distribution patterns in the leaf venation morphospace by characterizing leaf samples from both untreated and cleared leaf images. These results improve our understanding of morphological constraints and functional trade-offs shaping divergence in leaf venation. Our approach provides a basis for similar analyses in various fields targeting reticulate networks, which are ubiquitous in nature, including biomimetics, generative design, and microfluidics.
Why it matches plant phenotyping methods葉画像から葉脈ネットワークを抽出し、形態特徴を定量化する高スループット表現型解析ワークフローを開発・実証しており、フェノタイピング手法が中心である。
abstractwe developed a simple, high-throughput phenotyping workflow for the quantification of vein networks
Reproduction assets foundThe paper's cleared-leaf phenotyping analysis is built directly on the public NMNS Cleared Leaf Database (4,095 images; 328 filtered for the cleared leaf dataset and 20 high-resolution images for U-Net training). No author code, trained model checkpoints, or untreated-leaf dataset deposit is described with a public URLDataset · publict. (E) The tiled images were converted to grayscale images. (F) The grayscale
images were segmented using the trained U-Net.
124
125 Cleared leaf image dataset
126 Two datasets were created based on 4,095 cleared leaf images from the National Museum of
127 Nature and Science (NMNS) Cleared Leaf Database (NMNS, Tokyo, Japan;
128 https://www.kahaku.go.jp/research/db/geology-paleontology/cleared_leaf) [35]. To generate vein
129 images for training the DNN-based semantic segmentation model, 20 high-resolution images
130 were obtained (high-quality dataset; Fig 2).
131 To compare the results for the untreated leaf dataset with those for cleared leaves, images of
132 the genus corresponding to theOpen asset ↗pdf-layout-page:11 lines:1-62Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Three-dimensional (3D) laser point cloud technology is an important research method in the field of agricultural remote sensing research. The collection and processing technology of terrestrial light detection and ranging (LiDAR) point cloud of crops has greatly promoted the integration of agricultural informatization and intelligence. In a smart farmland based on 3D modern agriculture, the manager can efficiently and conveniently achieve the growth status of crops through the point cloud collection system and processing model integrated in the smart agricultural system. To this end, we took field maize as the research object in this study and processed four sets of field maize point clouds, named Maize-01, Maize-02, Maize-03, and Maize-04, respectively. In this research, we established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core, and four groups of field maize were used as research objects. Among them, the value of the overall accuracy (OA) index, which was used to evaluate the comprehensive performance of the model, were 0.98, 0.97, 0.95, and 0.94. Secondly, the multi-condition identification method was used to separate different maize organ point clouds from the individual maize point cloud. In addition, the organ stratification model of field maize was established. In this organ stratification study, we take Maize-04 as the research object and obtained the recognition accuracy rates of four maize organs: tassel, stalk, ear, and leaf at 96.55%, 100%, 100%, and 99.12%, respectively. We also finely segmented the leaf organ obtained from the above-mentioned maize organ stratification model into each leaf individual again. We verified the accuracy of the leaf segmentation method with the leaf length as the representative. In the linear analysis of predicted values of leaf length, R2 was 0.73, RMSE was 0.12 m, and MAE was 0.07 m. In this study, we examined the segmentation of individual crop fields and established 3D information interpretations for crops in the field as well as for crop organs. Results visualized the real scene of the field, which is conducive to analyzing the response mechanism of crop growth and development to various complex environmental factors.
Why it matches plant phenotyping methods圃場トウモロコシのLiDAR点群から個体・器官・葉を分割し、葉長を推定・検証する手法が研究の中心であり、再利用可能な植物表現型抽出ワークフローに該当する。
abstractwe established a field individual maize segmentation model with the density-based clustering algorithm (DBSCAN) as the core
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' PCL/C++ segmentation and stratification code at a public GitHub repository matching an allowed URL. The maize point cloud dataset is also stated to be online at scidb.cn, but the full dataset URL does not exactly match any allowed_urls entry, so aCode · publicThe main PCL/C++ code in this paper are available online at
https://github.com/1117ismore/HZAU-Segmentation-Stratification-pcd.gitOpen asset ↗HZAU-Segmentation-Stratification-pcdpdf-page:18 lines:1-56Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Why it matches plant phenotyping methodsUAVおよびSentinel-2マルチスペクトル画像から草丈、LAI、バイオマス、葉窒素などの植物形質を推定し、参照データとの相関比較・技術評価を行っているため、植物フェノタイピング手法の検証・応用が中心です。
abstractprospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/rs14174426/s1, Table S1: Summary statistics of the plant trait
variables measured at 20 sample points in field A; Table S2: Summary statistics of the plant trait
variables measured at 20 sample points in field G; Table S3: Absolute correlation results of plant
maximum height and mean height in field AOpen asset ↗pdf-page:20 lines:1-58Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Growth traits, such as fresh weight, diameter, and leaf area, are pivotal indicators of growth status and the basis for the quality evaluation of lettuce. The time-consuming, laborious and inefficient method of manually measuring the traits of lettuce is still the mainstream. In this study, a three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed. The TMSCNet consisted of five models, of which two master models were used to preliminarily estimate the fresh weight (FW), dry weight (DW), height (H), diameter (D), and leaf area (LA) of lettuce, and three auxiliary models realized the automatic correction of the preliminary estimation results. To compare the performance, typical convolutional neural networks (CNNs) widely adopted in botany research were used. The results showed that the estimated values of the TMSCNet fitted the measurements well, with coefficient of determination ( R 2 ) values of 0.9514, 0.9696, 0.9129, 0.8481, and 0.9495, normalized root mean square error (NRMSE) values of 15.63, 11.80, 11.40, 10.18, and 14.65% and normalized mean squared error (NMSE) value of 0.0826, which was superior to compared methods. Compared with previous studies on the estimation of lettuce traits, the performance of the TMSCNet was still better. The proposed method not only fully considered the correlation between different traits and designed a novel self-correcting structure based on this but also studied more lettuce traits than previous studies. The results indicated that the TMSCNet is an effective method to estimate the lettuce traits and will be extended to the high-throughput situation. Code is available at https://github.com/lxsfight/TMSCNet.git.
Why it matches plant phenotyping methodsRGB・深度画像からレタスの複数形質を推定する新規ネットワークを開発し、既存手法と性能比較しており、植物フェノタイピング手法が研究の中心である。
abstracta three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed
Reproduction assets foundThe paper uses the public Autonomous Greenhouses Challenge 3 dataset (RGB/depth lettuce images with FW/DW/H/D/LA measurements) and states author code availability on GitHub.Code · publicCode is available at https://github.com/lxsfight/TMSCNet.git .Open asset ↗github.com/lxsfight/TMSCNetlines:1-41Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination ( R 2 ) of 0.938 over the entire lettuce growth period. The experiment results demonstrated that the multi-modal fusion method could improve the fresh weight estimation performance by leveraging the advantages of empirical geometric traits and deep neural features simultaneously.
Why it matches plant phenotyping methodsRGB-D画像からレタスの生体重を非破壊推定する画像解析・深層学習手法の開発が研究の中心であり、植物表現型取得法に該当する。
abstractA lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits.
Reproduction assets foundThe paper's phenotyping inputs (top-view RGB and aligned depth images of 388 lettuces with destructively measured traits) come from the publicly available 3rd Autonomous Greenhouse Challenge Online Challenge Lettuce Images dataset, with an explicit public URL in the data availability statement. No author analysis code,Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.4tu.nl/articles/dataset/3rd_Autonomous_Greenhouse_Challenge_Online_Challenge_Lettuce_Images/15023088 .Open asset ↗data.4tu.nl · 15023088lines:657-691Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.
Why it matches plant phenotyping methodsクロロフィル蛍光データを用いたフェノミック予測モデルを構築・比較し、成長形質の予測性能と条件間・集団間の転移性を評価しているため、植物フェノタイピング手法が中心である。
abstractThe models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors.
Reproduction assets foundThe paper's Data availability statement explicitly states that all code and datasets (including the ChlF phenomic data and growth-trait phenotypes used for GP/PP modeling) are freely available at the authors' public GitHub repository https://github.com/alainmbebi/GP-PP, which matches an allowed URL. Other URLs (BGLR CRCode · publicr and is an excellent proxy for photosynthesis in coffee, making it a tool of choice for assessing the vigor of a genotype, which the present study tends to prove.
Data availability
We implemented all statistical models using R programming language; the codes and all data sets used in the current study are freely available from https://github.com/alainmbebi/GP-PP .
Supplemental material is available at G3 online.
Supplementary Material
jkac170_Supplementary_Data_File_S1
Click here for additional data file.
jkac170_Supplementary_Data_File_S2
Click here for additional data file.
Acknowledgments
We would like to thank the 2 anonymous reviewers for their suggestions and comments.
Funding
ThOpen asset ↗alainmbebi/GP-PPlines:876-910Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.
Why it matches plant phenotyping methodsRGB-D画像とCNNを用いてレタスの複数の生育形質を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractA convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera.
Reproduction assets foundThe paper's phenotyping inputs (388 RGB-D lettuce image pairs with destructive growth-index measurements from the Third Autonomous Greenhouse Challenge) are a third-party public dataset explicitly stated to be publicly available at 4TU.ResearchData, with the DOI 10.4121/15023088.v1 cited in the text and figure captionsDataset · publicThe dataset is available in online: https://doi.org/10.4121/15023088.v1 [ 30 ].Open asset ↗10.4121/15023088.v1lines:518-697Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と複数の回帰モデルを用いて植物群落形質を推定し、手法性能を比較評価しているため、形質取得・推定法が研究の中心である。
abstractwe compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicFigure S2: The UAV hyperspectral image used
for mapping plant community traits. The upper one is the raw image and the lower one is the
corrected image shown in true colour composites.Open asset ↗pdf-page:12 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
Why it matches plant phenotyping methods衛星リモートセンシングによるLAI・NDVIという植物キャノピー形質の推定を、APSIM制約のためのエミュレーションおよびベイズ最適化ワークフローとして技術的に評価しており、形質取得・抽出法が中心的です。
abstractWe leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest.
Reproduction assets foundThe paper uses a publicly available maize yield dataset from Beck's Hybrids covering 332 locations (2014-2019) with management, soil, and weather information as site-level inputs for APSIM simulations and yield validation. No author analysis code, trained models, or data deposit is disclosed (Data Availability and AcknDataset · public4 of 25
Figure 1. 2 Figures side by side
million ha from 2014-2019 [31]. To perform APSIM simulations at a series of randomly 143
selected locations, site-level information was acquired from the publicly available maize 144
yield dataset maintained by Beck’s Hybrids (https://www.beckshybrids.com/Research/ 145
Yield-Data). The dataset included information on management operations (i.e., planting 146
date, harvesting date, plant population, row spacing, and previous crop planted for residue 147
type), soil, and weather for 332 locations from 2014 to 2019 (Figure 2(a)). Information on 148
soil texture and soil organic carbon (SOC)Open asset ↗pdf-raw-page:4 lines:1-19Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy ( R 2 >0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy ( R 2 =0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Why it matches plant phenotyping methods葉の反射スペクトルから構造・化学的形質を推定する分光センシングとPLSRモデルを構築し、外部データで検証しており、植物形質取得法が研究の中心です。
abstractReflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits
Reproduction assets foundThe paper's fresh-leaf spectral data are publicly available via the CABO data portal, and the authors' analysis scripts are on GitHub. EcoSIS/EcoSML uploads are promised only upon publication and are not yet actionable.Dataset · publicected and curated the spectral and trait data. SK analyzed the data,
597 interpreted the results, and wrote the first draft with substantial contributions from EL. All authors
598 contributed to further revisions of the paper.
599
600 Data availability
601 All fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).
602 Upon publication, we will also upload all spectral data, as well as metadata and trait data, to theOpen asset ↗pdf-layout-page:38 lines:1-58Code · publicnder a CC-BY 4.0 International license.
603 Ecological Spectral Information System (EcoSIS, https://ecosis.org/), and upload models to the
604 Ecological Spectral Model Library (EcoSML, https://ecosml.org/). At that stage, we will update this
605 section accordingly. Analysis scripts are available as a repository on GitHub
606 (https://github.com/ShanKothari/CABO-trait-models).Open asset ↗ShanKothari/CABO-trait-modelspdf-layout-page:39 lines:1-14Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
Why it matches plant phenotyping methods植物画像からのセグメンテーションと葉数推定を中核とする深層学習フェノタイピング手法の開発であり、植物形質の自動抽出性能も評価しているため。
abstractwe focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework
Reproduction assets foundThe paper's experiments use the public CVPPP 2017 Leaf Counting Challenge dataset (Arabidopsis and tobacco plant images with segmentation masks and leaf counts), which the authors explicitly link in the data availability statement. No author code or trained models are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017 .Open asset ↗CVPPP2017 · CVPPP2017lines:527-601Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
Why it matches plant phenotyping methods葉面ハイパースペクトル反射から光合成・葉形質を推定するモデルを構築し、精度評価と集団への適用を行っており、表現型取得・推定手法が研究の中心である。
abstractwe used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits
Reproduction assets foundThe paper's leaf reflectance processing code (FieldSpec R Package, Zenodo DOI 10.5281/zenodo.6248237) and its paper-specific phenotype/reflectance data, PLSR model coefficients, and complete genetic mapping dataset are publicly available via the Genetics figshare supplemental repository (DOI 10.25386/genetics.19394693)Dataset · publicgenetic mapping and
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analyses can be found in File S18. The majority of lines and accessions used in this manuscript
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are available via GRIN (https://www.ars-grin.gov/) or by request from Soybase.org for the NAM
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lines (https://soybase.org/SoyNAM/SoyNAM_RIL_request.htm). Supplemental Material
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available at figshare: https://doi.org/10.25386/genetics.19394693
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Acknowledgements
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We thank Troy Cary, Chris Moller, and Noah Mitchell for help in setting up and maintaining the
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experimental plots, collecting data, and processing samples.
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Funding
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This work was supported by soybean checkoff funding from the United Soybean Board. ASS
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was supported by a post-doctoraOpen asset ↗figshare · 10.25386/genetics.19394693pdf-raw-page:39 lines:1-49Code / dataset availability confirmedbioRxiv · Europe PMC · checked 8 Sept 2026
O_LIMore than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. C_LIO_LIUsing 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. C_LIO_LIOur pressed-leaf models were best at estimating LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514-0.790; %RMSE = 12.8-19.6). Remaining elements were predicted poorly (R2 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. C_LIO_LIThese results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the worlds preserved plant collections. C_LI
Why it matches plant phenotyping methods押葉の反射分光から葉の機能形質を非破壊推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractwe used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits
Reproduction assets foundThe paper's fresh-leaf spectral dataset is publicly available via the CABO data portal. Pressed/ground spectra, trait data, and PLSR/PLS-DA models are only promised 'upon publication' to EcoSIS and EcoSML, so those require contacting the authors; no author analysis code with a public URL is stated.Dataset · publicAll fresh-leaf spectral data are available through the CABO data portal (https://data.caboscience.org/leaf).Open asset ↗CABO data portalpdf-page:24 lines:1-32Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Sorghum, a genetically diverse C 4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation ( V cmax ), phosphoenolpyruvate (PEP) carboxylation ( V pmax ), and electron transport ( J max ), quantified using a C 4 photosynthesis model, were evaluated in two field-grown training sets ( n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax , V pmax , J max , SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 ( n = 875 plots including 650 genotypes) and 2020 ( n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max . Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se . J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
Why it matches plant phenotyping methodsトラクター搭載ハイパースペクトルセンシングとPLSRにより光合成関連形質を推定し、独立試験でモデル性能を評価している。形質取得法の開発・検証と大規模スクリーニングへの応用が中心である。
abstractPartial least square regression (PLSR) was used to predict V cmax ( R 2 = 0.83), V pmax ( R 2 = 0.93), J max ( R 2 = 0.76), SLN ( R 2 = 0.82), and LMA ( R 2 = 0.68) from tractor-based hyperspectral sensing.
Reproduction assets foundThe authors state that all phenotypic data used to develop the PLSR models (ground truth Vcmax, Vpmax, Jmax, SLN, LMA and associated hyperspectral measurements) is publicly available via a UQ eSpace DOI. Genotypic marker data is only available upon request and is not a phenotyping asset. No author analysis code or URLsDataset · publicAll phenotypic data used to develop the models presented in this manuscript is available here: https://doi.org/10.48610/acbe0df .Open asset ↗10.48610/acbe0dflines:488-522Code / dataset availability 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 confirmedEurope PMC · checked 14 Sept 2026
The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals ( R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.
Why it matches plant phenotyping methods衛星データから作物形質を推定するGPR retrievalモデルと、GEE上での実装・検証ワークフローが研究の中心であり、植物形質フェノタイピング手法に該当する。
abstractwe implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe GEE codes to run the EBD-GPR models and display the vegetation maps of this study is hosted on the repository https://github.com/esjoal/GEE_GPR_mapping_vegetation .Open asset ↗esjoal/GEE_GPR_mapping_vegetationlines:222-231Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.
Why it matches plant phenotyping methodsSARデータとGPRモデルにより作物のPAI・VWC・湿重量バイオマスを推定する手法を開発・評価しており、植物形質取得が研究の中心である。
abstractThe purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the authors' GPR analysis code for crop biophysical parameter retrieval from RADARSAT-2 data. The in situ SMAPVEX16-MB measurements and RADARSAT-2 imagery themselves are not stated as publicly released by the authors.Code · publicData Availability Statement: The code for the present work is available at: https://github.com/
Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2, accessed 15 February 2022.Open asset ↗Swarnendu-sekhar-ghosh/GPR_biophysical_parameter_retrieval_RS2pdf-page:24 lines:1-60Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
BACKGROUND: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy. RESULTS: A dataset of [Formula: see text] 13,600 leaf images from 27 genotypes of Cotton was generated. Images were collected from leaves at two different positions in the canopy (leaf 3 & leaf 4), from genotypes grown in two consecutive years and in two growth environments (glasshouse & field). This dataset was used to build a 4-part deep learning model called HairNet. On the whole dataset, HairNet achieved accuracies of 89% per image and 95% per leaf. The impact of leaf selection, year and environment on HairNet accuracy was then investigated using subsets of the whole dataset. It was found that as long as examples of the year and environment tested were present in the training population, HairNet achieved very high accuracy per image (86-96%) and per leaf (90-99%). Leaf selection had no effect on HairNet accuracy, making it a robust model. CONCLUSIONS: HairNet classifies images of cotton leaves according to their hairiness with very high accuracy. The simple imaging methodology presented in this study and the high accuracy on a single image per leaf achieved by HairNet demonstrates that it is implementable at scale. We propose that HairNet replaces the current visual scoring of this trait. The HairNet code and dataset can be used as a baseline to measure this trait in other species or to score other microscopic but important phenotypes.
Why it matches plant phenotyping methods綿花葉の毛茸という植物形質を対象に、画像取得法と深層学習モデルHairNetを開発・精度評価しており、表現型取得手法が研究の中心である。
abstractCurrently, this key phenotype is measured visually which is slow, laborious and operator-biased. Here, we propose a simple, high-throughput and low-cost imaging method combined with a deep-learning model, HairNet, to classify leaf images with great accuracy.
Reproduction assets foundThe paper publicly releases its de-identified cotton leaf hairiness image dataset (~13,600 leaf images, 27 genotypes) via the CSIRO data access portal and the HairNet analysis code via a public Bitbucket repository, both with explicit availability statements and URLs.Dataset · publicThe HairNet image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:242-283Code · publicThe HairNet code is available at https://bitbucket.csiro.au/scm/sth/hairnet.git .Open asset ↗bitbucket.csiro.au/scm/sth/hairnetlines:242-283Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.
Why it matches plant phenotyping methods屋内ハイスループット表現型解析、画像処理、機械学習を用いて作物形質を抽出・予測し、プラットフォーム性能も評価しているため、方法が研究の中心である。
abstractA pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits
Reproduction assets foundThe paper's full LemnaTec HTP image dataset (RGB, NIR, FLUOR, HYP images of 50 B. napus founder lines) is openly available at the authors' P2IRC USask repository, directly reproducing this paper's phenotyping measurements. The genomevis tool concerns SNP/genotype visualization, not phenotyping, and no analysis code is,Dataset · publicThe full image dataset is openly available at https://p2irc-data-dev.usask.ca/dataset/10.1109.SciDataManager.2020.7284788 (Dataset name: P2IRC Flagship 1 Data).Open asset ↗P2IRC Flagship 1 Data · 10.1109.SciDataManager.2020.7284788lines:323-329Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Why it matches plant phenotyping methods植物のLAI・バイオマス等の状態観測をリモートセンシングとデータ同化で作物モデルへ統合し、収量推定性能を評価する計算・計測ワークフローが研究の中心であるため、植物表現型計測手法として収載する。
abstractthe assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
Reproduction assets foundThe paper's field validation dataset (wheat/soil state observations from the 2018/19 Cora Lynn experiment) is openly available on the authors' PRISM (Monash) site, and the authors' APSIM-EnKF data assimilation source code is explicitly stated to be publicly available on GitHub. Weather data sources (BoM, Weather UndergDataset · publicThe field validation data
presented in this study are openly available in the P-band Radiometer Inferred Soil Moisture (PRISIM)
website at https://www.prism.monash.edu/index.htmlOpen asset ↗pdf-page:19 lines:1-59Code · publicThe APSIM-EnKF data assimilation framework used in this study was the version
developed and described by Zhang [28] (source code available on https://github.com/Open asset ↗pdf-page:3 lines:1-53Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging (LiDAR) provides a new way to characterize three-dimensional (3D) plant structure, there is a need to develop robust algorithms for extracting 3D phenotypic traits from LiDAR data to assist in gene identification and selection. Accurate 3D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial LiDAR data. We describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the PointCNN model and obtains stem instances by fitting 3D cylinders to the points. It then segments the field LiDAR point cloud into individual plants using local point densities and 3D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs (F-score =0.8207) and plants (F-score =0.9909). The effectiveness of terrestrial LiDAR for phenotyping at organ (including leaf area and stem position) and individual plant (including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position (position error =0.0141 m), plant height (R2 >0.99), crown width (R2 >0.90), and leaf area (R2 >0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.
Why it matches plant phenotyping methodsLiDARとCNNを用いてトウモロコシの器官・個体を分割し、葉面積、茎位置、草丈、樹冠幅などの表現型形質を抽出する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractWe describe a two-stage method that combines both convolutional neural networks (CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments.
Reproduction assets foundThe authors explicitly state that the implementation code and test data for the maize LiDAR segmentation/phenotyping method are publicly available on GitHub at the sysu-xin-lab/Corn_segmentation repository, which is an allowed URL.Code · publicated that the proposed method extracts accurate
information for high-throughput phenotyping from terrestrial
LiDAR data and provides helpful information for potential analysis
of the relationship between genotypes, environmental conditions
and phenotypes. The implementation code and test data may be
publicly accessed in GitHub (https://github.com/sysu-xin-lab/Corn_segmentation). We welcome researchers and scholars to fur-
ther evaluate and improve the proposed method.
CRediT authorship contribution statement
Zurui Ao: Methodology, Investigation, Writing – original draft.
Fangfang Wu: Investigation. Saihan Hu: Investigation. Ying Sun:
Methodology. Yanjun Su: Methodology. Qinghua Guo: MethodolOpen asset ↗sysu-xin-lab/Corn_segmentationpdf-raw-page:10 lines:1-83Code / 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
Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
Why it matches plant phenotyping methods小麦のハイパースペクトル反射から生理・光合成形質を推定する深層学習モデルを開発・比較し、精度を検証した研究であり、表現型取得・推定法が中心である。
abstractHere we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets.
Reproduction assets foundThe paper publicly releases its authors' model code (GitHub) and hosts the training data and pre-trained models via the Wheat Physiology Predictor web server. The SAMS repository is a generic third-party tool and is excluded.Code · publicThe full code of these models is located at https://github.com/ashwhall/hyperspec-trait-prediction .Open asset ↗ashwhall/hyperspec-trait-predictionlines:132-148Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.
Why it matches plant phenotyping methods葉の反射スペクトルを用いて、近縁植物の種・集団構造・雑種を高精度に識別し、遺伝的多様性を推定する測定・解析手法が研究の中心であるため。
abstractClassification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy.
Reproduction assets foundThe paper's leaf reflectance spectra (the core phenotyping measurements) are publicly deposited on figshare, and the authors' R code for spectral analysis is on GitHub, both stated in the Data availability section. The NCBI BioProject is genomic data and excluded.Code · publicThe R code for spectral analysis is available at https://github.com/LanceStasinski/Dryas2 .Open asset ↗GitHub · LanceStasinski/Dryas2lines:155-197Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x . The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap-the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range-that was previously normalised-a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).
Why it matches plant phenotyping methods植物の葉数という形態形質を画像から推定する計算手法を開発し、種内・種間で性能評価している。植物課題は複数実験の一部だが、ドメイン適応と葉数カウントの技術評価が明示されており、単なるルーチン測定ではない。
abstractWe propose an approach tackling both covariate and label gap in a unified training framework.
Reproduction assets foundThe paper's authors explicitly state their implementation code is publicly available on GitHub; this is the authors' analysis code for the leaf/cell/pedestrian counting domain adaptation experiments. The CVPPP (Zenodo) and other datasets are cited prior-work datasets, not paper-specific assets.Code · publicOur code is available at https://github.com/MattiaLitrico/Semi-supervised-Domain-Adaptation-for-Holistic-Counting-under-Label-Gap (accessed on 20 September 2021).Open asset ↗MattiaLitrico/Semi-supervised-Domain-Adaptation-for-Holistic-Counting-under-Label-Gaplines:104-121Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Leaf mass per area (LMA) is a key plant functional trait closely related to leaf biomass. Estimating LMA in fresh leaves remains challenging due to its masked absorption by leaf water in the short-wave infrared region of reflectance. Vegetation indices (VIs) are popular variables used to estimate LMA. However, their physical foundations are not clear and the generalization ability is limited by the training data. In this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation. The relationship between LMA and VIs was constructed using PROSPECT-D model simulations. The three-VI space constituting a 3D matrix was divided into cubical cells and LMA values were assigned to each cell. Then, the 3D matrix retrieves LMA through the three VIs calculated from observations. Two 3D matrices with different VIs were established and validated using a second synthetic dataset, and two comprehensive experimental datasets containing more than 1400 samples of 49 plant species. We found that both 3D matrices allowed good assessments of LMA (R2 = 0.76 and 0.78, RMSE = 0.0016 g/cm2 and 0.0017 g/cm2, respectively for the pooled datasets), and their results were superior to the corresponding single Vis, 2D matrices, and two machine learning methods established with the same VI combinations.
Why it matches plant phenotyping methods植物機能形質LMAを可視・近赤外観測から推定する3D植生指数行列を開発し、合成データおよび大規模実験データで検証しており、表現型取得手法が中心である。
abstractIn this study, we proposed a hybrid approach by establishing a three-dimensional (3D) VI matrix for LMA estimation.
Reproduction assets foundThe paper's two experimental phenotyping datasets (LOPEX leaf spectra/LMA and the Madison, WI leaf spectra dataset) are explicitly stated to be publicly available on EcoSIS with direct URLs in the Data Availability Statement. No author analysis code or trained model is shared.Dataset · publicResearch Funds for the Central Universities, China Uni-
versity of Geosciences, Wuhan (grant number 111-G1323520290). T.T. was funded by SNSA (Dnr
96/16) and the EU-Aid-funded CASSECS project.
Data Availability Statement: All data used in this manuscript are publicly available through EcoSIS
spectral database, including LOPEX (https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-) and MA (https://ecosis.org/package/7433af7d-fbbd-4617-8df4-4d892f0d4357).Acknowledgments: We thank the open access to the LOPEX and MA datasets, as well as the PRO-
SPECT-D model.
Conflicts of Interest: The authors declare no conflict of interest. Authors are aware of and comply
with bestOpen asset ↗EcoSIS · leaf-optical-properties-experiment-database--lopex93-pdf-raw-page:13 lines:1-51Dataset · publicant number 111-G1323520290). T.T. was funded by SNSA (Dnr
96/16) and the EU-Aid-funded CASSECS project.
Data Availability Statement: All data used in this manuscript are publicly available through EcoSIS
spectral database, including LOPEX (https://ecosis.org/package/leaf-optical-properties-experiment-database--lopex93-) and MA (https://ecosis.org/package/7433af7d-fbbd-4617-8df4-4d892f0d4357).Acknowledgments: We thank the open access to the LOPEX and MA datasets, as well as the PRO-
SPECT-D model.
Conflicts of Interest: The authors declare no conflict of interest. Authors are aware of and comply
with best practices in publication ethics specifically about authorship (avoidance of guest authorOpen asset ↗EcoSIS · 7433af7d-fbbd-4617-8df4-4d892f0d4357pdf-raw-page:13 lines:1-51Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background and aims Leaf size has considerable ecological relevance, making it desirable to obtain leaf size estimations for as many species worldwide as possible. Current global databases, such as TRY, contain leaf size data for ~30 000 species, which is only ~8% of known species worldwide. Yet, taxonomic descriptions exist for the large majority of the remainder. Here we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait. Methods Using a global dataset of all major leaf shapes measured on 3125 leaves from 780 taxa, we quantified scaling functions that estimate leaf size as a product of leaf length, width and a leaf shape-specific correction factor. We validated our method by comparing leaf size estimates with those obtained from image recognition software and compared our approach with the widely used correction factor of 2/3. Key results Correction factors ranged from 0.39 for highly dissected, lobed leaves to 0.79 for oblate leaves. Leaf size estimation using leaf shape-specific correction factors was more accurate and precise than estimates obtained from the correction factor of 2/3. Conclusion Our method presents a tractable solution to accurately estimate leaf size when only information on leaf length, width and shape is available or when labour and time constraints prevent usage of image recognition software. We see promise in applying our method to data from species descriptions (including from fossils), databases, field work and on herbarium vouchers, especially when non-destructive in situ measurements are needed.
Why it matches plant phenotyping methods葉長・葉幅・葉形から葉面積を推定する植物形質測定法を開発し、画像認識ソフトウェア等と比較検証しているため、方法が研究の中心である。
abstractHere we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas
Reproduction assets foundThe paper's leaf measurement dataset (3125 leaves, 780 taxa) is stated to be fully contained in the paper's supplementary data (Table S1 with family/species data, plus Tables S2–S3 with correction factors), which the authors state are available online at the journal site. No author analysis code or trained models are披露Supplement · publiccated trait measurements
from easily measurable dimensions. We hope that under-
standing scaling functions of plant dimensions could help to
fill major gaps in knowledge, bringing us closer to a complete
understanding of morphological variation in the world’s plants.
SUPPLEMENTARY DATA
Supplementary data are available online at https://academic.oup.com/aob and consist of the following. Figure S1: Correction
factors for leaf base form, leaf margin, leaf medial symmetry
and leaf size class. Table S1: Family and species name data.
Table S2: Relative difference in leaf size and interquartile range
of leaf size estimated using leaf shape-specific correction fac-
tors and a universal COpen asset ↗pdf-raw-page:11 lines:1-82Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Phosphorus is one of the second most important nutrients for plant growth and development, and its importance has been realised from its role in various chains of reactions leading to better crop dynamics accompanied by optimum yield. However, the injudicious use of phosphorus (P) and non-renewability across the globe severely limit the agricultural production of crops, such as rice. The development of P-efficient cultivar can be achieved by screening genotypes either by destructive or non-destructive approaches. Exploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study. Eighteen genotypes were selected for hydroponic study from the soil-based screening of 68 genotypes to identify the traits through non-destructive (geometric traits by imaging) and destructive (morphology and physiology) techniques. Geometric traits such as minimum enclosing circle, convex hull, and calliper length show promising responses, in addition to morphological and physiological traits. In 28-day-old seedlings, leaves positioned from third to fifth played a crucial role in P mobilisation to different plant parts and maintained plant architecture under P deficient conditions. Besides, a reduction in leaf angle adjustment due to a decline in leaf biomass was observed. Concomitantly, these geometric traits facilitate the evaluation of low P-tolerant rice cultivars at an earlier stage, accompanying several stress indices. Out of which, Mean Productivity Index, Mean Relative Performance, and Relative Efficiency index utilising image-based traits displayed better responses in identifying tolerant genotypes under low P conditions. This study signifies the importance of image-based phenotyping techniques to identify potential donors and improve P use efficiency in modern rice breeding programs.
Why it matches plant phenotyping methods低リン耐性イネの選抜において、画像から幾何学的形質を抽出するイメージベース表現型解析を中心的に適用・評価しており、単なる生物学的実験のルーチン測定ではない。
abstractExploring image-based phenotyping (shoot and root) and tolerant indices in conjunction under low P conditions was the first report, the epicentre of this study.
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 ) were selected based on the level of tolerance from all quarters of principal component analysis (PCA) to evaluate further under hydroponics and identify the traits through destructive (morphology and physiology) and non-destructive (geometric traits by imaging) techniques in a low phosphorus regime.Open asset ↗lines:313-319Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
O_LIProper collection and preparation of empirical data still represent one of the most important, but also expensive steps in ecological and evolutionary/systematic research. Modern machine learning approaches, however, have the potential to automate a variety of tasks, which until recently could only be performed manually. Unfortunately, the application of such methods by researchers outside the field is hampered by technical difficulties, some of which, we believe, can be avoided. C_LIO_LIHere, we present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data. Besides providing a convenient command-line interface to existing software libraries, it comprises several additional tools for data handling, pre- and postprocessing, and building advanced analysis pipelines. C_LIO_LIWe demonstrate the application of GinJinn2 for biological purposes using four exemplary analyses, namely the evaluation of seed mixtures, detection of insects on glue traps, segmentation of stomata, and extraction of leaf silhouettes from herbarium specimens. C_LIO_LIGinJinn2 will enable users with a primary background in biology to apply deep learning-based methods for object detection and segmentation in order to automate feature extraction from image data. C_LI
Why it matches plant phenotyping methods植物画像から種子、気孔、葉形状などを抽出する深層学習ツールを開発・提示しており、植物表現型取得のためのソフトウェアが中心である。
abstractwe present GinJinn2, a user-friendly toolbox for deep learning-based object detection and instance segmentation on image data.
Reproduction assets foundThe paper's GinJinn2 source code and manual are explicitly stated to be freely available on the authors' GitHub repository. The annotated Seeds, Yellow-sticky-traps, Leucanthemum, and stomata annotation datasets are only promised via GfBio 'will be supplied as soon as available', so they are not yet actionable public; Code · public, and wrote the manuscript. Both authors approved the final version of the
379 manuscript. We further note that UL and TO contributed equally to this work. The
380 order of their names in the author list was decided by coin toss.
381
382 Data availability
383 GinJinn2’s source code and manual are freely available at GitHub
384 (https://github.com/AGOberprieler/GinJinn2). The annotated Seeds, Yellow-sticky-
385 traps and Leucanthemum datasets are hosted by the German Federation for
386 Biological Data (GfBio; Link A, Link B, Link C; will be supplied as soon as available).
387 The images used for the Stomata analysis are hosted by the Cuticle Database
388 (Barclay et al., 2012), a Python scripOpen asset ↗https://github.com/AGOberprieler/GinJinn2pdf-raw-page:15 lines:1-35Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
ArabidopsisMaizeLiDAR / point cloudRGB / grayscaleLeafWhole plant / canopy / plot / fieldClassificationMorphology / geometry measurementSegmentationGrowth / development / phenology
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.
Why it matches plant phenotyping methodsRoADはロボット、RGBカメラ、レーザープロフィロメータ、画像処理による植物形質抽出を中核とする自動フェノタイピングシステムであり、方法開発と実証が主目的です。
abstractwe developed the Robotic Assay for Drought (RoAD) system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' Arabidopsis image-processing source code (the pipeline that produced the paper's phenotypic trait measurements) on GitHub, making it a paper-specific, publicly actionable analysis code asset. No public phenotype dataset or image deposit is stated;Code · publicMN, ME, YY, YB, LT, SHH, and JWW. Funding
acquisition, YY, LT, JWW, and SHH.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
All relevant data can be found within the manuscript and
its supporting materials. The source code for Arabidopsis
image processing is available on GitHub at https://github.com/lr-xiang/RoAD-image-processing.SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. PCZ and BRZ responses of Arabidopsis accessions.
Figure S2. Drought responses in Arabidopsis using RoAD end-
point drought mode.
Figure S3. Validation results for maize plants.
Figure S4. ComparisonOpen asset ↗lr-xiang/RoAD-image-processingpdf-raw-page:15 lines:80-150Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online .
Why it matches plant phenotyping methods葉画像から形態・形状・質感・葉脈特徴を抽出して品種を識別するパイプラインを開発・ベンチマークし、ウェブサービス化しており、植物表現型取得・解析手法が中心である。
abstractHere, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN).
Reproduction assets foundThe paper's sweet cherry and soybean leaf image datasets are publicly available at http://mfcis.online/, and the full MFCIS analysis pipeline code (with Docker files and requirements) is publicly available at https://github.com/WeizhenLiuBioinform/mfcis under a BSD-3-Clause license.Code · publicAll the code and Docker files are available at the source code repository.
Code availability
Project name: Multifeature combined plant cultivar identification system
Project home page: https://github.com/WeizhenLiuBioinform/mfcisOpen asset ↗WeizhenLiuBioinform/mfcislines:185-202Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.
Why it matches plant phenotyping methods光学トモグラフィーと機械学習ツールによる気孔密度測定を中心的な方法として開発・適用し、ガス交換等の表現型を大規模集団で評価しているため。
abstractThis study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD).
Reproduction assets foundThe paper's optical tomography leaf images (the sensor inputs used for machine-learning stomatal density phenotyping) are publicly deposited in the Illinois Data Bank. Phenotypic trait data (Supplemental Table S12) are public but only via the article's supplemental material without a listed URL; RNA-seq (PRJNA522466) GDataset · publichttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA522466/ .
Genotyping-by-sequencing data are available at: https://doi.org/10.5281/zenodo.5019227 . Phenotypic data are available as
part of the supplemental
material ( Supplemental Table
S12 ). Optical tomography images from this article can be found in the Illinois
Data Bank under: https://doi.org/10.13012/B2IDB-1411926_V1 .
Supplemental data
The following materials are available in the online version of this article.Open asset ↗10.13012/B2IDB-1411926_V1lines:985-1051Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Heterophylly, or leaf morphological changes along plant shoot axes, is an important indicator of plant eco-adaptation to heterogeneous microenvironments. Despite extensive studies on the genetic control of leaf shape, the genetic architecture of heterophylly remains elusive. To identify genes related to heterophylly and their associations with plant saline tolerance, we conducted a leaf shape mapping experiment using leaves from a natural population of Populus euphratica . We included 106 genotypes grown under salt stress and salt-free (control) conditions using clonal seedling replicates. We developed a shape tracking method to monitor and analyze the leaf shape using principal component (PC) analysis. PC1 explained 42.18% of the shape variation, indicating that shape variation is mainly determined by the leaf length. Using leaf length along shoot axes as a dynamic trait, we implemented a functional mapping-assisted genome-wide association study (GWAS) for heterophylly. We identified 171 and 134 significant quantitative trait loci (QTLs) in control and stressed plants, respectively, which were annotated as candidate genes for stress resistance, auxin, shape, and disease resistance. Functions of the stress resistance genes ABSCISIC ACIS-INSENSITIVE 5-like ( ABI5 ), WRKY72 , and MAPK3 were found to be related to many tolerance responses. The detection of AUXIN RESPONSE FACTOR17-LIKE ( ARF17 ) suggests a balance between auxin-regulated leaf growth and stress resistance within the genome, which led to the development of heterophylly via evolution. Differentially expressed genes between control and stressed plants included several factors with similar functions affecting stress-mediated heterophylly, such as the stress-related genes ABC transporter C family member 2 ( ABCC2 ) and ABC transporter F family member ( ABCF ), and the stomata-regulating and reactive oxygen species (ROS) signaling gene RESPIRATORY BURST OXIDASE HOMOLOG ( RBOH ). A comparison of the genetic architecture of control and salt-stressed plants revealed a potential link between heterophylly and saline tolerance in P. euphratica , which will provide new avenues for research on saline resistance-related genetic mechanisms.
Why it matches plant phenotyping methods葉形を追跡・解析する方法を開発し、葉形状を動的な表現型として定量化しているため、植物フェノタイピング手法が研究の中心です。
abstractWe developed a shape tracking method to monitor and analyze the leaf shape using principal component (PC) analysis.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code and shell scripts (supporting the functional mapping/GWAS of leaf heterophylly phenotypes) in a public GitHub repository. No public phenotype dataset or image deposit is stated; supplementary material link exists but its contents areCode · publicThe code and shell script that support the findings of this study are available from https://github.com/YaruFu01/leafQTL or can be requested from the corresponding author.Open asset ↗YaruFu01/leafQTLlines:523-535Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background Morphological leaf traits are frequently used to quantify, understand and predict plant and vegetation functional diversity and ecology, including environmental and climate change responses. Although morphological leaf traits are easy to measure, their coverage for characterising variation within species and across temporal scales is limited. At the same time, there are about 3100 herbaria worldwide, containing approximately 390 million plant specimens dating from the 16th to 21st century, which can potentially be used to extract morphological leaf traits. Globally, plant specimens are rapidly being digitised and images are made openly available via various biodiversity data platforms, such as iDigBio and GBIF. Based on a pilot study to identify the availability and appropriateness of herbarium specimen images for comprehensive trait data extraction, we developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species. New information After scrutinising the metadata of digitised herbarium specimen images available from iDigBio and GBIF (21.9 million and 31.6 million images for Tracheophyta ; accessed date December 2020), we identified approximately 10 million images potentially appropriate for our study. From the 10 million images, we selected seven species ( Salix bebbiana Sarg., Alnus incana (L.) Moench, Viola canina L., Salix glauca L., Chenopodium album L., Impatiens capensis Meerb. and Solanum dulcamara L.) , which have a simple leaf shape, are well represented in space and time and have high availability of specimens per species. We downloaded 17,383 images. Out of these, we discarded 5779 images due to quality issues. We used the remaining 11,604 images to measure the area, length, width and perimeter on 32,009 individual leaf blades using the semi-automated tool TraitEx. The resulting dataset contains 128,036 trait records.We demonstrate its comparability to trait data measured in natural environments following standard protocols by comparing trait values from the TRY database. We conclude that the herbarium specimens provide valuable information on leaf sizes. The dataset created in our study, by extracting leaf traits from the digitised herbarium specimen images of seven selected species, is a promising opportunity to improve ecological knowledge about the adaptation of size-related leaf traits to environmental changes in space and time.
Why it matches plant phenotyping methodsデジタル標本画像から葉面積・長さ・幅・周囲長を半自動抽出し、大規模な再利用可能データセットを構築・比較検証しており、植物表現型取得法が中心である。
abstractwe developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species.
Reproduction assets foundThe paper's own leaf trait dataset (128,036 records from 11,604 herbarium specimen images) is publicly deposited on Zenodo, and two supplementary CSV files provide image metadata and per-image measurement/exclusion information. No author analysis code with a public URL is provided (Python scripts are mentioned but not)Supplement · publicMaterial
260ECA78-3D27-5F68-AD9E-72A5A181EDBD 10.3897/BDJ.9.e69806.suppl1 Supplementary material 1
Metadata from iDigBio and GBIF
Data type
comma-separated values
Brief description
This file contains the metadata of the 17383 digital herbarium specimen images from iDigBio and GBIF for selected seven species.
File: oo_548998.csv
https://binary.pensoft.net/file/548998 Vamsi Krishna Kommineni, Susanne Tautenhahn, Pramod Baddam, Jitendra Gaikwad, Barbara Wieczorek, Abdelaziz Triki, Jens Kattge F9029DEB-A4B1-50CA-9EC8-3F08DA0A1710 10.3897/BDJ.9.e69806.suppl2 Supplementary material 2
Information of digital herbarium specimen images with different kinds of problems
Data type
comma-separated valueOpen asset ↗lines:213-233Supplement · publicing 'NA', meaning AccessURL of the corresponding record is not responded or not reachable while downloading the images.
If 'Number of leaves measured' column is 'NA', then one of the columns 'Image', 'Remarks_1', 'Remarks_2', and 'Ruler' are updated accordingly, meaning the trait measurement is not possible.
File: oo_549000.csv
https://binary.pensoft.net/file/549000 Vamsi Krishna Kommineni, Susanne Tautenhahn, Pramod Baddam, Jitendra Gaikwad, Barbara Wieczorek, Abdelaziz Triki, Jens Kattge
AcknowledgementsOpen asset ↗lines:213-233Code / dataset availability confirmedCrossref · Europe PMC · checked 9 Sept 2026
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.
Why it matches plant phenotyping methods圃場分光データとSentinel-2模擬データを用いて、植物体N関連形質を推定する手法を比較・評価しており、形質取得・推定手法が研究の中心である。
abstractTherefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA.
Reproduction assets foundThe authors state the field-spectrometer spectral library and plant trait measurements (N conc, Chl AB, LAI, LAI-scaled traits) used in this study are openly available via an ETH research collection DOI, which is an allowed URL. This is a paper-specific, public, actionable phenotype/spectral dataset.Dataset · publicThe data presented in this study are openly available in: https://doi.org/10.3929/ethz-b-000488405 . Please also see the ‘ supplementary materials – dataset ’ for more information on the dataset.Open asset ↗ethz-b-000488405 · 10.3929/ethz-b-000488405lines:301-314Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high‐throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do‐it‐yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high‐end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r 2 = 0.96, Laser r 2 = 0.86; projected leaf area: EasyDCP r 2 = 0.96, Laser r 2 = 0.96). EasyDCP is an open‐source software tool to measure phenotypic traits of container plants with high‐throughput and low labour and financial costs.
Why it matches plant phenotyping methodsEasyDCPは、フォトグラメトリによる3D植物表現型取得と自動形質抽出のためのソフトウェア・撮像ワークフローを開発し、レーザースキャナおよび実測値と比較検証しており、方法が研究の中心です。
abstractWe introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data.
Reproduction assets foundThe paper's EasyDCP source code is publicly available on GitHub, and the performance-test data (source images, point clouds, trait data, R files) plus code and documentation are archived on Zenodo.Code · public| 1681
Methods in Ecology and Evolu on
FELDMAN et al.
EasyDCP_Creation (Section 2.2), which creates a 3D point
cloud from 2D images; and EasyDCP_Analysis (Section 2.3),
which analyses that point cloud and performs trait calcula-
tion. EasyDCP source code and documentation are available
on GitHub (https://github.com/UTokyo-FieldPhenomics-Lab/EasyDCP).2.1 | Image acquisition
Plants must be imaged prior to EasyDCP measurement, and the
image acquisition area can be set up according to the user's needs
(Figure 2a,b). The image acquisition area should have as little in-
clination as possible. One printed target page (.pdf provided with
the software) must be placed in a corner oOpen asset ↗UTokyo-FieldPhenomics-Lab/EasyDCPpdf-raw-page:3 lines:1-111Dataset · public.
PEER REVIEW
The peer review history for this article is available at https://publo
ns.
com/publon/10.1111/2041-210X.13645.
DATA AVAILABILITY STATEMENT
Data from the performance test (source images, point clouds, trait
data and R files), EasyDCP source code, example scripts and detailed
documentation are archived using Zenodo https://doi.org/10.5281/zenodo.4756537 (Feldman et al., 2021).
ORCID
Alexander Feldman https://orcid.org/0000-0002-1162-5917
Haozhou Wang https://orcid.org/0000-0001-6135-402X
Yuya Fukano https://orcid.org/0000-0001-9057-4742
Yoichiro Kato https://orcid.org/0000-0002-7131-0220
Seishi Ninomiya https://orcid.org/0000-0002-2123-4354
Wei Guo https://orcid.org/0000-0002-Open asset ↗Zenodo · 10.5281/zenodo.4756537pdf-raw-page:6 lines:1-102Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates' properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi 1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study.
Why it matches plant phenotyping methodsトウモロコシの光遮断係数kとfPARiを推定する統計手法を比較・評価し、植物キャノピー形質の再現可能な推定ワークフローとして推奨手法を提示しているため、方法論が中心である。
abstractThe parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution.
Reproduction assets foundThe paper's authors explicitly state that the R code implementing the k-estimation analysis (LSE, LogTLM, MLE, Bayesian estimation) is freely available in a public GitHub repository. The underlying phenotype datasets (fPARi/LAI measurements for seven maize genotypes) are only available from the corresponding author on,Code · publicR code is freely available at https://github.com/jlacasa/k-estimation/blob/main/k_estimation_02182021.Rmd .Open asset ↗https://github.com/jlacasa/k-estimation · k_estimation_02182021.Rmdlines:172-210Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.
Why it matches plant phenotyping methods植物画像から葉数を推定する2つの深層学習手法を開発・比較し、既存および新規データセットで評価しているため、表現型取得手法が中心である。
abstractTwo novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study.
Reproduction assets foundThe paper's authors explicitly state their full leaf-counting code (MSR and DRN models) is freely available on GitHub. The banana leaf dataset is proprietary and not public; the LCC datasets are external community benchmarks, not paper-specific assets.Code · publicThe entire code is freely accessible at https://github.com/farjon/Leaf-Counting .Open asset ↗farjon/Leaf-Countinglines:300-310Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. RESULTS: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. CONCLUSION: The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出するソフトウェアを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype
Reproduction assets foundThe authors' Maize-IAS analysis software (the paper's phenotyping analysis code) is publicly available on GitHub. The maize image datasets are explicitly not public and require request.Code · publicl development prospects of visual phenotype detection using deep learning methods. The methods and workflow provided in this article can also be easily applied to other crops.
Availability and requirements
Project name: A Maize Image Analysis Software using Deep Learning for High-throughput Plant Phenotyping.
Project home page: https://github.com/surefyyq/Maize-IAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None.
Supplementary information
Additional file 1. Installation and debug guidelines.
Publisher’s Note
Springer Nature remains neutral with regard to juOpen asset ↗surefyyq/Maize-IASlines:403-475Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Abstract Identification of plant disease is usually done through visual inspection or during laboratory examination which causes delays resulting in yield loss by the time identification is complete. On the other hand, complex deep learning models perform the task with reasonable performance but due to their large size and high computational requirements, they are not suited to mobile and handheld devices. Our proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification. In this study, six color features and twenty-two texture features have been calculated. Support vector machines is used to perform one-vs-one classification of plant disease. The proposed model of disease identification provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold cross-validation. The accuracy on a self-collected dataset is 82.47% for disease identification and 91.40% for healthy and diseased classification. The reported performance measures are better or comparable to the existing approaches and highest among the feature-based methods, presenting it as the most suitable method to automated leaf-based plant disease identification. This prototype system can be extended by adding more disease categories or targeting specific crop or disease categories.
Why it matches plant phenotyping methods葉画像から病変部位を抽出し、色・テクスチャ特徴量と分類器で植物病害状態を推定する手法の開発・検証が中心であり、植物フェノタイピング手法に該当する。
abstractOur proposed approach contributes automated identification of plant diseases which follows a sequence of steps involving pre-processing, segmentation of diseased leaf area, calculation of features based on the Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification.
Reproduction assets foundThe paper's experiments use the public PlantVillage leaf image dataset (54,309 images, 38 classes), which the authors explicitly state is available at the given GitHub URL. The authors' analysis code is only promised after acceptance, so it is not a qualifying public asset.Dataset · publicAvailability of data and material: The data used for experiments is available at
https://github.com/spMohanty/PlantVillage-DatasetOpen asset ↗spMohanty/PlantVillage-Datasetpdf-page:20 lines:1-46Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals’ fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote‐sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote‐sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large‐scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: (1) image segmentation, to identify individual trees and estimate structural traits; (2) an ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and (3) predictions for segmented crowns for the full remote‐sensing footprint at the NEON sites. The R 2 values on held‐out test data ranged from 0.41 to 0.75 on held‐out test data. The ensemble approach performed better than single partial least‐squares models. Carbon performed poorly compared to other traits ( R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over‐segmentation. The pipeline produced good estimates of DBH ( R 2 of 0.62 on held‐out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between 0.07 and 0.26. We used the pipeline to produce individual‐level trait data for ~5 million individual crowns, covering a total extent of ~360 km 2 . This large data set allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.
Why it matches plant phenotyping methods個体樹木の画像分割、ハイパースペクトル推定、アロメトリーを統合し、構造形質・葉形質を大規模に推定する手法を開発・適用しており、植物フェノタイピング手法が中心である。
abstractWe used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites.
Reproduction assets foundThe paper's Data Availability section deposits three paper-specific public assets on Zenodo: the authors' analysis code, the derived crown-level trait dataset for ~5 million trees, and the trait/input data with metadata. All are directly tied to this paper's phenotyping measurements and analysis.Code · publicgle tree extraction by exploiting airborne full-
waveform LiDAR data. Remote Sensing of Environment
123:368–380.
SUPPORTING INFORMATION
Additional supporting information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as describedOpen asset ↗Zenodo · 10.5281/zenodo.3991797pdf-raw-page:15 lines:1-105Dataset · publicrmation may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/eap.2300/full
DATA AVAILABILITY
Code for the analyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
June 2021 LEAFAND STRUCTURAL TRAIT REMOTE SENSING Article e02300; page 15
19395582,
2021,
4,
Downloaded
from
https://esajournals.onlinelibrary.wiley.Open asset ↗Zenodo · 10.5281/zenodo.3991815pdf-raw-page:15 lines:1-105Dataset · publicanalyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
June 2021 LEAFAND STRUCTURAL TRAIT REMOTE SENSING Article e02300; page 15
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(https://onlinelibrary.wileOpen asset ↗Zenodo · 10.5281/zenodo.4434481pdf-raw-page:15 lines:1-105Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Field / plotRaman / spectroscopyLeafWhole plant / canopy / plot / fieldObject detectionCalibration / preprocessingGrowth / development / phenologyLeaf traitsWater status / transpiration
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica . We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Highlights We partition biological variation from measurement uncertainty for leaf spectra. Measurement uncertainty represents ca. 3% of spectral variation among beech trees. Biological variation within an individual increases by 80% as leaves mature. Maxima of uncertainty correspond to maxima of biological variation (water content). We recommend procedures to quantify biological variation in spectral measurements.
Why it matches plant phenotyping methods葉の反射分光測定における不確実性を定量化し、植物形質変異の検出能力と標準化プロトコルを評価する研究であり、フェノタイピング手法が中心です。
abstractWe first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation.
Reproduction assets foundThe paper deposits its analysis scripts and source data in Dryad and its FieldSpec leaf/fabric reflectance spectra in the SPECCHIO spectral database, both publicly accessible with explicit identifiers.Dataset · publicumption. The number of
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replicates is indicated for each dataset. Data processing and statistical analyses were all
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
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individual wavelengths; see Results and figure captions for details. Scripts and source data
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are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
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spectroradiometer data are also deposited in SPECCHIO
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(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
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be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
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Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatOpen asset ↗Dryad · 10.5061/dryad.gtht76hkxpdf-raw-page:17 lines:1-49Dataset · publicall
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performed in Matlab R2020a. Normality tests, t-tests and ANOVAs were performed on
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individual wavelengths; see Results and figure captions for details. Scripts and source data
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are available in Dryad (https://doi.org/10.5061/dryad.gtht76hkx). FieldSpec
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spectroradiometer data are also deposited in SPECCHIO
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(http://sc22.geo.uzh.ch:8080/SPECCHIO_Web_Interface/search, Hueni et al., 2020) and can
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be found with the identifiers ‘Field spectroscopy Fabrics’ (dataset A), ‘Field spectroscopy F.
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Sylvatica individual’ (dataset B), ‘Field spectroscopy F. sylvatica La Massane’ (dataset C),
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‘Field spectroscopy F. sylvatica SwissForest’ (dataset D). Visualization and descriOpen asset ↗SPECCHIOpdf-raw-page:17 lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.
Why it matches plant phenotyping methodsUAV斜め写真測量と3D点群・ボクセル解析を用いて森林キャノピーのLAIを推定する手法を開発・比較検証しており、植物形態形質の取得方法が研究の中心である。
abstractTo explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes
Reproduction assets foundThe paper's authors publicly released the voxelization/LAI extraction code on GitHub; phenotype data (UAV images, point clouds, LAI-2200 measurements) are only available upon request.Code · publicData Availability Statement: The source codes developed in this study were donated to GitHub
(https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐
grammetry (accessed on 7 January 2021)). And the data used to support the findings of this study
are available from the corresponding author upon request.Open asset ↗https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐pdf-page:15 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Abstract Background : Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results : On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion : The Maize-PAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science. Keywords : Maize phenotyping; Instance segmentation; Computer vision; Deep learning; Convolutional neural network
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出する深層学習ソフトウェアを開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting.
Reproduction assets foundThe paper's maize phenotyping software (Maize-IAS), which implements the paper's image analysis functions (RoI extraction, color analysis, internode/height/stem diameter, leaf counting), is publicly available via the authors' GitHub project home page. The maize image datasets and annotations are not public and require Code · publicWe
reveal the potential development prospects of visual phenotype detection using deep
learning methods. The methods and workflow provided in this article can also be
easily applied to other crops.
Availability and requirements
Project name: Automated Maize Phenotyping Analysis Software using Deep Learn-
ing.
Project home page: https://github.com/sureatgithub/Maize- IAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The datasOpen asset ↗sureatgithub/Maize-pdf-raw-page:18 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
SoybeanSugar beetLiDAR / point cloudLeafMorphology / geometry measurement2D/3D reconstructionLeaf traits
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Why it matches plant phenotyping methods3D点群から植物葉面を再構成し、ノイズ耐性と葉面積推定の安定性を従来法と比較検証する手法開発研究であり、植物フェノタイピング手法が中心です。
abstractwe developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their proposed leaf surface reconstruction method is publicly available on GitHub. No public deposit of the 3D point cloud phenotype data (soybean/sugar beet scans) is mentioned, so only the code qualifies as a paper-specific public asset.Code · publicWe implemented the algorithm for the proposed method in Python ( http://www.python.org/ ). The source code is at https://github.com/oceam/LeafSurfaceReconstruction .Open asset ↗oceam/LeafSurfaceReconstructionlines:46-55Code / dataset availability confirmedOpenAlex · arXiv · checked 14 Sept 2026
Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research groups, one of the main challenges for proposing better methods is still the limitation of labelled data availability. The main efforts of the field seem to be augmenting existing limited data sets, and some aspects of the modelling process have been under-discussed. This paper explores such topics and present experiments that led to the development of the best-performing method in the Leaf Segmentation Challenge and in another external data set of Komatsuna plants. The model has competitive performance while been arguably simpler than other recently proposed ones. The experiments also brought insights such as the fact that model cardinality and test-time augmentation may have strong applications in object segmentation of single class and high occlusion, and regarding the data distribution of recently proposed data sets for benchmarking.
Why it matches plant phenotyping methods葉のセグメンテーションと計数という植物形態形質の抽出手法を開発・評価し、チャレンジと外部データセットで性能比較しているため、方法が研究の中心である。
abstractPlant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe CVPPP data set represents perhaps that largest effort to address the ongoing problem of lacking benchmark data sets and metrics for specific tasks such as leaf segmentation and counting on a controlled environment. First presented in 2014, but updated in 2017, the data set comprises images of mainly Arabidopsis and a small portion of Tobacco plants and can be accessed by Minervini et al. (2015b) .Open asset ↗lines:78-92Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
Why it matches plant phenotyping methods3D画像再構成と葉・茎分離による植物表現型抽出アルゴリズムを開発し、ベンチマークデータセットも提示する、植物フェノタイピング手法が中心の研究。
abstractWe introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV).
Reproduction assets foundThe paper introduces the UNL-3DPPD benchmark dataset (multiview maize/cotton plant images and calibration checkerboards) and states it is publicly available at the authors' plantvision.unl.edu site. No separate analysis code deposit is explicitly stated.Dataset · publicThe datasets generated for this study are publicly available from https://plantvision.unl.edu/dataset .Open asset ↗plantvision.unl.edulines:481-518Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is the leaf area index (LAI) if algorithms account for variability in the atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6S were used to simulate a look-up table (LUT) of TOA radiance data and associated input variables. This LUT was then used to train the Bayesian machine learning algorithms Gaussian processes regression (GPR) and variational heteroscedastic GPR (VHGPR). PROSAIL simulations were also used to train GPR and VHGPR models for LAI retrieval from S2 images at bottom-of-atmosphere (BOA) level (L2A product) for comparison purposes. The VHGPR models led to consistent LAI maps at BOA and TOA scale. We demonstrated that hybrid LAI retrieval algorithms can be developed from TOA radiance data given a cloud-free sky, thus without the need for atmospheric correction.
Why it matches plant phenotyping methodsSentinel-2のTOA放射輝度からLAIを推定する機械学習アルゴリズムを開発・比較しており、植物形質の取得手法が研究の中心である。
abstractWe demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework.
Reproduction assets foundThe paper's hybrid LAI retrieval (GPR/VHGPR) was developed within the authors' ALG-ARTMO software framework, and code snippets/demos for GPR and VHGPR are publicly available from the authors' UV-ES soft regression page. Both are explicitly stated as freely downloadable in the supplied text. No paper-specific phenotype/Code · publicCode snippets and demos for both GPR, VHGPR and other machine learning regression algorithms is available from https://isp.uv.es/soft_regression.html .Open asset ↗isp.uv.es/soft_regression.htmllines:485-521Code / dataset availability 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 confirmedbioRxiv · checked 8 Sept 2026
Morphometrics has been applied in several fields of science including botany. Plant leaves are been one of the most important organs in the identification of plants due to its high variability across different plant groups. The differences between and within plant species reflect variations in genotypes, development, evolution, and environment. While traditional morphometrics has contributed tremendously to reducing the problems that come with the identification of plants and delimitation of species based on morphology, technological advancements have led to the creation of deep learning digital solutions that made it easy to study leaves and detect more characters to complement already existing leaf datasets. In this study, we demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, height of (each position of the) tooth from tip, and the height of each (position of the) tooth from base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can quantitatively track diversity in leaf specimens, and it can be applied to functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to apply and carry out more accurate image-based researches in diverse areas including, but not limited to, plant development, evolution, and phenotyping.
Why it matches plant phenotyping methodsMorphoLeafによる葉画像のスキャン、ランドマーク抽出、形態計測データ化を中心に、葉の形状形質を定量化するソフトウェア／ワークフローを実証しているため。
abstractwe demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours.
Reproduction assets foundThe preprint states its supplementary data (the Cucurbitaceae leaf morphometric dataset from MorphoLeaf analysis) is available online in the authors' GitHub repository, matching an allowed URL.Dataset · public411 The Data for this article is available online at: https://github.com/osooluwatobia/cucurbitaceae-Open asset ↗cucurbitaceae-pdf-page:19 lines:1-46Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.
Why it matches plant phenotyping methodsLAIの連続測定システムと衛星画像によるLAI推定を構築し、実測値との比較で性能検証しており、植物形質取得法が中心である。
abstractfield LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology
Reproduction assets foundThe paper's continuous field LAI measurements (LAINet WSN datasets for 2018 and 2019 corn plots at the Heihe watershed) were explicitly released publicly via the National Science & Technology Infrastructure at the National Tibetan Plateau Data Center. Satellite imagery (Landsat/Sentinel-2) came from generic public USGSDataset · publicThe LAINet datasets of both years
have been released publicly via the National Science & Technology Infrastructure
(http://www.tpdc.ac.cn/zh-hans/, in Chinese).Open asset ↗National Science & Technology Infrastructurepdf-page:4 lines:1-54Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm 2 ). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.
Why it matches plant phenotyping methods葉脈画像のセグメンテーションと形態統計抽出を深層学習で開発・検証し、再利用可能なソフトウェアとして提供しているため、植物フェノタイピング手法が中心である。
abstractHere we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks.
Reproduction assets foundThe paper's Data and algorithm availability section explicitly deposits the LEAFVEINCNN GUI software with trained networks (Zenodo 4007731), the down-sampled CNN predictions, ground truths, and MATLAB analysis scripts (Zenodo 4008614), and all results (Zenodo 4008361), all openly available. These are paper-specific,公开,Code · publicfull width of the vein, the P-R analysis was also run fol-
lowing conversion of the binary image at each threshold value to
a single-pixel wide skeleton.
Data and algorithm availability
A MATLAB App or the standalone LEAFVEINCNN GUI software
package, including the trained networks, and manual (Fig. S2)
are openly available from https://doi.org/10.5281/zenodo.4007731. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from htOpen asset ↗zenodo · 10.5281/zenodo.4007731pdf-raw-page:8 lines:1-92Code · publicanual (Fig. S2)
are openly available from https://doi.org/10.5281/zenodo.4007731. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from https://doi.org/10.5281/zenodo.4008361.Results
CNNs provided high accuracy vein network segmentationOpen asset ↗zenodo · 10.5281/zenodo.4008614pdf-raw-page:8 lines:1-92Dataset · public31. The original image dataset is openly available (Blonder
et al., 2019). The down-sampled CNN predictions, ground
truths, and the MATLAB scripts used for the Precision-Recall (PR)
analysis and calculation of network metrics are openly available
from https://doi.org/10.5281/zenodo.4008614. All results are
openly available from https://doi.org/10.5281/zenodo.4008361.Results
CNNs provided high accuracy vein network segmentationOpen asset ↗zenodo · 10.5281/zenodo.4008361pdf-raw-page:8 lines:1-92Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotLeafWhole plant / canopy / plot / fieldVisualization / data managementBiomass / plant weightLeaf traitsPlant / canopy height
Abstract. The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).
Why it matches plant phenotyping methods植物のバイオマス、草丈、LAI、フェノロジーなどの再利用可能な形質データを含む包括的データセットを構築し、リモートセンシング手法の開発・検証にも利用できるため、植物フェノタイピングデータセットとして中心的です。
abstractHere, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany
Reproduction assets foundThis is a data description paper whose core contribution is a public plant-phenotyping dataset (vegetation states, biomass, LAI, phenology, fluxes, weather, management, soil) hosted at the TR32 database with a DOI. The dataset is directly downloadable via the authors' public URLs; no code or models are described.Dataset · publicments start with “#”. Comments can contain additional
information on yield, management of harvest residues, additional contents of
agrochemicals, etc.
9 Data availability
The dataset can be downloaded from the TR32
database ( https://www.tr32db.uni-koeln.de/data.php?dataID=1889 , last access: 29 September 2020) or using
the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020). The dataset is provided as a zip-compressed
container. All files are plain text files organized in a folder per site as
shown in Fig. 2 and as explained in Sect. 3. Technical details on file
formats and data structure within files are presented for the different kinds
of data in Sects. 4.4, 5.4, 6.4, 7Open asset ↗TR32DB · 10.5880/TR32DB.39lines:1217-1303Code / 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 confirmedEurope PMC · checked 15 Sept 2026
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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
PREMISE: X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers. METHODS AND RESULTS: We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations. By training the model on each scan using six hand-segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. CONCLUSIONS: Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
Why it matches plant phenotyping methods3DマイクロCT画像から葉の内部解剖形質を抽出する機械学習セグメンテーションと定量化パイプラインの開発が中心であり、植物フェノタイピングへの適用性も明示されている。
abstractWe present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification
Reproduction assets foundThe paper's authors publicly released their random forest segmentation/leaf-traits analysis code on GitHub and the microCT image dataset, hand-labeled training slices, and segmentation outputs on Zenodo.Code · publicThe code and an in-depth user manual are available at https://Open asset ↗pdf-raw-page:8 lines:1-78Dataset · publicgithub.com/plant-microct-tools/leaf-traits-microct. Future updates
will be integrated to this repository. The microCT data set, training
hand-labeled slices, and all image outputs of the program including
one full stack segmentation are available on Zenodo at https://doi.org/10.5281/zenodo.3694973 (Théroux-Rancourt et al., 2020b).
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Average proportion of pixels per tissue in the 24
slices of the training data set.
APPENDIX S2. Standard deviation of thickness estimates pre-
sented inOpen asset ↗Zenodo · 10.5281/zenodo.3694973pdf-raw-page:8 lines:79-106Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
ArabidopsisGrowth chamberLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementGrowth / development / phenologyLeaf traitsRoot system architecture
Background Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen - Agar , for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium. Results The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately. Conclusions Because the GrowScreen - Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.
Why it matches plant phenotyping methods自動画像計測による根・シュート形質の非破壊取得プラットフォームを開発・検証しており、表現型取得法が研究の中心である。
abstractThe phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min.
Reproduction assets foundThe paper's phenotypic datasets (root/shoot trait measurements of 78 Arabidopsis accessions and experiments 1-2) are publicly deposited in the e!DAL research data publication system. The analysis software is only available upon request from the corresponding author, so it is not a public asset. AraPheno and cited worksDataset · publicThe datasets generated and analysed during the current study are available in the e!DAL research data publication system, https://doi.org/10.25622/FZJ/2020/0 .Open asset ↗e!DAL research data publication system · 10.25622/FZJ/2020/0lines:157-166Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract Background: Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.Results: On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.Conclusion: The Maize-PAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Why it matches plant phenotyping methodsトウモロコシ画像から複数の形態形質を自動抽出するソフトウェアを開発し、精度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。
abstractThis paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype
Reproduction assets foundThe authors' Maize-PAS phenotyping analysis software is publicly available on GitHub with an explicit project home page. The maize image/annotation datasets are explicitly not public (available only on request), and Labelme is a generic third-party annotation tool, not a paper-specific asset.Code · publicWe
reveal the potential development prospects of visual phenotype detection using deep
learning methods. The methods and workflow provided in this article can also be
easily applied to other crops.
Availability and requirements
Project name: Automated Maize Phenotyping Analysis Software using Deep Learn-
ing.
Project home page: https://github.com/sureatgithub/MaizePAS
Operating system: Ubuntu18.04.
Programming language: Python3.
Other requirements: Pytorch 1.1.0 or higher, Torchvision 0.3.0 or higher.
Any restrictions to use by non-academic: None
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The datasetsOpen asset ↗sureatgithub/MaizePASpdf-raw-page:16 lines:1-46Code / dataset availability confirmedEurope PMC · Crossref · checked 9 Sept 2026
Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.
Why it matches plant phenotyping methodsUAV画像とCNNを用いて rapeseed の葉を認識し、植物体数（stand count）を自動推定する手法の開発・性能評価が研究の中心であるため、植物フェノタイピング方法論に該当します。
abstractA CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves.
Reproduction assets foundThe paper's data availability statement explicitly deposits the 'Rapeseed_seedling_counting' data (supporting the UAV imagery-based stand count findings) in a public GitHub repository with an authors' URL, qualifying as a paper-specific public asset.Dataset · publicThe “Rapeseed_seedling_counting” data that support the findings of this study are available in “LARSC-Lab/Rapeseed_seedling_counting” in GitHub, which can be found at https://github.com/LARSC-Lab/Rapeseed_seedling_counting .Open asset ↗LARSC-Lab/Rapeseed_seedling_countinglines:590-664Code / 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
Premise Physiological processes may vary within leaf laminae; however, the accompanying heterogeneity in leaf venation is rarely investigated because its quantification can be time consuming. Here we introduce accelerated protocols using existing software to increase sample throughput and ask whether laminae venation varies among three crop types and four subspecies of Brassica rapa . Methods FAA (formaldehyde, glacial acetic acid, and ethanol)-fixed samples were stored in ethanol. Without performing any additional clearing or staining, we tested two methods of image acquisition at three locations along the proximal-distal axis of the laminae and estimated the patterns of venation using the program phenoVein. We developed and made available an R script to handle the phenoVein output and then analyzed our data using linear mixed-effects models. Results Beyond fixation and storage, staining and clearing are not necessary to estimate leaf venation using phenoVein if the images are acquired using a stereomicroscope. All estimates of venation required some manual adjustment. We found a significant effect of location within the laminae for all aspects of venation. Discussion By removing the clearing and staining steps and utilizing the semi-automated program phenoVein, we quickly and cheaply acquired leaf venation data. Venation may be an important target for crop breeding efforts, particularly if intralaminar variation correlates with variation in physiological processes, which remains an open question.
Why it matches plant phenotyping methods葉脈という植物形態形質の画像取得・半自動定量法を開発・評価し、解析用Rスクリプトも提供しており、フェノタイピング手法が研究の中心である。
abstractHere we introduce accelerated protocols using existing software to increase sample throughput
Reproduction assets foundThe authors explicitly state that their custom R script for processing phenoVein output and all analyzed venation data are freely available on their public GitHub repository, making this a paper-specific, public, actionable asset.Code · publicWe developed an R script that (1) compiles data from multiple phenoVein .csv output files, (2) reformats the phenoVein output into a rectangular dataframe to facilitate the downstream data analysis, and (3) saves this new dataframe as a separate .csv file that can be easily imported into and analyzed using any number of statistical software environments (available at https://github.com/rlbaker5/AppsInPlantSci_phenoVein )Open asset ↗rlbaker5/AppsInPlantSci_phenoVeinlines:113-120Dataset · publicAll the analyzed data are available at https://github.com/rlbaker5/AppsInPlantSci_phenoVein .Open asset ↗rlbaker5/AppsInPlantSci_phenoVeinlines:113-120Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
The shapes of grapevine leaves have been critical to correctly identify economically important varieties throughout history. The correspondence of homologous features in nearly all grapevine species and varieties has enabled advanced morphometric approaches to mathematically classify leaf shape. These approaches either model leaves through the measurement of numerous vein lengths and angles or measure a finite number of corresponding landmarks and use Procrustean approaches to superimpose points and perform statistical analyses. Hand illustrations, too, play an important role in grapevine identification, as details omitted using the above methods can be visualized. Here, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations. Using these points, averaged leaf shapes for 60 varieties of wine and table grapes are calculated that preserve features. A pairwise Procrustes distance matrix of the overall morphological similarity of each variety to the other classifies leaves into two main groups--deeply lobed and more entire--that correspond to the measurements of sinus depth by Pierre Galet. Using the system of Galet, pseudo-landmarks are converted into relative distance and angle measurements. Both Galet-inspired and Procrustean methods allow increased accuracy in predicting variety compared to a finite number of landmarks. Using Procrustean pseudo-landmarks captures grapevine leaf shape at the same level of detail as drawings and provides a quantitative method to arrive at mean leaf shapes representing varieties that can be used within a predictive statistical framework.
Why it matches plant phenotyping methodsブドウ葉の形状を擬似ランドマークとプロクルステス解析で定量化し、品種識別・平均葉形状推定に用いる手法の開発が中心である。
abstractHere, I use a saturating number of pseudo-landmarks to capture intricate, local features in grapevine leaves: the curvature of veins and the shapes of serrations.
Reproduction assets foundThe paper explicitly links public GitHub repositories and a Dryad DOI containing its leaf photographs, hand-traced landmark/pseudo-landmark raw data, visual-check outputs, interpolation code and outputs, and Procrustes analysis code and outputs — all paper-specific, public, and actionable.Dataset · public) the photo ID of the leaf indicating the vineyard position of
206 the vine it was collected from, 2) an enumerating value 1 through 4 specifying which of four
207 leaves for the variety the data corresponds to, and 3) which vector the data file represents.
208 These files, the raw data, are available at the following link:
209 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/ampel
210 ometry_data. Tracing all data for a single leaf took approximately 15 minutes. Because the data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicthe data
211 was traced by hand, it was important to visually verify its accuracy. Analyses in Python were
212 undertaken using NumPy (Oliphant, 2006), pandas (McKinney, 2010), and Matplotlib (Hunter,
213 2007) to plot the data on the actual photo. The code for plotting vectors onto the original photo
214 can be found here:
215 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/0_visual_check/ampel
216 ometry_visual_check.ipynb. The visual checks for each of the 240 leaves analyzed in this study
217 can be found here:
218 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/0_visual_check/outpu
219 t_visual_check
220
5Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:5 lines:1-54Code · publicith assigned numbers of points to every vector, interpolation was
240 used to calculate equidistant pseudo-landmarks. A function was created using the scipy
241 (Virtanen et al., 2020) interp1d function to interpolate the correct number of equidistance
242 points for each vector. The code used to interpolate points is here:
243 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/ampe
244 lometry_interpolation.ipynb. The interpolated points can be found here:
245 https://github.com/DanChitwood/grapevine_ampelometry/blob/master/1_interpolation/outp
246 ut_interpolated_points.txt
247
248 With corresponding points between all leaves, a Procrustes analysis could be peOpen asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicesults saved as a pairwise distance matrix. The hclust() function in R using the
259 “mcquitty” method was used to hierarchically cluster varieties based on the pairwise distance
260 matrix and overall morphological similarity. The code for performing a Procrustes analysis for
261 each variety and outputs can be found here:
262 https://github.com/DanChitwood/grapevine_ampelometry/tree/master/2_procrustes_by_vari
263 ety
264
6Open asset ↗DanChitwood/grapevine_ampelometrypdf-layout-page:6 lines:1-54Code · publicll Procrustes mean
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shape, super-imposed Procrustes coordinates for all leaves, and eigenvalues and eigenleaves
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from a PCA. The superimposed Procrustes coordinates of all leaves and the mean shape were
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plotted together. The code for the Procrustes analysis for all 240 leaves and the outputs can be
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found here:
269
https://github.com/DanChitwood/grapevine_ampelometry/tree/master/3_overall_procrustes
270
271
Data analysis
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To calculate allometry for each line segment, distances between all points were converted to
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cm using the pixel to cm scale measured for each leaf. The lm() function in R was used to model
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the natural log of the distance from each point to the neOpen asset ↗DanChitwood/grapevine_ampelometrypdf-raw-page:7 lines:1-92Code / 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 confirmedbioRxiv · checked 15 Sept 2026
Museum specimens are the main source of information on organisms’ morphological features. Although access to this information was commonly limited to researchers able to visit collections, it is now becoming freely available thanks to the digitization of museum specimens. With these images, we will be able to collectively build large-scale morphological datasets, but these will only be useful if the limits to this approach are well-known. To establish these limits, we used two-dimensional images of plant specimens to test the precision and accuracy of image-based data and analyses.To test measurement precision and accuracy, we compared leaf measurements taken from specimens and images of the same specimens. Then we used legacy morphometric datasets to establish differences in the quality of datasets and multivariate analyses between specimens and images. To do so, we compared the multivariate space based on original legacy data to spaces built with datasets simulating image-based data.We found that trait measurements made from images are as precise as those obtained directly from specimens, but as traits diminish in size, the accuracy drops as well. This decrease in accuracy, however, has a very low impact on dataset and analysis quality. The main problem with image-based datasets comes from missing observations due to image resolution or organ overlapping. Missing data lowers the accuracy of datasets and multivariate analyses. Although the effect is not strong, this decrease in accuracy suggests caution is needed when designing morphological research that will rely on digitized specimens.As highlighted by images of plant specimens, 2D images are reliable measurement sources, even though resolution issues lower accuracy for small traits. At the same time, the impossibility of observing particular traits affects the quality of image-based datasets and, thus, of derived analyses. Despite these issues, gathering phenotypic data from two-dimensional images is valid and may support large-scale studies on the morphology and evolution of a wide diversity of organisms.Competing Interest StatementThe authors have declared no competing interest.View Full Text
Why it matches plant phenotyping methods植物標本画像から形態形質を測定する画像ベース手法の精度・正確性を、実標本および既存データと比較検証しており、フェノタイピング手法が中心です。
abstractTo establish these limits, we used two-dimensional images of plant specimens to test the precision and accuracy of image-based data and analyses.
Reproduction assets foundThe paper's leaf measurement dataset (specimen and image measurements) is publicly deposited on MorphoBank project 3764, with an explicit availability statement and URL. The authors also state all data and code are on GitHub/Zenodo (DOI 10.5281/zenodo.3924506), but no matching URL is present in the allowed list, so no码Dataset · publicavoided sampling
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size issues, as they are well represented in the Rio de Janeiro Botanical Garden herbarium (RB; acronym
80
according to Thiers, 2020, continuously updated. See the supplementary material for information on
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measurements, sampling, and vouchers. Metric data is also available from MorphoBank project 3764
82
http://morphobank.org/permalink/?P3764). We used a digital caliper to measure specimens and
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the FSI Viewer v. 5.6.6 software (available from RB’s website; http://jabot.jbrj.gov.br) to measure
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images of the same leaves from the same specimens. All measurements, both from images and speci-
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mens, were made twice to allow the following analyses.
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First, we teOpen asset ↗MorphoBank · project 3764pdf-raw-page:4 lines:1-79Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Published10 Mar 2020Environmental modelling & software : with environment data newsCited by 104 · OpenAlex ↗
Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regular or irregular satellite time series. This work describes the main features of DATimeS, and provides a demonstration case using Sentinel-2 Leaf Area Index time series data over a Spanish site. GPR resulted as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties. DATimeS further quantified LAI fluctuations among multiple crop seasons and provided phenological indicators for specific crop types.
Why it matches plant phenotyping methods植物のLAI時系列から雲のない合成画像、季節動態、フェノロジー指標を抽出するソフトウェアを開発・提示しており、形質推定ワークフローが中心である。
abstractThe here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns.
Reproduction assets foundThe paper's own analysis software, DATimeS, is explicitly made freely available for download from the ARTMO web page, and the MLRA source code used within it is stated to be freely available on the authors' IPL-UV GitHub repository. GPy and GDAL are generic third-party libraries cited in references and excluded.Code · publicTheir source code is freely available and can be found at: https://github.com/IPL-UV/simpleR .Open asset ↗IPL-UV/simpleRlines:122-166Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
Image-based plant phenotyping has been steadily growing and this has steeply increased the need for more efficient image analysis techniques capable of evaluating multiple plant traits. Deep learning has shown its potential in a multitude of visual tasks in plant phenotyping, such as segmentation and counting. Here, we show how different phenotyping traits can be extracted simultaneously from plant images, using Multi-Task Learning (MTL). MTL leverages information contained in the training images of related tasks to improve overall generalization and learns models with fewer labels. We present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification. We adopted a modified ResNet50 as a feature extractor, trained end-to-end to predict multiple traits. We also leverage MTL to show that through learning from more easily obtainable annotations (such as PLA and genotype) we can predict a better leaf count (harder to obtain annotation). We evaluate our findings on several publicly available datasets of top-view images of Arabidopsis thaliana. Experimental results show that the proposed MTL method improves the leaf count Mean Squared Error (MSE) by more than 40 %, compared to a single task network on the same dataset. We also show that our MTL framework can be trained with up to 75 % fewer leaf count annotations without significantly impacting performance, whereas a single task model shows a steady decline when fewer annotations are available.
Why it matches plant phenotyping methods植物画像から複数形質を同時推定するマルチタスク深層学習手法の開発・評価が中心であり、明確な植物フェノタイピング方法論研究である。
abstractWe present a Multi-Task Deep Learning framework for plant phenotyping, able to infer three traits simultaneously: (i) leaf count; (ii) projected leaf area (PLA); and (iii) genotype classification.
Reproduction assets foundThe paper's authors provide public analysis code (MTL phenotyping framework) on GitHub, and the study analyzes publicly available CVPPP plant image datasets (Ara2013, A1, A4) hosted on plant-phenotyping.org. Both are paper-specific, public, and actionable.Code · publicCode available at https://github.com/andobrescu/Multi_task_plant_phenotyping .Open asset ↗andobrescu/Multi_task_plant_phenotypinglines:224-295Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.plant-phenotyping.org/CVPPP2017-challenge .Open asset ↗lines:607-694Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The micro-morphology of leaf epidermises is valuable for the study of leaf development and function, as well as the classification of plant species. There have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis. Here, four specimen preparation methods were used to investigate the leaf epidermis morphology of Arabidopsis , radish, cucumber, wheat, rice, and maize, under an inverted basic light microscope (LM), a laser scanning confocal microscope (LSCM), or a scanning electron microscope (SEM). Optical microscope specimens were obtained using either the direct isolation method or the chloral hydrate-based clearing method. SEM images were obtained using a standard stage for conventional dehydrated samples or a Coolstage for fresh tissue. Different parts of epidermis peels were well focused under the LM. Investigation of samples cleared by chloral hydrate is convenient and autofluorescence of cell walls can be detected in rice. The resolution of images of conventional SEM leaf samples was generally higher than the Coolstage images at the same magnification, whereas local collapse and shrinkage were observed in leaves with high water content when using the conventional method. However, stomatal apparatuses of Arabidopsis , cucumber, radish, and maize deformed and showed poor appearance when using the Coolstage. Moreover, we usually used glutaraldehyde as an SEM fixative when using t-butanol for freeze-drying, though methanol is considered a better fixative in recent studies. In addition, fresh samples were not stable on the Coolstage. Thus, we compared four different t-butanol freeze-drying methods and two Coolstage methods. The dimension and morphology of tissues were compared using the six different methods. The results indicate that methanol fixative obviously reduced shrinkage of SEM samples compared with glutaraldehyde and formaldehyde alcohol acetic acid (FAA) fixatives. The use of methanol and a graded series of steps improved the preservation of samples. Preparing samples with optimal cutting temperature compound and observing at -30°C helped to increase the stability of Coolstage samples. In summary, our results provide an overview of the shortcomings and merits of four different methods, and might provide some information about choosing an optimal method for visualizing epidermal morphology.
Why it matches plant phenotyping methods葉表皮形態の可視化について、複数の試料調製法・顕微鏡法を比較し、組織形態の保存性や画像品質を評価しており、植物形質取得法が研究の中心である。
abstractThere have been few studies comparing different preparation and imaging methods for visualizing the leaf epidermis.
Reproduction assets foundThe paper reports LM/LSCM/SEM imaging of leaf epidermises and shrinkage/stability measurements. No author analysis code, trained models, or external repository deposit is mentioned. The only paper-specific public asset is the article's Supplementary Material, which the authors state contains all data generated or analySupplement · publicgy Project of Henan Province (182102110234).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.00133/full#supplementary-material
Click here for additional data file.
Abbreviations
SEM, scanning electron microscope; LSCM, laser scanning confocal microscope; LM, light microscope; DIC, differential interference contrast; CPD, critical point drying; OCT, optimum cutting temperature.
References
Bailes E. J. GlovOpen asset ↗lines:293-368Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).
Why it matches plant phenotyping methods作物の生育ステージ（フェノロジー）をLAIデータから推定する手法を開発し、広域データセットとして構築・検証しており、植物形質取得が中心である。
abstractThen, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China.
Reproduction assets foundThe paper's core output, the ChinaCropPhen1km 1 km gridded crop phenological dataset (2000–2015, maize/rice/wheat), is explicitly deposited publicly on Figshare by the authors. Input GLASS LAI products, NLCD masks, and CMA station observations are third-party sources, not paper-specific assets, and no author analysis代码Dataset · publicThe derived crop phenological dataset for three staple crops in China during
2000–2015 is available at https://doi.org/10.6084/m9.figshare.8313530 (Luo
et al., 2019).Open asset ↗figshare · 10.6084/m9.figshare.8313530lines:778-854Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 15 Sept 2026
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. Subsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput. A genome-wide association study, combined with RNAseq analyses of the bulliform cell ontogenic zone, identified candidate regulatory genes affecting bulliform cell column number and cell width. This study is the first to combine machine learning approaches, transcriptomics, and genomics to study bulliform cell patterning, and the first to utilize natural variation to investigate the genetic architecture of this microscopic trait. In addition, this study provides insight toward the improvement of macroscopic traits such as drought resistance and plant architecture in an agronomically important crop plant.
Why it matches plant phenotyping methodsCNNを用いてトウモロコシ葉の微細なブルフォーム細胞形態を高スループット測定しており、表現型取得・抽出法が研究の中心です。
abstractSubsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the leaf epidermal glue-impression images (Cyverse zip), the authors' analysis scripts (LD calculation, image processing, U-net architecture, GWAS) on GitHub, trained U-net models (Cyverse File S1 zip), and supplemental material on figshare. All are paper-phenCode · publicwere deposited at NCBI SRT with SRA accession numbers PRJNA545465 and PRJNA400334. Leaf epidermal glue-impression images can be found at https://de.cyverse.org/dl/d/8CA8D72B-24AF-4887-8899-14460021887A/resized.zip. The scripts including LD calculation, image processing, U-net architecture, and running the GWAS are deposited in https://github.com/pengfei-qiao/Bulliform-cell-deep-learning.git. Trained U-net models are deposited as File S1 under https://de.cyverse.org/dl/d/B352A862-5B08-4373-87EB-9B48356028C6/FlieS1.zip. We request that this manuscript be cited when using these data. Supplemental material available at figshare: https://doi.org/10.25387/g3.9939623.Open asset ↗GitHub · pengfei-qiao/Bulliform-cell-deep-learninghtml-lines:236-236Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotMultispectral / hyperspectralLeafMorphology / geometry measurementLeaf traits
Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products.
Why it matches plant phenotyping methods作物キャノピーのLAIという植物形質について、衛星LAI製品のマルチスケール検証手法を提案・評価しており、形質取得と検証が研究の中心である。
abstractwe propose a new multi-scale LAI product validation method based on a crop growth cycle.
Reproduction assets foundThe paper's LAI validation analysis relies on two public data assets: field-measured corn LAI/LAD/spectral samples from the Heihe Plan Science Data Center (public URL given) and the MOD15A2H LAI satellite product from LP DAAC, both directly used in the paper's multi-scale MODIS LAI validation. No author analysis code, Dataset · publicR.; Che, T.; Wang, W.; Hu, X.; Xu, Z.; Wen, J.; et al. A multiscale dataset
for understanding complex eco-hydrological processes in a heterogeneous oasis system. Sci. Data 2017,
4, 170083. [CrossRef] [PubMed]
22. Available online: http://www.heihedata.org (accessed on 10 October 2019).
23. LP DAAC - MOD15A2H. Available online: https://lpdaac.usgs.gov/products/mod15a2hv006/ (accessed on
20 October 2019).
24. Kancheva, R.; Georgiev, G. Assessing Cd-induced stress from plant spectral response. In Proceedings of the
SPIE—The International Society for Optical Engineering, Amsterdam, The Netherlands, 22–25 September
2014; Volume 9239, pp. 1–12.
25. Krzywinski, M.; Altman, N. Multiple linear regresOpen asset ↗LP DAAC · MOD15A2Hpdf-raw-page:15 lines:1-53Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 15 Sept 2026
The worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system. Heat stress exposure resulted in an acute reduction of quantum yield of photosystem II and increased leaf angle. In the longer term, exposure to heat also affected plant growth and morphology. By tracking the recovery period of WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6h of heat stress provides the most informative insight in plant responses to heat, as it shows a clear separation between treated and non-treated plants as well as WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline and new phenotypes that could be used as targets in thermotolerance screenings.
Why it matches plant phenotyping methods自動化・非破壊フェノタイピングシステムを用いた形態・光合成表現型の取得プロトコル、データ解析パイプライン、新規表現型を中心的に提示しており、耐暑性スクリーニングへの再利用可能な方法論である。
abstractwe describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system.
Reproduction assets foundThe paper publicly deposits its authors' analysis code: an R-notebook for data analysis and a Jupyter notebook for machine learning, both on Zenodo. No phenotype dataset or image deposit is stated in the supplied blocks.Code · public5
statistical analysis using ggpubr. Machine learning classification was implemented using
1
Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
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publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
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the Jupyter notebook containing the command lines used for machine learning
4
(http://doi.org/10.5281/zenodo.3534148).5
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3. Results
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3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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To assess whether high-throughput phenotyping cOpen asset ↗zenodo · 10.5281/zenodo.3534239pdf-raw-page:5 lines:1-56Code · publicng ggpubr. Machine learning classification was implemented using
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Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is
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publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as
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the Jupyter notebook containing the command lines used for machine learning
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(http://doi.org/10.5281/zenodo.3534148).5
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3. Results
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3.1 Extended exposure to heat stress results in a proportional decrease of the rosette
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size and photosynthetic efficiency
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To assess whether high-throughput phenotyping can capture significant alterations in plant
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physiology caused by exposure to heat stress, we exposed three weeks old ArabidopsisOpen asset ↗zenodo · 10.5281/zenodo.3534148pdf-raw-page:5 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Nitrogen use efficiency (NUE) in crops is generally low, with more than 60% of applied nitrogen (N) being lost to the environment, which increases production costs and affects ecosystems and human habitats. To overcome these issues, the breeding of crop varieties with improved NUE is needed, requiring efficient phenotyping methods along with molecular and genetic approaches. To develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham. The results from the initial experiment showed that two relative N levels-5 mM and 20 mM, designated as low and optimum N, respectively-were ideal to screen a diverse range of wheat germplasm for NUE on the automated imaging phenotyping platform. In the second experiment, estimated plant parameters such as shoot biomass and top-view area, derived from digital images, showed high correlations with phenotypic traits such as shoot biomass and leaf area seven weeks after sowing, indicating that they could be used as surrogate measures of the latter. Plant growth analysis confirmed that the estimated plant parameters from the vegetative linear growth phase determined by the "broken-stick" model could effectively differentiate the performance of wheat varieties for NUE. Based on this study, vegetative phenotypic screens should focus on selecting wheat varieties under low N conditions, which were highly correlated with biomass and grain yield at harvest. Analysis indicated a relationship between controlled and field conditions for the same varieties, suggesting that greenhouse screens could be used to prioritise a higher value germplasm for subsequent field studies. Overall, our results showed that this phenotypic screening method is highly applicable and can be applied for the identification of N-efficient wheat germplasm at the vegetative growth phase.
Why it matches plant phenotyping methods自動画像フェノタイピング基盤を用い、デジタル画像から植物形質の推定値を抽出してN利用効率スクリーニング法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractTo develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham.
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 , was supplied depending on the crop growth stages (vegetative or reproductive).Open asset ↗lines:306-315Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements.
Why it matches plant phenotyping methods植物フェノタイピングのための3D再構成・葉セグメンテーション・深層学習による形質推定法の開発と手測定による検証が研究の中心である。
abstractwe present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting
Reproduction assets foundThe paper's leaf segmentation analysis code (a Tensorflow Mask R-CNN implementation used for the plant phenotyping measurements) is explicitly stated to be freely available on the authors' GitHub organization. The collected image/phenotype dataset (1728 images, 2592 manual data points) is described but no public data-Code · publicWe used our
implementation in Tensorflow, which is freely available on GitHub (See https://github.com/alcor-lab).Open asset ↗alcor-labpdf-page:10 lines:1-109Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
In this article we introduce a robusta coffee leaf images dataset called RoCoLe. The dataset contains 1560 leaf images with visible red mites and spots (denoting coffee leaf rust presence) for infection cases and images without such structures for healthy cases. In addition, the data set includes annotations regarding objects (leaves), state (healthy and unhealthy) and the severity of disease (leaf area with spots). Images were all obtained in real-world conditions in the same coffee plants field using a smartphone camera. RoCoLe data set facilitates the evaluation of the performance of machine learning algorithms used in image segmentation and classification problems related to plant diseases recognition. The current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2.
Why it matches plant phenotyping methodsコーヒー葉の病害状態と重症度を画像・アノテーションとして収録し、植物病害認識手法の評価用データセットとして提供することが中心であるため、植物フェノタイピング手法文献に含める。
abstractwe introduce a robusta coffee leaf images dataset called RoCoLe
Reproduction assets foundThe paper is a Data in Brief article introducing the RoCoLe dataset of 1560 annotated robusta coffee leaf images, explicitly stated as freely and publicly available on Mendeley Data with DOI 10.17632/c5yvn32dzg.2.Dataset · publicThe current dataset is freely and publicly available at https://doi.org/10.17632/c5yvn32dzg.2 .Open asset ↗10.17632/c5yvn32dzg.2lines:1-55Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
The extraction of desirable heritable traits for crop improvement from high-throughput phenotyping (HTP) observations remains challenging. We developed a modeling workflow named “Digital Plant Phenotyping Platform” (D3P), to access crop architectural traits from HTP observations. D3P couples the Architectural model of DEvelopment based on L-systems (ADEL) wheat (Triticum aestivum) model (ADEL-Wheat), which describes the time course of the three-dimensional architecture of wheat crops, with simulators of images acquired with HTP sensors. We demonstrated that a sequential assimilation of the green fraction derived from Red–Green–Blue images of the crop into D3P provides accurate estimates of five key parameters (phyllochron, lamina length of the first leaf, rate of elongation of leaf lamina, number of green leaves at the start of leaf senescence, and minimum number of green leaves) of the ADEL-Wheat model that drive the time course of green area index and the number of axes with more than three leaves at the end of the tillering period. However, leaf and tiller orientation and inclination characteristics were poorly estimated. D3P was also used to optimize the observational configuration. The results, obtained from in silico experiments conducted on wheat crops at several vegetative stages, showed that the accessible traits could be estimated accurately with observations made at 0° and 60° zenith view inclination with a temporal frequency of 100 °Cd (degree day). This illustrates the potential of the proposed holistic approach that integrates all the available information into a consistent system for interpretation. The potential benefits and limitations of the approach are further discussed.
Why it matches plant phenotyping methodsHTP画像から作物の建築形質を推定するD3Pワークフローを開発し、推定精度と観測配置を評価しており、フェノタイピング手法が研究の中心である。
abstractWe developed a modeling workflow named “Digital Plant Phenotyping Platform” (D3P), to access crop architectural traits from HTP observations.
Reproduction assets foundThe paper's D3P phenotyping platform code (coupling ADEL-Wheat with POV-Ray/PyProSAIL simulators used for the GF assimilation experiments) is explicitly stated to be freely available on GitHub under an MIT license. Other URLs (POV-Ray, OpenAlea, PyProSAIL, Python) are generic third-party dependencies, not paper assets.Code · publicThe code and user manual of D3P is freely available on GitHub ( https://github.com/lsymuyu/Digital-Plant-Phenotyping-Platform ). D3P is distributed under the free software open-source MIT license.Open asset ↗https://github.com/lsymuyu/Digital-Plant-Phenotyping-Platformlines:191-201Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Background Characterization and quantification of visual plant traits is often limited to the use of tools and software that were developed to address a specific context, making them unsuitable for other applications. CoverageTool is flexible multi-purpose software capable of area calculation in cm 2 , as well as coverage area in percentages, suitable for a wide range of applications. Results Here we present a novel, semi-automated and robust tool for detailed characterization of visual plant traits. We demonstrate and discuss the application of this tool to quantify a broad spectrum of plant phenotypes/traits such as: tissue culture parameters, ground surface covered by annual plant canopy, root and leaf projected surface area, and leaf senescence area ratio. The CoverageTool software provides easy to use functions to analyze images. While use of CoverageTool involves subjective operator color selections, applying them uniformly to full sets of samples makes it possible to provide quantitative comparison between test subjects. Conclusion The tool is simple and straightforward, yet suitable for the quantification of biological and environmental effects on a wide variety of visual plant traits. This tool has been very useful in quantifying different plant phenotypes in several recently published studies, and may be useful for many applications.
Why it matches plant phenotyping methods植物画像から面積や被覆率などの形質を定量化する半自動ソフトウェアが論文の中心であり、植物フェノタイピング手法に該当する。
abstractThe CoverageTool software provides easy to use functions to analyze images.
Reproduction assets foundThe paper's own phenotyping software CoverageTool is publicly released on GitHub with an explicit project home page, license, and availability statement. Supplementary image datasets (Additional files 5-8, 10-11) are described but only available via the article's supplementary material, not via an allowed URL.Code · publicsigned the phenotyping protocol and the tissue culture experiment. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
CoverageTool software and it’s additional files are in Additional files 1 , 2 , 3 , 4 , 5 , 6 , 7 and 8 .
Project name: CoverageTool
Project home page: https://github.com/lianneovnat/CoverageTool.git
Operating system(s): MS Windows: XP, Win7, Win10 etc.
Programming language: “C” with WIN32 (Visual Studio 2008 Express Edition)
Other requirements: Visual Studio 2008 Redistributal (or above)
License: GNU.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interesOpen asset ↗lianneovnat/CoverageToollines:346-425Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
ABSTRACT Automatically scoring plant traits using a combination of imaging and deep learning holds promise to accelerate data collection, scientific inquiry, and breeding progress. However, applications of this approach are currently held back by the availability of large and suitably annotated training datasets. Early training datasets targeted arabidopsis or tobacco. The morphology of these plants quite different from that of grass species like maize. Two sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait. Convolutional neural networks (CNNs) trained on entirely synthetic data provided predictive power for scoring leaf number in real-world images. This power was less than CNNs trained with equal numbers of real-world images, however, in some cases CNNs trained with larger numbers of synthetic images outperformed CNNs trained with smaller numbers of real-world images. When real-world training images were scarce, augmenting real-world training data with synthetic data provided improved prediction accuracy. Quantifying leaf number over time can provide insight into plant growth rates and stress responses, and can help to parameterize crop growth models. The approaches and annotated training data described here may help future efforts to develop accurate leaf counting algorithms for maize.
Why it matches plant phenotyping methodsトウモロコシの葉数という植物形質を対象に、合成・実画像データセットとCNNによる画像ベース計測手法を開発・評価しており、フェノタイピング手法が中心である。
abstractTwo sets of maize training data, one real-world and one synthetic were generated and annotated for late vegetative stage maize plants using leaf count as a model trait.
Reproduction assets foundThe paper deposits its phenotyping analysis scripts/source code in a public GitHub repository and used a public Zooniverse project to crowd-score the real-world maize leaf-count images; both are paper-specific, public, and actionable.Code · publicAdditional Information
The scripts and source code employed in this study have been deposited at https://github.com/freemao/MaizeLeafCounting.Images and annotations used in this study have been deposited with CyVerse [27].
The authors declare no competing interests.
References
1. Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. reviews genetics 11, 855 (2010).
2. Furbank, R. T. & Tester, M. Phenomics–technologies to relieve the phenotyping bottOpen asset ↗freemao/MaizeLeafCounting.Imagespdf-raw-page:9 lines:1-56Code / dataset availability confirmedbioRxiv · checked 9 Sept 2026
The aerial epidermis of plants plays a major role in their environment interactions, and the development of its cellular components -trichomes, stomata and pavement cells- is still not fully understood. We have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques. Quantification of the trichome and stomatal densities in the ILs revealed 18 genomic regions with a low trichome density and 4 ILs with a high stomatal density. We also found ILs with abnormal proportions of different trichome types and aberrant trichome morphologies. This work has led to the identification of new, unexplored genomic regions with roles in trichome and stomatal formation and provides an important dataset for further studies on tomato epidermal development that is publically available to the research community.
Why it matches plant phenotyping methods走査電子顕微鏡を用いた葉表皮の画像取得と、毛状突起・気孔密度および形態の定量が研究の中心であり、再利用可能な表現型データセットも提供しているため。
abstractWe have performed a detailed screen of the leaf epidermis of two generations of the well-established Solanum pennellii ac. LA716 x Solanum lycopersicum cv. M82 introgression line (IL) population using a combination of scanning electron microscopy techniques.
Reproduction assets foundThe paper states that all SEM micrographs used for the trichome/stomatal phenotyping screen are publicly available in the BioStudies database under accession S-BSST262. This is a paper-specific public asset (the SEM images underlying the phenotyping measurements). No author analysis code was deposited.Dataset · public321 study are available in the BioStudies database (http://www.ebi.ac.uk/biostudies) (McEntyre et al.,Open asset ↗BioStudiespdf-page:10 lines:1-44Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Afforestation projects for mitigating CO 2 emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome‐BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11‐year‐old Eucommia ulmoides plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( g smax ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11‐year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30‐year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14–0.35 kg‐C m −2 y −1 in simulations using parameters of phases (i)–(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.
Why it matches plant phenotyping methods植物器官・生態系の炭素固定と成長を推定する監視手法を、プロセスモデルと圃場データ、パラメータ最適化で構築・不確実性評価しており、植物の生理状態推定が中心です。
abstractWe proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process‐based ecosystem model and field data
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' biometric data for E. ulmoides allometric relationships and the files related to parameter optimization and simulation results on Zenodo, a public repository with a DOI. This is a paper-specific, publicly actionable asset. The NCDC GSOD meteoricalDataset · publicThe biometric data for allometric relationships of E. ulmoides and the files related to optimization and simulation results are available on Zenodo ( https://doi.org/10.5281/zenodo.2815612 ).Open asset ↗Zenodo · 10.5281/zenodo.2815612lines:393-549Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
We present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges. We introduce a novel approach to edge classification, which forms an important part of a method to both count the leaves and establish the leaf area of a growing plant from images obtained in a high-throughput phenotyping system. Our technique uses a relatively shallow convolutional neural network to classify image edges as background, plant edge, leaf-on-leaf edge or internal leaf noise. The edges themselves were found using the Canny edge detector and the classified edges can be used with simple image processing techniques to generate a region-based segmentation in which the leaves are distinct. This approach is strong at distinguishing occluding pairs of leaves where one leaf is largely hidden, a situation which has proved troublesome for plant image analysis systems in the past. In addition, we introduce the publicly available plant image dataset that was used for this work.
Why it matches plant phenotyping methods葉画像から葉数・葉面積を抽出するエッジ分類および画像分割手法を開発し、ハイスループット表現型解析で評価するとともにデータセットも公開しており、植物フェノタイピング手法が中心である。
abstractWe present an approach to leaf level segmentation of images of Arabidopsis thaliana plants based upon detected edges.
Reproduction assets foundThe paper introduces the Aberystwyth Leaf Evaluation Dataset (ALED), a public Zenodo deposit containing the Arabidopsis top-down plant images, hand-annotated ground truth, and segmentation evaluation software used directly in this work's leaf segmentation experiments.Dataset · publicArabidopsis plant image dataset
As part of this work we have generated a dataset of several thousand top
down images of growing Arabidopsis plants.
The data has been made available to the wider community as the Aberystwyth Leaf Evaluation Dataset (ALED)
1 1
1
The dataset is hosted at https://zenodo.org and it can be found from
https://doi.org/10.5281/zenodo.168158 .
The images were obtained
using the Photon Systems Instruments (PSI) PlantScreen plant scanner
[ 22 ] at the National Plant Phenomics Centre.
Alongside the images themselves are some ground truth hand-annotations and
software to evaluate leaf level segmentation against this ground truth.Open asset ↗zenodo · 10.5281/zenodo.168158lines:101-172Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Multispectral / hyperspectralLeafPhysiological trait estimationLeaf traitsWater status / transpiration
Approaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding. However, many key traits still require measurement using slow, labor-intensive, and destructive approaches. We investigated the potential to retrieve key traits associated with leaf source-sink balance and carbon-nitrogen status from leaf optical properties. Structural and biochemical traits and leaf reflectance (500-2400 nm) of eight crop species were measured and used to develop predictive 'spectra-trait' models using partial least squares regression. Independent validation data demonstrated that the models achieved very high predictive power for C, N, C:N ratio, leaf mass per area, water content, and protein content (R2>0.85), good predictive capability for starch, sucrose, glucose, and free amino acids (R2=0.58-0.80), and some predictive capability for nitrate (R2=0.51) and fructose (R2=0.44). Our spectra-trait models were developed to cover the trait space associated with food or biofuel crop plants and can therefore be applied in a broad range of phenotyping studies.
Why it matches plant phenotyping methods葉の光学特性から複数の生理・構造形質を非破壊かつ高スループットに推定する分光法と予測モデルを開発・独立検証しており、フェノタイピング手法が中心である。
abstractApproaches that enable high-throughput, non-destructive measurement of plant traits are essential for programs seeking to improve crop yields through physiological breeding.
Reproduction assets foundThe paper states that its leaf spectra, biochemical trait data, and PLSR R code were made publicly available via the EcoSIS spectral library (doi:10.21232/C2GM2Z). This is a paper-specific, public phenotyping asset (spectra–trait dataset plus analysis code), but no URL is present in the allowed_urls list, so it cannot Dataset · publicLeaf spectra, leaf trait data and the R code for the PLSR model are available from the EcoSIS
spectral library (ecosis.org), doi:10.21232/C2GM2Z.EcoSIS · doi:10.21232/C2GM2Zpdf-raw-page:17 lines:1-10Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual); or (2) using remote sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages: 1) image segmentation, to identify individual trees and estimate structural traits; 2) ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and 3) predictions for segmented crowns for the full remote sensing footprint at the NEON sites. The R 2 values on held out test data ranged from 0.41 to 0.75 on held out test data. The ensemble approach performed better than single partial least squares models. Carbon performed poorly compared to other traits (R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R 2 of 0.62 on held out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between to 0.26. We used the pipeline to produce individual level trait data for ∼5 million individual crowns, covering a total extent of ∼360 km 2 . This large dataset allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.
Why it matches plant phenotyping methods個体樹木の構造・葉機能形質をリモートセンシング画像から推定するパイプラインを開発・評価しており、形質取得手法が研究の中心である。
abstractRemote sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals.
Leaf tooth can indicate several systematically informative features and is extremely useful for circumscribing fossil leaf taxa. Moreover, it can help discriminate species or even higher taxa accurately. Previous studies extract features that are not strictly defined in botany; therefore, a uniform standard to compare the accuracies of various feature extraction methods cannot be used. For efficient and automatic retrieval of plant leaves from a leaf database, in this study, we propose an image-based description and measurement of leaf teeth by referring to the leaf structure classification system in botany. First, image preprocessing is carried out to obtain a binary map of plant leaves. Then, corner detection based on the curvature scale-space (CSS) algorithm is used to extract the inflection point from the edges; next, the leaf tooth apex is extracted by screening the convex points; then, according to the definition of the leaf structure, the characteristics of the leaf teeth are described and measured in terms of number of orders of teeth, tooth spacing, number of teeth, sinus shape, and tooth shape. In this manner, data extracted from the algorithm can not only be used to classify plants, but also provide scientific and standardized data to understand the history of plant evolution. Finally, to verify the effectiveness of the extraction method, we used simple linear discriminant analysis and multiclass support vector machine to classify leaves. The results show that the proposed method achieves high accuracy that is superior to that of other methods.
Why it matches plant phenotyping methods葉の画像から葉縁の歯状形態を自動抽出・測定する手法を開発し、分類精度で有効性を検証しており、植物表現型取得が研究の中心である。
abstractwe propose an image-based description and measurement of leaf teeth
Reproduction assets foundThe paper's Data Availability statement deposits the minimal dataset (leaf tooth feature data underlying the measurements) on Figshare at a public link. No author analysis code or trained models are explicitly deposited; the Swedish Leaf and Flavia datasets are cited prior public resources, not paper-specific assets.Dataset · publicData Availability: The minimal data set has been uploaded to Figshare and is available at the following link: https://figshare.com/s/60d984461451a0c69e8e .Open asset ↗Figsharelines:145-151Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background The development of leaf area is one of the fundamental variables to quantify plant growth and physiological function and is therefore widely used to characterize genotypes and their interaction with the environment. To date, analysis of leaf area often requires elaborate and destructive measurements or imaging-based methods accompanied by automation that may result in costly solutions. Consequently in recent years there is an increasing trend towards simple and affordable sensor solutions and methodologies. A major focus is currently on harnessing the potential of applications developed for smartphones that provide access to analysis tools to a wide user basis. However, most existing applications entail significant manual effort during data acquisition and analysis. Results With the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field. To distinguish between plant tissue and background the core of the application comprises different classification approaches that can be parametrized by users delivering results on-the-fly. We demonstrate the practical applications of computing projected leaf area based on two case studies with Eragrostis and Musa plants. These studies showed highly significant correlations with destructive measurements of leaf area and biomass from both ground truth measurements and estimations from well-established screening systems. Conclusions We show that a smartphone together with our analysis tool Plant Screen Mobile is a suitable platform for rapid quantification of leaf and shoot development of various plant architectures. Beyond the estimation of projected leaf area the app can also be used to quantify color and shape parameters of other plant material including seeds and flowers.
Why it matches plant phenotyping methodsスマートフォン画像と分類アルゴリズムによる葉面積・バイオマス推定アプリを開発し、破壊測定および既存システムと検証しており、植物表現型取得が中心である。
abstractWith the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field.
Reproduction assets foundThe authors deposited the plant image data and corresponding ground truth measurements from the banana and Eragrostis case studies in the e!DAL research data publication system (DOI 10.25622/FZJ/2018/1), a paper-specific public asset. The project homepage (fz-juelich.de/ibg/ibg-2/psm) hosts the app and manual but is a Dataset · publichave no competing interests.
Availability of data and materials
The app is accompanied by a detailed manual and checkerboard images for calibration, which can also be downloaded from the project homepage. The datasets generated and/or analyzed during the current study are available in the e!DAL research data publication system, http://dx.doi.org/10.25622/FZJ/2018/1 [ 30 ].
Availability and requirements
Project name: Plant Screen Mobile. Project home page: https://fz-juelich.de/ibg/ibg-2/psm . Operating system(s): Android OS 4.0 (Ice Cream Sandwich) or higher. Programming language: Java. Other requirements: OpenCV manager (will be installed during Plant Screen Mobile setup). License: GNU GOpen asset ↗e!DAL research data publication system · 10.25622/FZJ/2018/1lines:127-198Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models' key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
Why it matches plant phenotyping methods水稲の葉面積指数（LAI）という植物形質を、ハイパースペクトルデータと機械学習で推定する手法を開発・比較・検証しており、形質取得手法が研究の中心です。
abstractThe performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study.
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 · publicS1 File. The dataset used in this study.Open asset ↗lines:291-301Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system.
Why it matches plant phenotyping methodsリモートセンシング、気候データ、機械学習を統合して葉形質を推定する処理チェーンを開発・比較検証しており、植物形質取得が中心的である。
abstractThis paper introduces a modular processing chain to derive global high-resolution maps of leaf traits.
Reproduction assets foundThe paper's core phenotyping input is the public TRY plant trait database, from which the authors obtained in-situ leaf trait measurements (SLA, LDMC, LNC, LPC, LNPR) that they gap-filled and spatialized. No author analysis code, trained models, or data products with a public authors' URL are stated in the supplied.Dataset · publicOur methods first involve using a random forest with surrogates technique to gap fill the largest global plant trait database available (TRY, ( https://www.try-db.org// ))Open asset ↗TRY · TRYlines:106-109Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, petiole number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform also quantified shoot and root shapes in terms of principal components, which do not have traditional, manually measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a rapid, automated image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態を画像から自動抽出する解析プラットフォームを開発し、手動測定との相関や反復性を検証しており、表現型取得手法が研究の中心である。
abstractwe developed an automated image analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis scripts on GitHub and the carrot images plus unfiltered F2 SNP calls on FigShare, both with public URLs.Code · publicScripts for data processing, visualization, and QTL mapping are available on GitHub at https://github.com/mishaploid/carrot-image-analysis .Open asset ↗mishaploid/carrot-image-analysislines:960-975Dataset · publicUnfiltered SNPs from the F 2 mapping population (variant call format) and images are deposited on FigShare at https://doi.org/10.6084/m9.figshare.c.4300439.v1 .Open asset ↗10.6084/m9.figshare.c.4300439.v1lines:960-975Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 14 Sept 2026
In the past years, the diversity of Capsicum has been mainly investigated through genetics and genomics approaches, fewer efforts have been made in the field of plant phenomics. Assessment of crop traits with high-throughput methodologies could enhance the knowledge of the plant phenome, giving at the same time a key contribution to the understanding of the function of many genes. In this study, a wide germplasm collection of 307 accessions retrieved from 48 world regions, and belonging to nine Capsicum species was characterized for 54 plant, leaf, flower and fruit traits. Conventional descriptors and semi-automated tools based on image analysis and colour coordinate detection were used. Significant differences were found among accessions, between species and between sweet and spicy cultivated types, revealing a large diversity. The results highlighted how the domestication process and the continued selection have increased the variability of fruit shape and colour. Hierarchical clustering based on conventional and fruit morphological descriptors reflected the separation of species on the basis of their phylogenetic relationships. These observations suggested that the flow between distinct gene pools could have contributed to determine the similarity of the species on the basis of morphological plant and fruit parameters. The approach used represents the first high-throughput phenotyping effort in Capsicum spp. aimed at broadening the knowledge of the diversity of domesticated and wild peppers. The data could help to select best the candidates for breeding and provide new insight into the understanding of the genetic base of the fruit shape of pepper.
Why it matches plant phenotyping methods大規模な植物表現型解析を主題とし、半自動画像解析・色座標検出ツールを用いて植物、葉、花、果実の形質を高スループットに取得しているため、方法の実質的適用に該当する。
abstractAssessment of crop traits with high-throughput methodologies could enhance the knowledge of the plant phenome
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 are available online at http://www.mdpi.com/2223-7747/7/4/103/s1 , Figure S1: Distribution of fruit traits in the 307 pepper genotypes under study, Figure S2a: Loading plot of the first and second component based on eight highly correlated fruit traits in all species under study, Figure S2b: Loading plot of the first and second component based on eight highly fruit correlated traits in domesticated and wild species, Figure S3: Hierarchical clustering based on eight highly correlated fruit traits and two most significant plant traits, Table S1: Mean, range, significance of the means within each species and among the 9 Capsicum species for plant traits (BOpen asset ↗lines:943-957Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( www.try-db.org ) to be included in the next version release.
Why it matches plant phenotyping methods植物の形態・機能形質を大規模に収録した再利用可能なデータベースであり、植物フェノタイピング用データセットとして中心的な貢献がある。
abstractThe Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome.
Reproduction assets foundThe paper's own Tundra Trait Team (TTT) trait database (raw and cleaned csv data) plus the authors' R data-cleaning scripts are publicly released in the authors' GitHub repository, with additional deposition in TRY and the Polar Data Catalogue.Dataset · publicthis cleaning protocol is primarily useful for species with large num‐
bers of observations of a given trait, and that much of the variation
within a species may be due to environmental or other differences
among sites (not error).
2.3 | Data availability and access
The TTT database will be maintained at the GitHub repository
(https://github.com/TundraTraitTeam/TraitHub). Trait data collec‐
tion is ongoing; thus, we will periodically release updated versions
of the database. A new version number will be assigned every time
there is a database update, and old database versions will be ar‐
chived for reference. A static version of the cleaned database (v. 1.0)
will also be available at the PolOpen asset ↗TundraTraitTeam/TraitHubpdf-raw-page:7 lines:1-59Code · publicSoftware format: csv file and GitHub repository with data cleaning scripts in R; con‐
tribution to TRY plant trait database (www.try-db.org) to be included in the next ver‐
sion release.Open asset ↗pdf-raw-page:4 lines:1-79Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article presents experimental data describing the physiology and morphology of sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France). Here, we provide data including specific leaf area, osmotic potential and adjustment, carbon isotope discrimination, leaf transpiration, plant architecture: plant height, leaf number, stem diameter. We also provide leaf areas of individual organs through time and growth rate during the stress period, environmental data such as temperatures, wind and radiation during the experiment. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the physiological basis of heterosis. It is available on the following repository: https://doi.org/10.25794/phenotype/er6lPW7V.
Why it matches plant phenotyping methods植物の形態・生理形質を高スループット表現型解析プラットフォームで取得した再利用可能なデータセットであり、表現型データ資源の提供が中心です。
abstractDrought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen at INRA Toulouse (France).
Reproduction assets foundThe article is a Data in Brief paper whose entire content is the paper's own eco-physiological phenotyping dataset (24 sunflower genotypes, water deficit, Heliaphen platform). The authors explicitly deposit the data publicly in the SUNRISE Phenotype Archive with DOI 10.25794/phenotype/er6lPW7V, described as csv/xls/pdfDataset · publicData accessibility Data are with this article and also publicly available in the SUNRISE
Archive depository with following DOI: 10.25794/phenotype/er6lPW7VOpen asset ↗10.25794/phenotype/er6lPW7Vpdf-raw-page:3 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as leaf count, preferably across a variety of species and growth conditions. However, current leaf counting methods do not work across species or conditions and therefore may lack broad utility. In this paper, we present Pheno-Deep Counter, a single deep network that can predict leaf count in two-dimensional (2D) plant images of different species with a rosette-shaped appearance. We demonstrate that our architecture can count leaves from multi-modal 2D images, such as visible light, fluorescence and near-infrared. Our network design is flexible, allowing for inputs to be added or removed to accommodate new modalities. Furthermore, our architecture can be used as is without requiring dataset-specific customization of the internal structure of the network, opening its use to new scenarios. Pheno-Deep Counter is able to produce accurate predictions in many plant species and, once trained, can count leaves in a few seconds. Through our universal and open source approach to deep counting we aim to broaden utilization of machine learning-based approaches to leaf counting. Our implementation can be downloaded at https://bitbucket.org/tuttoweb/pheno-deep-counter.
Why it matches plant phenotyping methods植物画像から葉数を抽出する汎用深層学習手法を開発しており、植物フェノタイピング手法が研究の中心である。
abstractwe present Pheno-Deep Counter, a single deep network that can predict leaf count in two-dimensional (2D) plant images of different species with a rosette-shaped appearance.
Reproduction assets foundThe authors explicitly deposit their Pheno-Deep Counter source code and pre-trained model in a public Bitbucket repository; the plant image datasets used (CVPPP, Cruz et al., komatsuna, Aberystwyth) are cited prior-work datasets rather than paper-specific deposits.Code · publicinput/modalities without changing the overall architecture. This simplifies adoption and permits the sharing of model updates when new experiments have been made available on the basis of our architecture. Therefore, by placing our pre‐trained PhenoDC and source code (and instructions) into the publicly available repository at https://bitbucket.org/tuttoweb/pheno-deep-counter , we hope to accelerate the adoption of such methods in plant phenotyping analysis.Open asset ↗https://bitbucket.org/tuttoweb/pheno-deep-counterlines:340-387Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Carrot is a globally important crop, yet efficient and accurate methods for quantifying its most important agronomic traits are lacking. To address this problem, we developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots, which are necessary to advance carrot breeding and genetics. This method reliably measured variation in shoot size and shape, leaf number, petiole length, and petiole width as evidenced by high correlations with hundreds of manual measurements. Similarly, root length and biomass were accurately measured from the images. This platform quantified shoot and root shapes in terms of principal components, which do not have traditional, manually-measurable equivalents. We applied the pipeline in a study of a six-parent diallel population and an F 2 mapping population consisting of 316 individuals. We found high levels of repeatability within a growing environment, with low to moderate repeatability across environments. We also observed co-localization of quantitative trait loci for shoot and root characteristics on chromosomes 1, 2, and 7, suggesting these traits are controlled by genetic linkage and/or pleiotropy. By increasing the number of individuals and phenotypes that can be reliably quantified, the development of a high-throughput image analysis pipeline to measure carrot shoot and root morphology will expand the scope and scale of breeding and genetic studies.
Why it matches plant phenotyping methodsニンジンのシュート・根の形態形質を画像から自動抽出する高スループット解析基盤を開発し、手動測定との相関や反復性で検証しているため、表現型取得法が研究の中心である。
abstractwe developed an automated analysis platform that extracts components of size and shape for carrot shoots and roots
Reproduction assets foundThe paper's Data Availability statement provides public, paper-specific assets: carrot plant images via a CyVerse download link, and authors' scripts for data processing, visualization, and QTL mapping on GitHub. Both are directly tied to this paper's phenotyping measurements and analysis.Dataset · publicAutomated image analysis for genetic studies of carrot shoot and root shape
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5 Data Availability
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All images, scripts, and sequence data used in this study are publicly available. Images are available
539
at https://de.cyverse.org/dl/d/2F1B4398-9D2E-4BF4-BFFF-65F507DB6865/sampleCarrotImages.zip
540
and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
541
for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
542
processing, visualization, and QTL mapping are available on GitHub at
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https://github.com/mishaploid/carrot-image-Open asset ↗CyVersepdf-raw-page:14 lines:1-65Code · publicF4-BFFF-65F507DB6865/sampleCarrotImages.zip
540
and will also be deposited in the Dryad digital repository (https://datadryad.org/). Custom algorithms
541
for image analysis are accessible on CyVerse as part of the PhytoMorph ToolKit. Scripts for data
542
processing, visualization, and QTL mapping are available on GitHub at
543
https://github.com/mishaploid/carrot-image-analysis. SNPs from the F2 mapping population will be
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deposited as VCF files on FigShare.
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6 Conflict of Interest
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The authors declare that the research was conducted in the absence of any commercial or financial
547
relationships that could be construed as a potential conflict of interest.
548
7 Author ContributionOpen asset ↗GitHub · mishaploid/carrot-image-analysispdf-raw-page:14 lines:1-65Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
This study exhaustively explores leaf features seeking diagnostic characters to aid the classification (assigning cases to groups, i.e. populations to taxa) in a polyploid plant-species complex. A challenging case study was selected: Veronica subsection Pentasepalae, a taxonomically intricate group. The "divide and conquer" approach was implemented-that is, a difficult primary dataset was split into more manageable subsets. Three techniques were explored: two data-mining tools (artificial neural networks and decision trees) and one unsupervised discriminant analysis. However, only the decision trees and discriminant analysis were finally used to select diagnostic traits. A previously established classification hypothesis based on other data sources was used as a starting point. A guided discriminant analysis (i.e. involving manual character selection) was used to produce a grouping scheme fitting this hypothesis so that it could be taken as a reference. Sequential unsupervised multivariate analysis enabled the recognition of all species and infraspecific taxa; however, a suboptimal classification rate was achieved. Decision trees resulted in better classification rates than unsupervised multivariate analysis, but three complete taxa were misidentified (not present in terminal nodes). The variable selection led to a different grouping scheme in the case of decision trees. The resulting groups displayed low misclassification rates when analyzed using artificial neural networks. The decision trees as well as the discriminant analysis are recommended in the search of diagnostic characters. Due to the high sensitivity that artificial neural networks have to the combination of input/output layers, they are proposed as evaluation tools for morphometric studies. The "divide and conquer" principle is a promising strategy, providing success in the present case study.
Why it matches plant phenotyping methods植物の葉形態形質を対象に、決定木・判別分析・ニューラルネットワークを用いて診断形質を選抜・評価する方法論が研究の中心であり、分類目的だけのルーチン測定ではない。
abstractThree techniques were explored: two data-mining tools (artificial neural networks and decision trees) and one unsupervised discriminant analysis.
Reproduction assets foundThe paper deposits its morphometric phenotype dataset (raw and population-averaged leaf measurements) and its analysis scripts (decision tree and ANN workflows) in three public GitHub repositories under the authors' account, with explicit availability statements in the text.Dataset · publicThe matrices containing raw data and all the average values per population are available on GitHub ( https://github.com/NoeLG4/morpho.dataset ).Open asset ↗NoeLG4/morpho.datasetlines:116-194Code · publicThe script used to analyze the data is available on GitHub ( https://github.com/NoeLG4/morpho.DT ).Open asset ↗NoeLG4/morpho.DTlines:206-210Code · publicThe script used for analyzing the data and generating the graphics is available on GitHub ( https://github.com/NoeLG4/morpho.ANN ).Open asset ↗NoeLG4/morpho.ANNlines:211-264Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. RESULTS: A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. CONCLUSION: The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.
Why it matches plant phenotyping methods画像系列から葉・茎を自動検出し、葉数・葉長などの表現型を抽出する手法の開発が中心で、ベンチマークデータセットも提供しているため。
abstractThe central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD.
Reproduction assets foundThe paper releases UNL-CPPD, a maize component plant phenotyping dataset with original images, ground truth, and annotated images, publicly downloadable from the authors' site. The paper's holistic phenotyping analysis also uses Panicoid Phenomap-1, available from the same authors' site. No author analysis code is madeDataset · publicmanuscript.
Acknowledgements
Authors appreciate Dipal Bhandari for his contributions in preparing the ground truth of the dataset.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
UNL-CPPD public dataset is released with the paper and may be freely downloaded from http://plantvision.unl.edu/dataset . The dataset contains original images, ground truth and annotated images.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Funding
The authors would like to thank the Agricultural Research Division in the Institute of Agriculture and Natural Resources of the UNL, USA, for proviOpen asset ↗plantvision.unl.edu · UNL-CPPDlines:1781-1890Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific "dynamic features." According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice.
Why it matches plant phenotyping methodsイネ葉画像を連続取得・画像処理し、新規形状指標を開発して窒素ストレス診断モデルを検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractScanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB.
Reproduction assets foundThe paper's Data Availability statement deposits all underlying data (rice leaf image-derived phenotype measurements used for nitrogen diagnosis) on figshare with a public DOI link.Dataset · publicAll data underlying the findings are fully available without restriction from figshare: http://dx.doi.org/10.6084/m9.figshare.5965846 .Open asset ↗figshare · 10.6084/m9.figshare.5965846lines:918-936Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness ( R ² = 0.9457-0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship ( R ² = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.
Why it matches plant phenotyping methodsKinectによる多視点3D再構成とアルゴリズムを用いて、植物の高さ・葉面積・体積・バイオマス関連形質を自動測定し、精度と頑健性も検証しているため、植物フェノタイピング手法が中心である。
abstractThe paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables.
Reproduction assets foundThe paper's Supplementary Materials, available at the MDPI s1 URL, explicitly contain the paper-specific phenotyping assets: point clouds and meshes shown in figures, the datasets used for the scatter plots of Kinect-measured growth parameters vs. reference measurements, and interactive MATLAB 3D scatter plots. No codeDataset · publicThe following are available online at http://www.mdpi.com/1424-8220/18/3/806/s1 . Supplementary data associated with this article have been provided. These data include the point clouds and meshes appeared in figures, the data sets used by scatter plots, and interactive MATLAB 3D scatter plots.Open asset ↗lines:114-135Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 10 Sept 2026
BACKGROUND: Image-based plant phenotyping has become a powerful tool in unravelling genotype-environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a 'gold-standard' and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count. RESULTS: to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability. CONCLUSIONS: While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem.
Why it matches plant phenotyping methods画像ベース植物フェノタイピングにおける葉数アノテーションの観察者間・内変動を、専用ツール、市民参加型基盤、機械学習アルゴリズムと比較検証しており、測定手法の技術的妥当性評価が中心である。
abstractHere we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.
Reproduction assets foundThe article states that the Arabidopsis image dataset used for the leaf-counting observer-variability study is publicly available at the plant-phenotyping.org datasets page, and the citizen-science annotations were collected via the authors' public Zooniverse 'Leaf Targeting' project. No author analysis code or trainedDataset · publicAvailability of data and materials
The image dataset used in this article is available at http://www.plant-phenotyping.org/datasets .Open asset ↗plant-phenotyping.orglines:307-379Dataset · publicThe A data (RPi) were included as part of a larger citizen-powered study (“Leaf Targeting”, available at https://www.zooniverse.org/projects/venchen/leaf-targeting ) built on ZooniverseOpen asset ↗Zooniverse · venchen/leaf-targetinglines:132-149Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 10 Sept 2026
Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.
Why it matches plant phenotyping methods合成植物画像によるデータセット拡張と深層学習を用いたロゼット植物の葉数推定手法が中心であり、植物表現型取得・推定の技術開発に該当する。
abstractDeep learning presents many opportunities for image-based plant phenotyping.
Reproduction assets foundThe paper publicly releases its generated/analysed leaf-counting datasets (including synthetic rosette images and real Ara2012/Ara2013-Canon subsets) via a figshare deposit with an explicit availability statement, and it uses the public IPPN PRL dataset as its real-plant phenotyping input. The L-system model code is inDataset · publicThe datasets generated and analysed during the current study are available for download at the following url: https://figshare.com/articles/SATLC-28-09-17_zip/5450080 .Open asset ↗figshare · SATLC-28-09-17_zip/5450080lines:221-323Dataset · publicwe use a publicly available plant phenotyping dataset from the International Plant Phenotyping Network (IPPN), Footnote 1 referred to by its authors as the PRL datasetOpen asset ↗International Plant Phenotyping Network (IPPN)lines:75-84Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Quantitative phenotyping of downy mildew sporulation is frequently used in plant breeding and genetic studies, as well as in studies focused on pathogen biology such as chemical efficacy trials. In these scenarios, phenotyping a large number of genotypes or treatments can be advantageous but is often limited by time and cost. We present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient. The pipeline was tested on images from leaf disc assay experiments involving two F 1 grapevine families, one that had glabrous leaves (Vitis rupestris B38 × ‘Horizon’ [RH]) and another that had leaf trichomes (Horizon × V. cinerea B9 [HC]). Correlations between computer vision and manual visual ratings reached 0.89 in the RH family and 0.43 in the HC family. Additionally, we were able to use the computer vision system prior to sporulation to measure the percent leaf trichome area. We estimate that an experienced rater scoring sporulation would spend at least 90% less time using the computer vision system compared with the manual visual method. This will allow more treatments to be phenotyped in order to better understand the genetic architecture of downy mildew resistance and of leaf trichome density. We anticipate that this computer vision system will find applications in other pathosystems or traits where responses can be imaged with sufficient contrast from the background.
Why it matches plant phenotyping methods画像からブドウ葉のべと病胞子形成面積と毛状突起面積を定量推定するコンピュータビジョン手法の開発・検証が中心であり、植物表現型手法に該当する。
abstractWe present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient.
Reproduction assets foundThe paper's authors publicly deposited the four Python/OpenCV scripts (crop.py, values.py, circles.py, lines.py) used to quantify downy mildew sporulation and leaf trichome area from smartphone leaf-disc images, with an explicit availability statement and URL. No phenotype dataset or image deposit is stated in the textCode · publicd the threshold and Hough
circle transform algorithm parameters and lines.py is used to find the
Hough line transform algorithm parameters. All of the scripts are
parallelized, such that, when run, they automatically use all available
CPU cores for faster image processing. The scripts and a guide for the
scripts can be found at https://github.com/kdivilov/downymildew-CV.For the computer vision system, the images were initially cropped
so that only leaf discs that were fully contained in an image were kept
(Fig. 1). The cropped images were then converted to Lab color space,
which, unlike RGB color space, includes all colors visible to the
human eye, with all of the layers thresholded using usOpen asset ↗kdivilov/downymildew-CVpdf-raw-page:2 lines:79-134Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Plant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area. Results The new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels. Conclusions The imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.
Why it matches plant phenotyping methods植物の葉面積を画像から抽出する低コスト手法と装置を開発し、精度検証および圃場でのハイスループット適用まで行っており、フェノタイピング手法が研究の中心である。
abstractwe developed a low-cost, 2D imaging method, hereafter called PYM.
Reproduction assets foundThe authors explicitly state that the original pictures and PYM analysis code are publicly available in a GitHub repository. The lettuce agrivoltaic phenotype datasets are not public (confidentiality agreement; available on request), so only the code qualifies as a public paper-specific asset.Code · publicThe original pictures and code for PYM method used in the current study are available in the repository https://github.com/bevalle/pym .Open asset ↗bevalle/pymlines:174-250Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Why it matches plant phenotyping methods植物表現型解析用の合成画像生成ネットワークを開発し、葉数を条件付けたデータセットを作成・評価しており、表現型取得・解析ワークフローの技術的貢献が中心である。
abstractWe propose a new Ax dataset of artificial plants images, obtained by our ARIGAN.
Reproduction assets foundThe paper's authors publicly released the Ax dataset of 57 synthetic Arabidopsis plant images generated by ARIGAN (with leaf-count annotations in a CSV), which directly reproduces the paper's phenotyping data contribution. The CVPPP 2017 LCC dataset is the training input but is cited prior work, not a paper-specific.Dataset · publicr quantitative experiments show that the extension of the training dataset with the images in Ax improved the testing error and reduced overfitting. We run a 4-fold cross validation experiment on A4 dataset. Evaluation metrics of our experiments are reported in Table 1 . Our synthetic dataset Ax is available to download at \url http://www.valeriogiuffrida.academy/ax.
Acknowledgements
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1, and also by the BBSRC grant BB/P023487/1.
References
[1]
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron,
N. Bouchard, and Y. Bengio.
Theano: new features and speed improvements.
Deep LearninOpen asset ↗Axlines:101-153Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 15 Sept 2026
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DC-GAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128 x 128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Why it matches plant phenotyping methods植物フェノタイピング用の合成画像生成手法を開発し、葉数条件付き生成とデータセットの評価を行っており、表現型データ取得・解析基盤が研究の中心です。
abstractWe propose a new Ax dataset of artificial plants images, obtained by our ARIGAN.
Reproduction assets foundThe paper's authors publicly released their synthetic Ax dataset of 57 GAN-generated Arabidopsis plant images, with an explicit download URL stated in the paper. This is a paper-specific, publicly available asset directly tied to this paper's phenotyping analysis. No code or trained model availability is stated.Dataset · publicBatch normalization: Accelerating
Evaluation metrics of our experiments are reported in Ta- deep network training by reducing internal covariate shift.
In F. Bach and D. Blei, editors, Proceedings of the 32nd In-
ble 1. Our synthetic dataset Ax is available to download at
ternational Conference on Machine Learning, volume 37 of
http://www.valeriogiuffrida.academy/ax. Proceedings of Machine Learning Research, pages 448–456,
Lille, France, 07–09 Jul 2015. PMLR.
Acknowledgements [14] Y. LeCunn. The MNIST database of handwritten digits,
http://yann.lecun.com/exdb/mnist/.
This work was supported by The Alan Turing Institute un- [15] A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier non-
der theOpen asset ↗pdf-layout-page:7 lines:1-56Code / dataset availability confirmedarXiv · checked 10 Sept 2026
In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets.
Why it matches plant phenotyping methodsロゼット葉の画像から葉数を推定する深層学習手法を提案し、セグメンテーションと葉数カウントをデータセットで評価しており、植物表現型取得手法が中心である。
abstractcounting rosette leaves from an RGB image, an important task in plant phenotyping
Reproduction assets foundThe paper's authors explicitly state their leaf counting/segmentation code is publicly available on GitHub, and the CVPPP2017 Leaf Counting Challenge dataset used for all experiments is publicly hosted at plant-phenotyping.org.Code · publicCode is publicly available here. 1 1Open asset ↗lines:215-305Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 14 Sept 2026
Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana .
Why it matches plant phenotyping methods植物フェノタイピング向けのオープンソース深層学習ツールを開発し、複数のベンチマークで性能評価しているため、表現型取得・解析手法が研究の中心です。
abstractwe introduce an open-source deep learning tool called Deep Plant Phenomics.
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 · publicDeep Plant Phenomics is available for download at https://github.com/usaskdapper/deepplantphenomics . Detailed documentation describing installation and usage of the platform is available in the software repository.Open asset ↗usaskdapper/deepplantphenomicslines:49-57Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Diverse leaf forms ranging from simple to compound leaves are found in plants. It is known that the final leaf size and shape vary greatly in response to developmental and environmental changes. However, changes in leaf size and shape have been quantitatively characterized only in a limited number of species. Here, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula . The software processes high quality leaf images in an automated or manual fashion to generate size and shape parameters for both blades and serrations. In addition, it generates spectral components for each leaflets using elliptic Fourier transformation. Reconstruction studies show that the spectral components can be reliably used to rebuild the original leaflet images, with low, and middle and high frequency spectral components corresponding to the outline and serration of leaflets, respectively. The software uses artificial neutral network or k -means classification method to classify leaflet groups that are developed either on successive nodes of stems within a genotype or among genotypes such as natural variants and developmental mutants. The automated feature of the software allows analysis of thousands of leaf samples within a short period of time, thus facilitating identification, comparison and classification of leaf groups based on leaflet size, shape and tooth features during leaf development, and among induced mutants and natural variants.
Why it matches plant phenotyping methods葉の画像からサイズ・形状・鋸歯などの表現型を自動抽出・分類するソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractHere, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula .
Reproduction assets foundThe paper deposits original M. truncatula leaf images used for LeafletAnalyzer phenotyping in three public Harvard Dataverse datasets, and raw measured data are in Supplementary Files 1–3. No public code deposit for the LeafletAnalyzer software is stated.Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/ZPGVPPlines:40-52Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/QLXGBGlines:40-52Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/29PJR1lines:40-52Supplement · publicRaw data are listed in Supplementary Files 1 – 3 .Open asset ↗lines:40-52Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate ( P max ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal P max responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured P max , especially in lower layers, was consistently higher than the optimal P max indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
Why it matches plant phenotyping methodsステレオカメラによる3D植物再構成を用いてイネの群落構造形質を取得し、光環境・光合成との関係を解析しており、表現型取得ワークフローが研究の中心である。
abstractRay tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S2
Physiological characteristics of the 15 parental MAGIC lines + IR64 used in the initial screening . All measurements, apart from harvest dry weight and seed dry weight, were taken 55–60 days after transplanting (DAT), corresponding to the vegetative growth stage.Open asset ↗lines:538-570Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Leaf morphometrics are used frequently by several disciplines, including taxonomists, systematists, developmental biologists, morphologists, agronomists, and plant breeders to name just a few. Leaf shape is highly variable and can be used for identifying species or genotypes, developmental patterning within and among individuals, assessing plant health, and measuring environmental impacts on plant phenotype. Traditional leaf morphometrics requires hand tools and access to specimens, but modern efforts to digitize botanical collections make digital morphometrics a readily accessible and scientifically rigorous option. Here we provide detailed instructions for performing some of the most informative digital geometric morphometric analyses available: generalized Procrustes analysis, elliptical Fourier analysis, and shape features. This comprehensive procedure for leaf shape analysis is comprised of six main sections: A) scanning of material, B) acquiring landmarks, C) analysis of landmark data, D) isolating leaf outlines, E) analysis of leaf outlines, and F) shape features. This protocol provides a detailed reference for applying landmark and outline analysis to leaf shape as well as describing leaf shape features, thus empowering researchers to perform high throughput phenotyping for diverse applications.
Why it matches plant phenotyping methods葉形状のデジタル幾何形態計測を体系化したプロトコルであり、スキャン、ランドマーク取得、輪郭抽出、形状解析を中心に扱うため、植物表現型取得法が中核です。
abstractHere we provide detailed instructions for performing some of the most informative digital geometric morphometric analyses available: generalized Procrustes analysis, elliptical Fourier analysis, and shape features.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicExamples of R scripts and ImageJ macros referenced throughout the protocol can be freely downloaded from GitHub (link:
https://github.com/htsvoboda/LeafGeometricMorphometrics.gitOpen asset ↗htsvoboda/LeafGeometricMorphometrics · LeafGeometricMorphometricslines:78-117Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Background Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. Results Here we present rosettR , a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. Conclusions Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.
Why it matches plant phenotyping methods植物ロゼット面積と成長率を画像から高スループットに測定するプロトコルおよびRソフトウェアの開発が研究の中心であり、再現性・精度も実証している。
abstractHere we present rosettR , a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile 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 · publicThe rosettR R-package along with example data is available from our github repository http://github.com/hredestig/rosettROpen asset ↗hredestig/rosettRlines:130-159Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動計測を行う半自動パイプラインを開発し、ソルガムの草型形質を取得する方法が研究の中心である。
abstractA semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe authors explicitly deposit their image acquisition/processing and QTL mapping code (C++, Bash, Python, R) plus genotype/phenotype data on GitHub, and per-plant depth images, RGB images, and segmented meshes on Dryad. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for QTL mapping, the genotype and phenotype data, and the full multiple- QTL models for each phenotype-by-time point combination can be found on GitHub at https://github.com/MulletLab/SorghumReconstructionAndPhenotyping .Open asset ↗MulletLab/SorghumReconstructionAndPhenotypinglines:240-292Dataset · publicFor each imaged plant, its depth images, a single RGB image, and the segmented mesh can be found at the Dryad Digital Repository ( http://dx.doi.org/10.5061/dryad.9vs26 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.9vs26lines:240-292Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and non-destructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of Sorghum bicolor, an important grain, forage, and bioenergy crop, at multiple developmental timepoints from a greenhouse-grown recombinant inbred line population. A semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci (QTL) for standard measures of shoot architecture such as shoot height, leaf angle and leaf length, and for novel composite traits such as shoot compactness. The phenotypic variability associated with some of the QTL displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動抽出を行う半自動パイプラインを開発し、ソルガムのシュート構造形質を取得・評価しており、表現型取得法が研究の中心です。
abstractA semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe paper explicitly deposits its authors' image acquisition/processing and QTL mapping code on GitHub, and its per-plant depth images, RGB images, and segmented meshes on the Dryad repository. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for
QTL mapping, the genotype and phenotype data, and the full multiple-QTL models for each phenotype
by timepoint combination can be found on GitHub at
https://github.com/MulletLab/SorghumReconstructionAndPhenotyping.Open asset ↗MulletLab/SorghumReconstructionAndPhenotypingpdf-page:8 lines:1-43Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
CoffeeGrapevineLeafMorphology / geometry measurementPhysiological trait estimationLeaf traitsWater status / transpiration
Fresh water is a key natural resource for food production, sanitation and industrial uses and has a high environmental value. The largest water use worldwide (~70%) corresponds to irrigation in agriculture, where use of water is becoming essential to maintain productivity. Efficient irrigation control largely depends on having access to reliable information about the actual plant water needs. Therefore, fast, portable and non-invasive sensing techniques able to measure water requirements directly on the plant are essential to face the huge challenge posed by the extensive water use in agriculture, the increasing water shortage and the impact of climate change. Non-contact resonant ultrasonic spectroscopy (NC-RUS) in the frequency range 0.1-1.2 MHz has revealed as an efficient and powerful non-destructive, non-invasive and in vivo sensing technique for leaves of different plant species. In particular, NC-RUS allows determining surface mass, thickness and elastic modulus of the leaves. Hence, valuable information can be obtained about water content and turgor pressure. This work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure. A sensing prototype is proposed, described and, as application example, used to study two different species: Vitis vinifera and Coffea arabica, whose leaves present thickness resonances in two different frequency bands (400-900 kHz and 200-400 kHz, respectively), These species are representative of two different climates and are related to two high-added value agricultural products where efficient irrigation management can be critical. Moreover, the technique can also be applied to other species and similar results can be obtained.
Why it matches plant phenotyping methods植物葉の水分量・膨圧を非接触超音波で測定するセンサー方式の要件分析、試作、応用を中心に扱っており、植物表現型取得法が明確に中心的である。
abstractThis work analyzes and reviews the main requirements for sensors, electronics, signal processing and data analysis in order to develop a fast, portable, robust and non-invasive NC-RUS system to monitor variations in leaves water content or turgor pressure.
Reproduction assets foundThe paper's inverse-problem analysis code for extracting leaf parameters (thickness, density, ultrasound velocity, attenuation) from measured resonance spectra is explicitly stated to be publicly available via the authors' GitHub repository and the US-BIOMAT resource page. No phenotype dataset deposit is mentioned.Code · publicUS-BIOMAT
Available online: https://us-biomat.com/resources/code-2/ or https://github.com/usbiomat/ultrasonic-thickness-resonance (accessed on 12 July 2016)Open asset ↗usbiomat/ultrasonic-thickness-resonancelines:327-413Code / dataset availability confirmedOpenAlex · Europe PMC · checked 11 Sept 2026
BACKGROUND: In this study we carried out a genome-wide association analysis for plant and grain morphology and root architecture in a unique panel of temperate rice accessions adapted to European pedo-climatic conditions. This is the first study to assess the association of selected phenotypic traits to specific genomic regions in the narrow genetic pool of temperate japonica. A set of 391 rice accessions were GBS-genotyped yielding-after data editing-57000 polymorphic and informative SNPS, among which 54% were in genic regions. RESULTS: In total, 42 significant genotype-phenotype associations were detected: 21 for plant morphology traits, 11 for grain quality traits, 10 for root architecture traits. The FDR of detected associations ranged from 3 · 10-7 to 0.92 (median: 0.25). In most cases, the significant detected associations co-localised with QTLs and candidate genes controlling the phenotypic variation of single or multiple traits. The most significant associations were those for flag leaf width on chromosome 4 (FDR = 3 · 10-7) and for plant height on chromosome 6 (FDR = 0.011). CONCLUSIONS: We demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice. We identified strong associations that may be used for selection in temperate irrigated rice breeding: e.g. associations for flag leaf width, plant height, root volume and length, grain length, grain width and their ratio. Our findings pave the way to successfully exploit the narrow genetic pool of European temperate rice and to pinpoint the most relevant genetic components contributing to the adaptability and high yield of this germplasm. The generated data could be of direct use in genomic-assisted breeding strategies.
Why it matches plant phenotyping methods高スループット表現型解析プラットフォームの開発・適用が明示され、植物形態・根系・穀粒形質の測定とGWASを結び付けているため、表現型取得基盤が研究の主要部分と判断する。
abstractWe demonstrate the effectiveness and resolution of the developed platform for high-throughput phenotyping, genotyping and GWAS in detecting major QTLs for relevant traits in rice.
Reproduction assets foundThe authors state that all relevant data (phenotypic and genotypic data underlying the GWAS) are publicly available in a Zenodo repository, which qualifies as a paper-specific public data asset.Dataset · publicData Availability All relevant data are publicly available in a Zenodo repository at the following URL: https://zenodo.org/record/50803#.VytVnrp97CI .Open asset ↗Zenodo · record/50803lines:48-55