Growth chamberMultimodalStereoRootStem / branchTrackingGrowth / development / phenology
Understanding plant behaviour requires the integration of multiple phenotypic and physiological signals measured over time under controlled conditions. However, different plant signals are typically studied using separate experimental setups, limiting temporal alignment and integrative analyses.We present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging. Its modular architecture is designed to accommodate additional acquisition modules, such as electrophysiological signalling, as future extensions. The platform integrates a controlled growth environment with stereovision imaging, high-resolution time-of-flight mass spectrometry and custom rhizocameras. These components are connected through a unified network infrastructure that ensures synchronized acquisition and centralized data handling.We validate the performance of each acquisition module through multi-week recordings, demonstrating high-temporal stability, reliable stereovision synchronization, effective isolation of VOCs signals and robust operation of below-ground imaging. We further illustrate the analytical potential of the platform using a one-day continuous multimodal acquisition combining shoot kinematics, above-ground VOC emissions, rhizocameras observations and environmental data.Mind(the)Plant provides a novel methodological framework for studying plant behaviour, signalling and phenotypic plasticity in ecological and evolutionary research. By enabling coordinated measurements of multiple plant response modalities, the platform supports investigations of dynamic plant-environment and plant-plant interactions from a behavioural perspective.
Why it matches plant phenotyping methods植物の複数の表現型・生理シグナルを同期取得する施設を開発し、各取得モジュールの性能を検証しているため、表現型計測プラットフォームが研究の中心です。
abstractWe present Mind(the)Plant, a modular experimental facility designed for the synchronized, long-term acquisition of multimodal plant data, including three-dimensional shoot kinematics, above- and below-ground volatile organic compounds (VOCs) and root imaging.
Reproduction assets foundThe paper's data availability statement explicitly deposits data, code and processing pipelines (supporting the multimodal plant phenotyping measurements and analysis) in a public Zenodo archive with an authors' URL matching an allowed URL.Code · publicf Interest Statement
The authors have no conflicts of interest to declare.
Peer Review
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/2041-210x.70411 .
Data availability Statement
Data, code and processing pipelines supporting this study are available at https://doi.org/10.5281/zenodo.22095454 ( Simonetti & Castiello, 2026 ).
References
Avesani S, Bonato B, Simonetti V, Guerra S, Ravazzolo L, Gjinaj G, Dadda M, Castiello U. Comparing proton transfer reaction (PTR) and adduct ionization mechanism (AIM) for the study of volatile organic compounds. Molecules. 2026;31(3):402. doi: 10.3390/molecules31030402.
Baluška F, LeOpen asset ↗zenodo · 10.5281/zenodo.22095454lines:482-508Code / dataset availability confirmedbioRxiv · OpenAlex · Europe PMC · checked 15 Sept 2026
Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.
Why it matches plant phenotyping methods植物画像から形態形質を自動抽出するツールの開発と、手動測定との性能検証が中心であるため。
abstractHere, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images.
Reproduction assets foundThe paper's own phenotyping tool BioIMA (source code, documentation, example datasets, and user manual) is publicly available on the authors' GitHub repository, directly supporting the paper's image-based trait extraction and validation analyses.Code · publicis powered by embedded models
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currently including SAM (Kirillov et al., 2023) and mobile SAM (Zhang et al., 2023),
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which are executed locally through ONNX Runtime for efficient inference without
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internet connectivity. Source code, documentation, example datasets, and a user manual
100
are publicly available on GitHub (https://github.com/jingwanglab/BioIMA).101
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
;
https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Why it matches plant phenotyping methodsスパーステスト設計とゲノム予測を用いて、複数環境での植物形質予測と表現型測定コスト削減を評価しており、表現型取得・予測手法が研究の中心である。
abstractSparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at −log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10–30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, “Rice_Stem_Pre_V1.1.exe,” for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.
Why it matches plant phenotyping methodsイネ茎維管束の画像から複数の表現型形質を自動抽出する深層学習モデル、データセット、検証、ソフトウェアを中心的に開発しており、明確な植物フェノタイピング手法研究である。
abstractwe present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated.
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 6
Description of annotated and predicted stem internal structural traits.Open asset ↗lines:510-582Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Accurate lesion segmentation is essential for automated plant disease analysis in precision agriculture. Although the Segment Anything Model (SAM) exhibits strong generalization ability, its direct application to plant disease images in natural field environments remains challenging due to cluttered backgrounds, dense leaf veins, uneven illumination, and frequent occlusions. In particular, SAM mainly relies on global structural cues and is often insufficiently sensitive to subtle lesion textures and weak local details, which can result in missed small or early-stage lesions and inaccurate boundary delineation. To address these limitations, we enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation. A ResNet-50based branch is employed to extract fine-grained local texture features that are difficult for SAM to capture. These fine-grained features are fused with SAM encoder representations and then injected into the SAM decoder, enabling more accurate lesion prediction while preserving SAM’s strong global modeling capability. More importantly, we propose a reliability-guided variational fusion framework to further improve the interaction between heterogeneous features. Specifically, instead of conventional similarity or addition-based fusion, we introduce an uncertainty-aware variational fusion strategy that explicitly quantifies the confidence of each feature stream. An uncertainty encoder models feature distributions probabilistically, and a variational fusion module dynamically assigns higher weights to more reliable features while suppressing uncertain or interfering responses. In addition, Kullback-Leibler divergence regularization is introduced to stabilize cross-feature alignment and improve fusion robustness. Extensive experiments on PlantSeg, PlantDoc-Seg, and ATLDSD demonstrate that the proposed method outperforms state-of-theart approaches, achieving DSC scores of 81.05%, 91.12%, and 88.27%, respectively. The proposed method addresses SAM’s weakness in fine-grained disease feature extraction, accurately identifies early and small lesions, and delivers reliable segmentation for field plant disease automatic diagnosis.
Why it matches plant phenotyping methods植物病斑を対象とする画像セグメンテーション手法を開発し、複数データセットで性能検証しているため、病害状態のフェノタイピング手法が中心である。
abstractwe enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation.
Reproduction assets foundThe paper evaluates ReLeaf-SAM on three public plant disease segmentation datasets. One of them, PlantDoc-Seg, is explicitly a community-provided Kaggle dataset with a verbatim URL matching an allowed URL; it is a public plant image/mask dataset directly used for this paper's segmentation measurements. PlantSeg and ATLDataset · publicTherefore, we used a community-provided segmentation subset from Kaggle 1 , which we refer to as PlantDoc-Seg in this study. This subset is derived from PlantDoc and contains 588 diseased leaf images with corresponding binary masks, enabling supervised leaf disease segmentation.Open asset ↗Kagglelines:48-115Code / dataset availability confirmedarXiv · OpenAlex · checked 11 Sept 2026
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Three dimensional (3D) instance segmentation is essential for precision characterization of tree architecture at the branch level, which supports both tree fruit crop breeding and the development of robotic systems for orchard management. Existing methods usually use sparse convolution-based operation, which requires a coordinate quantization preprocess to generate sparse tensors, risking the loss of geometric details for fine-grained downstream phenotyping tasks. To overcome this challenge, we developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees. Our hybrid DSPVFormer architecture maximizes the use of raw point features by dynamically mapping and aggregating the raw point features into the sparse voxel embeddings, capturing strong geometric features that may be discarded during quantization. Evaluations demonstrate that DSPVFormer achieved statistically significant improvements over baseline models on most instance segmentation metrics, which are further translated into more accurate phenotyping evaluation including branch counting and pruning map generation. These advances directly benefit downstream applications in plant phenotyping and robotic pruning for tree crops such as apples. Meanwhile, experimental results on phenotyping tasks suggested that phenotyping-specific evaluation metrics should be prioritized over upstream computer vision performance metrics to realize the full potential of high-throughput phenotyping for real-world applications.
Why it matches plant phenotyping methodsリンゴ樹の3D点群から枝レベル形質を抽出するセグメンテーション手法を開発・評価し、枝数や剪定マップへの応用まで検証しており、植物フェノタイピング手法が中心である。
abstractwe developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees.
Reproduction assets foundThe paper states that its data and code (including the DSPVFormer analysis pipeline built on Plant Segmentation Studio) are publicly available at the authors' PSS GitHub repository. The COS dataset of 98 dormant apple tree point clouds is also referenced as accessible via this repository/statement. Other URLs (spconv,mCode · publicThe data and code are available at the PSS GitHub repository: https://github.com/perrydoremi/PlantSegStudio .Open asset ↗https://github.com/perrydoremi/PlantSegStudiolines:383-408Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
LiDAR / point cloudFlowerLeafStem / branchSegmentationGrowth / development / phenology
The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentation is more challenging due to the interwoven distribution of various parts such as stems, leaves, and flowers. In this paper, we propose a universal point cloud segmentation network PlantEFRSegnet that can be used for multi-species of plants. The proposed PlantEFRSegnet utilizes a newly designed edge point preservation downsampling module to identify and preserve the points at the edges of plant organs during the downsampling process, in order to assist the segmentation network in learning the contours of various plant organs. PlantEFRSegnet performs supervised feature repair on the point cloud features obtained through downsampling to mitigate the impact of feature loss on segmentation performance during feature embedding. The encoder of the segmentation network is composed of four local feature extraction modules. These four modules can not only extract features but also enhance the features corresponding to points with high contributions in local regions based on point attention mechanism. We evaluated the proposed PlantEFRSegnet on a laser-scanned plant point cloud dataset. Compared with the state-of-the-art approaches, the proposed PlantEFRSegnet achieved better segmentation results.
Why it matches plant phenotyping methods植物器官の3D点群を対象に、器官分割と植物成長フェノタイプ解析を行う新規ネットワークを開発・評価しており、フェノタイプ取得の計算手法が中心である。
abstractThe segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype 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。Dataset · publicThe experimental dataset used in this paper can be obtained through the following link: https://github.com/dllab23/PlantPointCloud (accessed on 11 May 2026).Open asset ↗dllab23/PlantPointCloudhtml-lines:785-806Code / 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 confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.
Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.
Reproduction assets foundThe paper's MRI phenotyping data (~160 GB raw, 1.4 TB processed) is only available upon request, but the authors' processing pipeline (scripts and parameters) is publicly deposited on Zenodo with an explicit URL.Code · publicThe processing pipeline (including scripts and parameters required to reproduce the processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369 .Open asset ↗Zenodo · 10.5281/zenodo.17944369lines:370-484Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Code / 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 · bioRxiv · Crossref · checked 15 Sept 2026
Quantitative studies of plant growth and environmental responses increasingly rely on time-series imaging, yet automated segmentation remains challenging due to continuous growth, large non-rigid morphological change, and frequent self-occlusion. Traditional image-processing pipelines and task-specific deep learning models often require extensive annotated datasets and retraining, limiting portability across species, developmental stages, and imaging conditions. Here we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery. SAP integrates interactive prompting, automated temporal mask propagation, and centerline extraction within a web-based interface, allowing users to move from raw images to quantitative descriptors of organ shape and dynamics without programming expertise. Across multiple systems, including Arabidopsis thaliana rosette development, root growth, sunflower gravitropism, and confocal root microscopy, SAP achieves high segmentation accuracy (mean IoU 0.89–0.93) and sub-pixel centerline precision from single-frame prompting. By reducing the need for task-specific retraining, SAP provides a transferable framework for reproducible time-series phenotyping across diverse experimental contexts.
Why it matches plant phenotyping methods植物の時系列画像から器官形状・動態を抽出するセグメンテーション手法とWeb基盤を開発し、複数系で精度検証しているため、植物フェノタイピング手法が中心である。
abstractHere we present SAP (Segment Any Plant), a plant-focused framework that leverages the pretrained Segment Anything Model 2 (SAM2) to enable few-shot, training-free segmentation of plant time-series imagery.
Reproduction assets foundThe paper's authors publicly release both the SAP analysis code (GitHub repository) and the datasets generated/analyzed in the study (Zenodo), including raw images, ground-truth and SAP-generated segmentation masks, centerline validation data, and supplementary videos. Both are paper-specific, public, and directly cit.Dataset · publicCode Availability. The code is available at
https://github.com/merozlab/plant-segmentation-app.Data Availability. The datasets generated and an-
alyzed during this study are available on Zenodo at
https://doi.org/10.5281/zenodo.18732705. This includes
raw images and segmentation masks for the sunflower
gravitropism and Arabidopsis root growth experiments,
SAP-generated masks for the Lee et al. (9) and Strauss
et al. (13) datasets, centerline validation data, and supple-
mentary videos.
Funding. Y.M. acknowledges support from the Israel Sci-
ence Foundation ResOpen asset ↗zenodo · 10.5281/zenodo.18732705pdf-raw-page:9 lines:1-74Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
3D crop phenotyping technology provides critical support for screening morphology-related plant genes and identification of germplasm resource. Organ segmentation or recognition is the first key step in 3D crop phenotyping, where inductive deep learning currently dominates as the mainstream methodology. However, the high requirement for data annotation in inductive learning paradigm has transformed the manual data labeling into a labor-intensive task, thereby in turn restricting the progress of inductive learning. This problem has inspired us to leverage Graph Neural Networks (GNNs) as the transductive learning tool to directly segment organs on sparsely annotated crop point clouds. We propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds that have featureless point features. Different from existing graph-based networks, DBGCN not only carries out the static-feature-space graph convolutions that are good at mining and aggregating on local information on the point cloud, but also incorporates dynamic graph convolutions that captures the potential changes of the graph manifold in deep feature space. Extensive experiments prove that the fusion of two types of graph feature convolutions brings a high node (point) classification accuracy, outperforming mainstream GNNs and even several popular inductive deep architectures. On the PlantNet sub-dataset, DBGCN achieves an mAcc (mean accuracy of node classification) of 93.00% under 1.95% manual annotation ratio. On the Soybean-MVS sub-dataset, DBGCN achieves an mAcc of 91.05% under 4.88% manual annotation ratio. Furthermore, our DBGCN not only works well on crop 3D data but can also serve other applications such as the segmentation of point cloud data for large-scale street view. Our dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.
Why it matches plant phenotyping methods3D植物点群から器官を分割・推論する深層学習手法を開発し、植物フェノタイピングデータ上で精度検証しているため、表現型取得・抽出法が中心である。
abstractWe propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds
Reproduction assets foundThe authors explicitly state that their dataset (plant point clouds) and DBGCN code are publicly available on GitHub.Code · publicOur data and code are available at: https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:414-455Dataset · publicOur dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:88-94Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Abstract Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To address these limitations, TomatoMAP is introduced as a comprehensive dataset for Solanum lycopersicum . The dataset contains 68,080 RGB images: 3,616 high-resolution macrophotographs (3648 × 5472) with semantic annotations, and 64,464 moderate-resolution images (1080 × 1440) captured from 12 plant poses at four camera elevations. Each image is accompanied by manually annotated bounding boxes for seven regions of interest (leaves, panicle, flower clusters, fruit clusters, axillary shoot, shoot, and whole-plant area) and by labels spanning 50 BBCH classes representing phenologically growth stages. A general cascading structure is proposed. For real-time applicability, models emphasizing the accuracy-efficiency trade-off (MobileNetv3, YOLOv11, and Mask R-CNN) are prioritized and benchmarked against multiple state-of-the-art models. Performance is assessed using accuracy, mAP, inference FPS, and normalized confusion matrices. In a study involving five domain experts, AI models trained on TomatoMAP achieves comparable accuracy levels. Reliability of automated fine-grained phenotyping is supported by Cohen’s Kappa statistics and inter-rater agreement heatmaps.
Why it matches plant phenotyping methodsトマトの多視点画像、器官領域・生育ステージ注釈を備えたデータセットを構築し、画像モデルの精度・効率・専門家一致度をベンチマークしており、植物フェノタイピング手法が中心である。
titleTomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping.
Reproduction assets foundThe paper's authors publicly release their analysis code (dataset construction scripts for TomatoMAP-Cls/Det and model training/evaluation code) on GitHub. The TomatoMAP phenotype image dataset itself is deposited at e!DAL (10.5447/ipk/2025/14), but no matching URL is present in the allowed list, so only the code assetCode · publicThe scripts for constructing TomatoMAP-Cls and TomatoMAP-Det, as well as the code used for model evaluation, are available at: https://github.com/0YJ/TomatoMAP.Open asset ↗https://github.com/0YJ/TomatoMAPhtml-lines:423-479Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface ('TillerCounter' program) allows adjusting the automatic detections interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting. The key contributions of our work include:1.An efficient method for imaging straw tillers based on bundle assembly.2.An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.3.Demonstration of the approach's high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).
Why it matches plant phenotyping methods小麦分げつ数という植物形態形質を、画像取得・古典的画像解析・専用ソフトウェアで自動推定し、手動計数を基準に精度検証しているため、フェノタイピング手法が中心です。
abstractWe present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples.
Reproduction assets foundThe paper's authors publicly released the TillerCounter GUI source code on GitHub, which implements the Hough-transform-based tiller counting analysis used in this study. The paper also cites original image/count data at Zenodo (10.5281/zenodo.14446564), but no Zenodo URL is present in the allowed URL list, so only theCode · publicThe source code of the TillerCounter GUI is given at https://github.com/agroscope-ch/TillerCounterGui. Original data is given at Zenodo repository: 10.5281/zenodo.14446564Open asset ↗agroscope-ch/TillerCounterGuihtml-lines:163-195Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleLeafStem / branchWhole plant / canopy / plot / fieldClassificationSegmentation
Abstract Annotated datasets are essential for training and evaluating machine learning models in forest ecology. This dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species. Data were acquired using Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS), include field-measured attributes such as species identity and Diameter at Breast Height (DBH), and terrestrial and aerial RGB imagery. TLS point clouds were georeferenced and co-registered with centimetre-level accuracy, enabling precise integration with ALS data. The dataset includes segmented individual trees and wood–leaf classifications, suitable for applications such as tree morphology analysis, biomass estimation, and species classification. To support benchmarking, outputs from established classification algorithms (LeWoS, TLSeparation, CANUPO, and Random Forest) are included. As one of the first open-access LiDAR datasets from Indian tropical forests, it provides critical reference data for developing and validating forest structure models. It can also aid biomass mapping efforts in support of large-scale missions such as NASA-ISRO’s NISAR and ESA’s BIOMASS.
Why it matches plant phenotyping methods個体樹木のLiDAR点群・RGB画像と樹木セグメンテーションを含む公開データセットで、樹形解析や森林構造モデルの開発・検証、分類アルゴリズムのベンチマークを目的としており、植物形質取得が中心です。
abstractThis dataset provides high-resolution, annotated LiDAR point clouds of 674 individual trees from 12 forest plots in the Shivalik Range of northern Haryana, India, representing 24 species.
Reproduction assets foundThe paper's authors explicitly state that all code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset, which is an allowed URL. The paper's core LiDAR dataset is deposited on Zenodo (10.5281/zenodo.153Code · publicAll code used for data processing, wood-leaf classification, feature extraction, and tree volume estimation is openly available on GitHub at https://github.com/moonis-ali/Dataset .Open asset ↗https://github.com/moonis-ali/Datasetlines:479-553Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Poplars are essential to China's forestry, contributing to timber production, ecological restoration, and shelterbelt construction. Branch architecture critically influences tree growth, demanding scalable solutions beyond manual methods to assess phenotypic variation in large-scale poplar breeding programs. Unmanned aerial vehicle light detection and ranging (UAV LiDAR) provides an efficient alternative; however, existing methods focus on conifers, leaving a gap in approaches for the more complex morphology of poplar branches. This study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data. First, a voxel-based near-centroid method is used to extract skeleton points from tree point clouds. Next, a material transport flux model identifies individual branches, and geometric features of transport paths, including path length and curvature, are used to reconstruct each branch. Finally, branch parameters are estimated based on reconstructed branches. Data from a 5-ha plot were collected using the DJI Zenmuse L1 UAV LiDAR at the Shishou National Poplar Breeding Station, Hubei Province, China. Results demonstrate the proposed algorithm achieves high accuracy in first-order branch identification (F1-score = 1), with second-order branches having an average F1-score of 0.69. Branch length estimation demonstrates an RMSE of 0.47 m, while branch angles show an RMSE of 7.06°. The study also reveals structural variability in branch traits, with the highest variability observed in the second-order branch length (coefficient of variation = 29.68%), and a moderate positive correlation between first- and second-order branch lengths (correlation coefficient = 0.34), providing insights into tree growth patterns. This approach offers a framework for high-throughput phenotyping, which provides an efficient solution towrads advanced tree breeding using UAV LiDAR.
Why it matches plant phenotyping methodsUAV LiDARによるポプラの枝構造再構成と枝長・枝角度などの形質推定アルゴリズムを開発し、精度検証まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data.
Reproduction assets foundThe paper's Data availability statement provides a public URL to the supporting UAV LiDAR point cloud data (the paper-specific phenotyping measurements) hosted on forestdata.cn, with a DOI. No author analysis code or trained models are mentioned.Dataset · publicThe data that support this study are available from https://www.forestdata.cn/dataDetail.html?id=6f6934f4-680e-4e18-b4e3-1e7a60b85b55 . The DOI is 10.12459.14.0320260116001.0000.V1.Open asset ↗forestdata.cn · 10.12459.14.0320260116001.0000.V1lines:192-218Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Common beanStem / branchObject detectionPhysiological trait estimationGrowth / development / phenology
Growing plants are remarkable at negotiating obstacles in their unstructured and changing environments. Measuring the mechanical interactions of growing plants with surrounding objects is a critical step towards deciphering thigmotropic responses underpinning complex growth strategies. Yet, available force measurement systems have limited capacity to capture weak forces in freely moving plant organs-such as the forces applied by a growing shoot pushing at an obstacle. We developed a measurement system based on the deflection of a pendulum by a freely moving shoot. Unlike many force measurement systems, the organ is not tethered to the device. Moreover, force is measured along two axes, as opposed to one axis in commonly used methods. Orthogonal cameras track the 3D position of the rod and shoot, yielding the rod deflection angle and, using a mechanical torque equilibrium equation, allowing extraction of the force applied by the plant over time. This system is relevant for measuring weak forces in macro-sized systems (e.g. growth or turgor pressures), and the force detection range can be tuned by altering rod mass and length. We demonstrate the system with Phaseolus vulgaris shoots, measuring the forces they apply on a candidate support during inherent circumnutation movements, prior to twining. Such measurements lay the foundations for deciphering how climbing plants assess whether to twine or not- an open question since Darwin's first observations.
Why it matches plant phenotyping methods自由に動く植物器官が発生する微弱な力を、カメラ追跡と力抽出により定量する測定システムを開発・実証しており、植物表現型の取得方法が研究の中心である。
abstractWe developed a measurement system based on the deflection of a pendulum by a freely moving shoot.
Reproduction assets foundThe authors deposited the full analysis workflow (data and code) for five example force-measurement trajectories on Zenodo, publicly accessible via DOI 10.5281/zenodo.15545548. This directly reproduces the paper's camera-based plant force phenotyping measurements and computational analysis. Other experimental data are仅Dataset · publicof interest
None declared.
Funding
YM acknowledges support from the Israel Science Foundation Research
Grant (ISF) no. 2307/22, and ERC grant GROWsmart 101165101. AO
acknowledges support from the Colton Foundation scholarship.
Data availability
We have put the full workflow for five example trajectories on a Zenodo
repository (https://doi.org/10.5281/zenodo.15545548; Ohad and
Meroz, 2025). Other experimental data are available upon request.
References
Autumn K, Liang YA, Tonia Hsieh S, Zesch W, Chan WP, Kenny TW,
Fearing R, Full RJ. 2000. Adhesive force of a single gecko foot-hair.
Nature 405, 681–685.
Backholm M, Bäumchen O. 2019. Micropipette force sensors for in vivo
force measurementsOpen asset ↗Zenodo · 10.5281/zenodo.15545548pdf-raw-page:9 lines:1-95Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping.
Why it matches plant phenotyping methodsゴム樹の表現型取得を目的に、UAVおよびハンドヘルドLiDARのデータ融合・位置合わせ手法を開発し、複数圃場で精度と季節変動下の頑健性を評価しているため、方法が中心的である。
abstractthis paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems.
Reproduction assets foundThe authors publicly released the ULS-HLS rubber plantation point cloud dataset (25 plots, 5 sites, leaf-on/leaf-off, >400M points) used for this paper's phenotyping/registration analysis, via an explicit Data Availability Statement with a Hugging Face URL matching an allowed URL.Dataset · publicThe dataset is available at https://huggingface.co/datasets/TanJunxiang/ULS-HLS-Rubber/tree/main (accessed on 20 January 2026).Open asset ↗TanJunxiang/ULS-HLS-Rubberlines:670-670Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Accurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding. However, cotton seedling segmentation remains challenging due to fine-scale and complex organ morphology, uneven point density with noise, and the lack of high-quality annotated datasets. Methods: To address these issues, we propose DCSFormer, a tailored extension of Point Transformer V3 designed for cotton seedling point cloud segmentation. The model introduces the DCS Block, which leverages dynamic sparse expert routing and dual-channel attention to adaptively capture global semantic dependencies and subtle local geometric variations, thereby improving stem-leaf boundary discrimination. In addition, the proposed CLFSkip replaces traditional skip connections with a cross-layer fusion strategy, effectively integrating multi-scale features while preserving organ-level details. We also constructed an annotated cotton seedling dataset to support training and evaluation. Results and Discussion: Experimental results show that DCSFormer achieves 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1, outperforming multiple comparison models. Furthermore, when evaluated against baseline models on two public datasets, Crops3D and Pheno4D, DCSFormer exceeds the baseline across all four metrics, further validating its effectiveness and generalizability. This work provides an effective solution for precise cotton seedling organ segmentation.
Why it matches plant phenotyping methods綿花幼苗の3D点群から器官を抽出する手法を開発し、アノテーション済みデータセットの構築と複数データセットでの性能検証を行っており、植物表現型取得が中心である。
abstractAccurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding.
Reproduction assets foundThe authors constructed an annotated cotton seedling point cloud dataset (100 samples with semantic/instance organ labels and ground-truth traits) used for training and evaluating DCSFormer, and the data availability statement points to a public Kaggle repository containing it. No author analysis code or trained model/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://www.kaggle.com/datasets/tengfeiliu333/dcsformer-cotton/croissant/download .Open asset ↗Kaggle · tengfeiliu333/dcsformer-cottonlines:957-1015Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
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 · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation.
Why it matches plant phenotyping methods自律ロボット、3D点群、深層学習を用いて樹木の直径・高さを推定する手法を開発し、点群密度による推定精度も評価しており、植物形質取得が中心である。
abstractwe develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation.
Reproduction assets foundThe paper's point-cloud-density/tree-parameter analysis (Section 2.3) is based on two open-source MLS forest point cloud datasets (Forest 1 from southern Finland, and Forest 2 openly accessible via the 3DFin platform), which are public, paper-specific phenotype/trait data assets. However, the supplied blocks do not包含 aDataset · publicTwo different open-source datasets acquired using a Mobile Laser Scanning (MLS)
system (i.e., GeoSLAM Zeb-Horizon) and available online were considered in this study.Open asset ↗pdf-page:12 lines:1-60Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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Foundation.
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Data Availability
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Code and data associated with this manuscript are available on GitHub
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(https://github.com/danforthcenter/teff-manuscript).477
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 7, 2026.
;
https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
3
Plant Phenomics Page 26 of 29Open asset ↗zenodo · 10.5281/zenodo.17944369pdf-layout-page:26 lines:1-14Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Plant developmental plasticity, particularly in root system architecture, is fundamental to understanding adaptability and agricultural sustainability. Existing automated phenotyping solutions face limitations, including binary segmentation approaches, restricted structural analysis capabilities, and text-based interfaces that limit accessibility, with most focusing solely on root structures while overlooking valuable information from simultaneous analysis of multiple plant organs. FINDINGS: ChronoRoot 2.0 builds upon established low-cost hardware while significantly enhancing software capabilities and usability. The system employs nnUNet architecture for multi-class segmentation, demonstrating significant accuracy improvements while simultaneously tracking 6 distinct plant structures encompassing root, shoot, and seed components: main root, lateral roots, seed, hypocotyl, leaves, and petiole. This architecture enables easy retraining and incorporation of additional training data without requiring machine learning expertise. The platform introduces dual specialized graphical interfaces: a Standard Interface for detailed architectural analysis with novel gravitropic response parameters and a Screening Interface enabling high-throughput analysis of multiple plants through automated tracking. Functional principal component analysis integration enables discovery of novel phenotypic parameters through temporal pattern comparison. We demonstrate multi-species analysis, with Arabidopsis thaliana and Solanum lycopersicum, both morphologically distinct plant species. Three use cases in Arabidopsis thaliana and validation with tomato seedlings demonstrate enhanced capabilities: circadian growth pattern characterization, gravitropic response analysis in transgenic plants, and high-throughput etiolation screening across multiple genotypes. CONCLUSIONS: ChronoRoot 2.0 maintains the low-cost, modular hardware advantages of its predecessor while dramatically improving accessibility through intuitive graphical interfaces and expanded analytical capabilities. The open-source platform makes sophisticated temporal plant phenotyping more accessible to researchers without computational expertise. SOFTWARE AVAILABILITY: https://chronoroot.github.io.
Why it matches plant phenotyping methods根・シュート・種子を時系列追跡し、植物形態・成長・重力応答などの表現型を抽出するオープンプラットフォームの開発と検証が中心である。
titleChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Reproduction assets foundThe paper publicly releases its authors' analysis code (GitHub), the annotated plant image dataset used for segmentation training/validation (HuggingFace), a pre-configured Docker image, and a project home page, all with explicit availability statements and URLs matching allowed entries.Code · publicapproach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional principal component analysis (FPCA) for readers without a quantitative backgrOpen asset ↗https://github.com/ChronoRoot/ChronoRoot2lines:439-479Dataset · publicgulates LAZY genes. Plant J. 2025;121:e70016. 10.1111/tpj.70016.
19.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Main Source Code Repository. 2026. https://github.com/ChronoRoot/ChronoRoot2 . Accessed 25 February 2026.
20.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Annotated Image Dataset. 2026. https://huggingface.co/datasets/ngaggion/ChronoRoot2 . Accessed 25 February 2026.
21.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Docker Image. 2026. https://hub.docker.com/r/ngaggion/chronoroot . Accessed 25 February 2026.
22.
Gaggion N, Boccardo N A, Bonazzola R, et al. ChronoRoot 2.0 Project Home Page. 2026. https://chronoroot.github.io . Accessed 2Open asset ↗https://huggingface.co/datasets/ngaggion/ChronoRoot2lines:568-618Code · publicical modules, and experimental protocols. We hope that this approach to open science will not only ensure transparency and reproducibility but also allow the system to evolve alongside the changing needs of the plant biology community.
Availability of source code and requirements
Project name: ChronoRoot 2.0.
Project home page: https://chronoroot.github.io .
Main Source Code repository: https://github.com/ChronoRoot/ChronoRoot2 .
Operating system(s): Platform independent.
Programming language: Python.
Other requirements: Conda, Apptainer, or Docker.
License: GNU GPL 3.0.
Additional files
Supplementary Text S1 : Functional PCA. Provides an intuitive explanation of functional princOpen asset ↗lines:439-479Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Nature-based climate solutions, such as agroforestry, offer potential for carbon sequestration while providing co-benefits. However, the lack of scalable and low-cost measurement, reporting, and verification (MRV) systems limits smallholder participation in carbon markets. This study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry. The fine-tuned model achieved a mean intersection over union (mIoU) of 0.937. The algorithm was tested on image datasets from managed trees settings in Kenya (n = 142) and Pennsylvania, USA (n = 40), with regression analysis showing high accuracy (R² = 0.97, RMSE = 2.20–2.23 cm). Bias analysis showed slight overestimation for small to medium trees (5–35 cm DBH) and underestimation for larger trees (>36 cm DBH), with an overall mean bias of +0.68 cm. Coupled with allometric equations, the DiameterAlgorithm enables scalable, site-level biomass estimation for carbon markets.
Why it matches plant phenotyping methods樹木直径という植物形態形質を画像から推定する手法を開発し、複数地域のデータで精度・バイアスを検証しており、フェノタイピング手法が研究の中心である。
abstractThis study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry.
Reproduction assets foundThe paper publicly releases its tree image dataset (calibration/evaluation images from Kenya and Pennsylvania) on ScholarSphere and the containerized diameter estimation tool on Docker Hub, both explicitly stated in the data availability statement.Dataset · publicThe image dataset that was used to calibrate and evaluate the algorithm can be found on the ScholarSphere repository
of the Pennsylvania State University (https://scholarsphere.psu.edu/resources/08a985a4-d878-4fa9-b2f2-60601005Open asset ↗ScholarSphere · 08a985a4-d878-4fa9-b2f2-60601005pdf-page:13 lines:1-61Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Volume electron microscopy (vEM) provides nanometer-scale, three-dimensional imaging of cells, but applying it to plant systems remains challenging. Cell walls, large vacuoles, and tissue thickness complicate sample preparation and cryogenic imaging. Here we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts without chemical fixation, dehydration, resin embedding, or heavy-metal staining. The method integrates optimized protoplast isolation, plunge-freezing vitrification for native-state preservation, automated cryogenic focus ion beam scanning electron microscopy (cryo-FIB-SEM) slice-and-view acquisition, contrast enhancement and stack alignment, and AI-assisted human-in-the-loop 3D segmentation. Using sorghum stem protoplasts as a demonstration, the workflow captured large-volume frozen-hydrated protoplast ultrastructure, allowing visualization of major organelles, including the nucleus, mitochondria, vacuoles, ER/Golgi-like membranes, lipid bodies, and subcellular features consistent with nuclear-envelope pores. We further quantified organelle volumes and surface areas from the segmented 3D data, highlighting the potential for quantitative cellular ultrastructure analysis. This cryo-vEM workflow provides a platform for near-native structural studies of isolated plant protoplasts.
Why it matches plant phenotyping methods植物プロトプラストの三次元画像取得・セグメンテーション・オルガネラ形態量化を中核とする手法開発であり、植物の細胞形態形質を抽出するため。
abstractHere we report a cryogenic vEM (cryo-vEM) workflow for unstained plant protoplasts that achieves volumetric imaging of whole vitrified sorghum stem protoplasts
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 · public369 The codes are freely available at https://github.com/xzhang0123/vEMOpen asset ↗https://github.com/xzhang0123/vEM · xzhang0123/vEMpdf-page:10 lines:1-24Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset includes 1045 high-quality point clouds of field-grown maize collected using a terrestrial laser scanner (TLS). Point clouds of 520 plants from this dataset were segmented and annotated using a graph-based segmentation method to isolate individual leaves and stalks, ensuring consistent labeling across all samples. This labeled data was then used for fitting procedural models that provide a structured parametric representation of the maize plants. The leaves of the maize plants in the procedural models are represented using Non-Uniform Rational B-Spline (NURBS) surfaces that were generated using a two-step optimization process combining gradient-free and gradient-based methods. We conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset also includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled point cloud data (100k, 50k, 10k points), which can be readily used for different downstream computational tasks. MaizeField3D will serve as a comprehensive foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.
Why it matches plant phenotyping methods3D点群の収集・分割・注釈・手続き型モデル化を中核とする、植物表現型解析向けの再利用可能なデータセットである。
abstractwe present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research.
Reproduction assets foundThe paper's own MaizeField3D dataset (1045 TLS point clouds, 520 segmented/annotated plants, metadata, STL/DAT procedural model outputs) is publicly available on Hugging Face, with a project website and public GitHub code for the procedural NURBS surface generation used in the analysis.Dataset · publicThe MaizeField3D dataset is publicly available on the Hugging Face Datasets platform at https://huggingface.co/datasets/BGLab/MaizeField3D. It includes high-resolution point clouds, segmented plant models, metadata, and reconstructed outputs in STL and DAT formats.Open asset ↗BGLab/MaizeField3Dhtml-lines:343-354Code · publicThe code for procedural NURBS surface generation used in this work is available at https://github.com/baskargroup/ProceduralMaize3D.Open asset ↗baskargroup/ProceduralMaize3Dhtml-lines:343-354Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotLeafStem / branchMorphology / geometry measurementLeaf traitsStress response / toleranceWater status / transpiration
Background Plant functional traits provide key information about species' ecological strategies and their responses to environmental disturbances such as fire. This dataset documents 14 morpho-functional traits of leaves (specific leaf area, leaf water content and leaf dry matter content), stems (maximum height, bark thickness, diameter at 40 cm, wood density, stem water content and stem dry matter content), one regenerative trait (resprouting capacity), as well as fire-related traits (ignition time, flaming time and flammability) and growth form in 50 woody plant species (27 trees, 22 shrubs and one liana) inhabiting a pine-oak forest in the "Barranca del Cupatitzio" National Park (BCNP), located in Uruapan, Michoacán, Mexico. This dataset is formatted according to the Darwin Core Archive standard and is publicly available for use. New information This dataset is standardised under the Darwin Core framework. It includes 14 morpho-functional and fire-related traits. The data were obtained from 50 woody species with a diameter at breast height (DBH) > 2.5 cm (27 trees, 22 shrubs and one liana), in a pine-oak forest located in the western Trans-Mexican Volcanic Belt, in the Municipality of Uruapan, Michoacán, Mexico. Here, we report flammability-related traits for these species for the first time. The collection of biological material and the measurement of functional traits followed internationally recognised protocols, ensuring methodological consistency and facilitating integration with other global datasets. The dataset includes values for flammability, ignition time, flaming time, specific leaf area, wood density, stem water and dry matter content, bark thickness, leaf water and dry matter content, maximum height, stem diameter at 40 cm above the ground, plant growth form and resprouting capacity. This information is particularly valuable for studies in functional ecology, ecological restoration, the dynamics of woody plant communities and fire management in temperate, fire-prone ecosystems.
Why it matches plant phenotyping methods植物の形態・機能・火災関連形質を体系的に収集し、Darwin Coreで標準化した再利用可能なデータセットであり、形質測定とデータ提供が中心である。
abstractThis dataset documents 14 morpho-functional traits of leaves
Reproduction assets foundThe paper is a data paper whose own trait/flammability dataset is deposited publicly on GBIF via DOI 10.15468/46f8xe, explicitly linked as the data package for this study's measurements.Dataset · publiche Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits use, distribution and reproduction in any medium, provided the original authors are properly credited.
Data resources
Data package title
Functional traits related to fire in woody species from Barranca del Cupatitzio National Park
Resource link
https://doi.org/10.15468/46f8xe
Number of data sets
2
Data set 1.
Data set name
occurrence.txt
Data format
Darwin Core
Data set 1.
Column label
Column description
id
Unique identifier for each occurrence.
institutionID
The identifier for the institution having custody of the specimens.
institutionCode
Full name of the institution having custody of the specimeOpen asset ↗10.15468/46f8xelines:87-297Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Abstract Plant cell walls are dynamic composites whose architecture determines growth, mechanics, and environmental resilience. Efforts to link pectin structure to function have been limited by the lack of molecular probes with sufficient specificity, a gap that becomes even more pronounced for the intricately branched rhamnogalacturonon-II (RG-II) subclass. Here we report the first fluorescent probes with defined specificity to RG-II, engineered from catalytic site mutants of Bacteroides thetaiotaomicron glycoside hydrolases BT1010 and BT0996. These enzyme-derived probes bind RG-II monomer with high affinity, discriminate against dimeric forms, and localize to cell corners and junctions in Arabidopsis thaliana stems, consistent with RG-II’s unique ability among wall polysaccharides to form borate-mediated, covalent crosslinkages between molecules. Application of these probes revealed spatial partitioning distinct from the homogalacturonan (HG)- and rhamnogalacturonan I (RG-I)-enriched middle lamella, highlighting functional specialization among pectic domains, with RG-II reinforcing cell junctions while HG and RG-I mediate wall flexibility. Our work establishes a generalizable framework for transforming CAZymes into high-precision imaging reagents, enabling molecular-level visualization of structurally complex polysaccharides in the cell wall.
Why it matches plant phenotyping methodsRG-IIを特異的に可視化する蛍光プローブを開発し、植物細胞壁内の空間分布という植物状態を画像で測定する手法を示しているため、フェノタイピング手法が中心的です。
abstractHere we report the first fluorescent probes with defined specificity to RG-II
Reproduction assets foundThe paper deposits its raw microscopy z-stacks and maximum projections on OSF and its custom MATLAB image-analysis code on GitHub, both with explicit availability statements and public URLs.Dataset · publicMicroscopy data that support the findings of this study have been deposited in Open Science Framework. Raw z-stacks, output maximum intensity projections, and annotated figure images in greyscale are available at (https://osf.io/8utvs/overview).Open asset ↗Open Science Frameworklines:319-349Code · publicMATLAB code used for image analysis is available at https://github.com/kristenthorne/GHprobes.git, with usage instructions and example input and output files provided.Open asset ↗GitHub · kristenthorne/GHprobeslines:319-349Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
The comprehensive understanding of the dormant pruning patterns in pear trees, along with the accurate identification of shoots suitable for pruning, is essential for implementing automated pruning and fruit production. Due to the complexity of tree architecture, previous descriptions of pruning strategies were qualitative summaries based on experience. In this study, we proposed a high-precision shoot extraction pipeline through point cloud alignment at different times, enabling a quantitative analysis of the pruning patterns. The structural parameters of 126 full bearing period pear trees, encompassing two cultivars and three architectures, were characterized, including the shoot number, single shoot angle and length, as well as shoot length density. The validation results demonstrated that the method attained an R 2 of 0.82, 0.92, and 0.85 for shoot number, single shoot angle and length, respectively, with mean absolute error of 18.72, 6.08°, and 0.13 m. The findings indicate that tree architecture exerts a greater influence on pruning compared to cultivar, particularly in Cuiguan, where significant differences were observed across diverse tree architectures. The characters of the corresponding annual (one-year-old) shoots (AS) and pruned shoots (PS) exhibit similar distribution. The AS, constituted 78.62% of the PS number, and 94.90% of length of AS were pruned, indicating that dormant pruning in full bearing period pear tree primarily targets at the annual shoots, and the pruning of annual shoots is mainly by thinning. This study could help the automatic pruning system make pruning decisions and promotes the development of fine orchard management.
Why it matches plant phenotyping methodsナシ樹のシュート形態を点群アライメントで抽出・定量化する手法を開発し、精度検証まで行っており、植物フェノタイピング手法が中心です。
abstractwe proposed a high-precision shoot extraction pipeline through point cloud alignment at different times, enabling a quantitative analysis of the pruning patterns.
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' source code and point cloud samples at a public GitHub repository, matching an allowed URL.Code · publicThe source code and point clouds samples used in this study are publicly available at: https://github.com/Lixiao-bai/Pear_branch_seg_and_analysis .Open asset ↗Lixiao-bai/Pear_branch_seg_and_analysislines:227-309Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotFlowerLeafRootStem / branchClassificationDisease symptoms / severity
This dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes. The images were captured from one family-owned durian orchard and four nearby orchards in Vinh Long Province, Vietnam. Each class contains approximately 405-427 raw images, photographed using an iPhone 14 under natural field conditions. These conditions simulate typical farmer photography practices, featuring varied angles, inconsistent lighting, and complex environmental backgrounds, resulting in significant visual noise. All raw JPEG images were manually reviewed and cropped on macOS systems using MacBook devices equipped with Apple M4 chips to focus on disease-affected regions, reduce file size, and minimize background noise. The processed, cropped images are provided in PNG format with variable dimensions. Images were resized to 224×224 pixels only during model training for machine learning experiments. Disease symptoms were verified in collaboration with plant pathologists to ensure accurate classification. This dataset is publicly available on Mendeley Data and is suitable for developing and evaluating machine learning models in plant disease classification. It is particularly valuable for testing model performance under real-world, noisy conditions and for supporting the creation of mobile or edge-based diagnostic tools in agriculture.
Why it matches plant phenotyping methods植物病徴を画像で直接捉えた公開データセットで、植物病害状態の分類モデル開発・評価を主目的とするため、表現型計測データセットとして中心的です。
abstractThis dataset comprises 5452 images of durian plant parts-including leaves, flowers, branches, stems, and roots-affected by ten common disease classes.
Reproduction assets foundThe paper is a Data in Brief describing a public durian disease image dataset (5452 field images, ten classes) deposited on Mendeley Data with an explicit DOI and direct URL, matching an allowed URL. This is a paper-specific, publicly available image dataset directly reproducing the paper's phenotyping measurements. NoDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/mhjwyb5p48
Direct URL to data: https://data.mendeley.com/datasets/mhjwyb5p48/1Open asset ↗Mendeley Data · 10.17632/mhjwyb5p48lines:47-125Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² = 0.78, RMSE = 3 cm, MAPE = 10 %). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 % for GH, 83 % for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.
Why it matches plant phenotyping methodsUAS画像、SAM、CNNを統合したピーナッツの草冠高・生育型・主茎優勢度の自動推定パイプラインを開発・検証しており、表現型取得と抽出手法が研究の中心である。
abstractThis study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN).
Reproduction assets foundThe authors deposited the paper's phenotyping inputs (plot-level aerial RGB images and nDSM maps for GH/MP classification) publicly on Zenodo. The analysis source code is only available upon request, so it does not qualify as a public asset.Dataset · public0126 .
Contributor Information
Peggy Ozias-Akins, Email: pozias@uga.edu.
Changying Li, Email: cli2@ufl.edu.
Appendix A.
Supplementary data
The following is the supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
The datasets supporting this study are publicly available on Zenodo [ https://doi.org/10.5281/zenodo.17274012 ]. They include plot-level aerial RGB images and nDSM maps from peanut breeding fields for classification of Growth Habit and Mainstem Prominence. The source code used for data processing and analysis will be made available upon request.
References
1. U. S. Department of Agriculture . USDA National Agricultural Statistics ServiOpen asset ↗Zenodo · 10.5281/zenodo.17274012lines:327-356Code / dataset availability confirmedEurope PMC · Crossref · checked 6 Sept 2026
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models will perform accurate disease detection through the identification of spatial patterns. However, they cannot predict the diseases at the early stages due to the lack of temporal information. Temporal observations will help perform continuous monitoring and detect minute changes in the crops at the early times. To tackle this problem, we proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively. The SSDHR network uses multi-branch convolution kernels to extract distinct discriminative characteristics rather than conventional leaf-based indicators. It incorporates spatial, and temporal-based attention mechanism Symmetric Fusion Attention (SFA) to improve feature selection and XGBoost (XGB) classifier for better stability. According to experimental findings, the suggested framework achieves a 99.25% accuracy rate in identifying and classifying 13 paddy classes, including normal, blast, hispa, tungro, white stem borer, brown spot, leaf roller, downy mildew, yellow stem borer, bacterial leaf blight, bacterial leaf streak, black stem borer, and bacterial panicle blight.
Why it matches plant phenotyping methodsイネ病害の症状を空間・時間画像データから検出・分類する深層学習フレームワークを提案し、その性能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す。
abstractwe proposed Self-Supervised Deep Hierarchical Reconstruction (SSDHR), and Long Short-Term Memory (LSTM) which perform early disease detection based on the spatial and temporal data respectively.
Reproduction assets foundThe paper's phenotyping inputs are the publicly available Paddy Doctor image dataset (16,225 annotated paddy disease images) hosted on IEEE DataPort, with an explicit dataset link and data availability statement. No author analysis code or trained models are shared.Dataset · publicThe dataset used in our study was obtained from the publicly available repository titled “Paddy Disease and Pest Image Dataset” on IEEE Data Port. The dataset comprises 16,225 high-quality images across 13 classes, including 12 paddy disease and pest categories along with healthy samples.Open asset ↗IEEE Data Porthtml-lines:118-200Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Sugarcane stem node detection is critical for monitoring sugarcane growth, enabling precision cutting, reducing spuriousness, and improving breeding for resistance to downfall. However, in complex field environments, sugarcane stem nodes often suffer from reduced detection accuracy due to background interference and shadowing effects. For this reason, this paper proposes an improved sugarcane stem node detection model based on YOLO11. This study incorporates the ASF-YOLO (Attentional Scale Sequence Fusion based You Only Look Once) mechanism to enhance the feature fusion layer of YOLO11. Additionally, a high-resolution detection layer, P2, is integrated into the fusion module to improve the model's ability to detect small objects-particularly sugarcane stem nodes-and to better handle multi-scale feature representations. Secondly, to better align with the P2 small-object detection layer, this paper adopts a shared convolutional detection head named LSDECD (Lightweight Shared Detail-Enhanced Convolutional Detection Head), which can better deal with small target detection while reducing the number of model parameters through parameter sharing and detail-enhanced convolution. Using soft-NMS (non-maximum suppression) to replace the original NMS and combining with Shape-IoU, a bounding box regression method that focuses on the shape and scale of the bounding box itself, makes the bounding box regression more accurate, and solves the problem of the impact of detection caused by occlusion and illumination. Finally, to address the increased complexity introduced by the addition of the P2 detection layer and the replacement of the detection head, channel pruning is applied to the model, effectively reducing its overall complexity and parameter count. The experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95, respectively, which are 11.9% and 11.1% higher than the original YOLO11n, and the model after pruning also has 10.8% and 9.3% higher than the original YOLO11n, respectively, and the number of parameters is reduced to 279,778, and model size is reduced to 1.3MB. The computational cost decreased from 11.6 GFlops to 6.6 GFlops.
Why it matches plant phenotyping methodsサトウキビ茎節という植物器官の検出を対象に、改良YOLOモデルの開発と性能評価を中心的に行っており、再利用可能な画像ベース表現型取得手法に該当する。
abstractThe experimental results show that the model before pruning has 96.1% and 53.2% mean average precision mAP50 and mAP50:95
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sugarcane stem node dataset and the study's source code on ScienceDB with public DOIs, both matching allowed URLs.Dataset · publicppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 ThOpen asset ↗ScienceDB · 10.57760/sciencedb.27078lines:65-90Code · publicno pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .
Data Availability
All data and code underlying the findings of this study are fully available without restriction from the ScienceDB. The sugarcane stem node dataset is available at DOI: https://doi.org/10.57760/sciencedb.27078 The source code used in this study is available at DOI: https://doi.org/10.57760/sciencedb.27287 .Open asset ↗ScienceDB · 10.57760/sciencedb.27287lines:65-90Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
In this study, we explore the application of videogrammetry for 3D reconstruction in complex forest environments, aiming to enhance forest inventory measurement methods. Traditional techniques are often labor-intensive and lack scalability in dense or challenging terrain. We assess the efficacy of videogrammetry for generating 3D point clouds in complex forest environments, focusing on an Insta 360 Pro 2 setup with six fish-eye cameras. Harnessing this lightweight and user-friendly technology, we aim to elevate the process of data collection while delivering realistic visual representations of forest areas. Our approach enables the estimation of key forest characteristics, such as tree distribution and Diameter at Breast Height (DBH). The average errors for tree position and DBH measurements range from 5.2 cm to 18.8 cm and from 0.9 cm to 1.9 cm, respectively. The reconstructed 3D tree information is structurally similar to data obtained with Terrestrial Laser Scanning (TLS), with normally distributed Multiscale Model-to-Model Cloud Comparison (M3C2) errors with a mean of 0 cm and a standard deviation of 15 cm to 22 cm. Our method reduces the need for manual data collection, thus supporting effective forest management and planning.
Why it matches plant phenotyping methods森林内の樹木形態（樹木位置・胸高直径）を videogrammetry で推定する手法を開発し、TLS と比較検証しており、植物フェノタイピング手法が中心である。
abstractWe assess the efficacy of videogrammetry for generating 3D point clouds in complex forest environments
Reproduction assets foundThe authors publicly deposited the videogrammetric point clouds generated by their pipeline (with walkthrough demos and TLS comparison videos) on Zenodo, directly reproducing this paper's 3D reconstruction measurements.Dataset · publicd have appeared
to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary material related to this article can be found online
at https://doi.org/10.1016/j.ecoinf.2025.103398.Data availability
The generated videogrammetric point clouds using the proposed
pipeline are available for download here: https://doi.org/10.5281/zenodo.16258209. The folder also contains walkthrough demos of the
point clouds, as well as video comparisons with TLS-derived point
clouds.
References
AgiSoft, 2018. AgiSoft metashape professional (version 1.4.5) (software),. Available
Online: http://www.agisoft.com.Alsadik, B., Gerke, M., Vosselman, G., 2015. Efficient use of video for 3D moOpen asset ↗zenodo · 10.5281/zenodo.16258209pdf-raw-page:12 lines:1-70Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Computer vision has emerged as a critical enabler of sustainable production in protected agriculture by offering efficient and non-invasive crop disease diagnosis. The development of accurate disease recognition models relies heavily on the availability of high-quality image datasets. This study introduces a tomato disease image dataset collected in 2024 from greenhouse facilities within a modern agricultural park in Sichuan Province, China. The dataset comprises 1026 high-resolution images, including 417 images of viral disease, 82 images of gray mold, and 527 images of bacterial wilt, totaling approximately 2.78 GB. Captured under real-world greenhouse conditions and from multiple angles and distances, the images effectively capture multi-scale phenotypic disease features. Manual annotation was conducted using the LabelImg tool under the guidance of plant pathology experts, with labeled regions covering leaves, fruits, and stems. Annotation files are stored in XML format, each corresponding to a specific image. This dataset is well-suited for research in disease classification, object detection, and phenotyping, and supports deep learning model training and cross-crop transfer learning applications.
Why it matches plant phenotyping methodsトマト病害の症状を画像で捉え、分類・検出モデル用に専門家アノテーションした再利用可能なデータセットであり、植物病害状態の表現型取得が中心である。
abstractThe development of accurate disease recognition models relies heavily on the availability of high-quality image datasets.
Reproduction assets foundThe paper is a Data in Brief article describing a public tomato disease image dataset (1026 annotated images) deposited on Mendeley Data with a direct URL and DOI, matching an allowed URL exactly.Dataset · publicwas conducted at the Modern Agricultural Science and Technology Innovation Demonstration Park of the Sichuan Academy of Agricultural Sciences (30.7797° N, 104.2082° E), located in Sichuan Province, China.
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/c2×8rynybg.1
Direct URL to data: https://data.mendeley.com/datasets/c2×8rynybg/1
Related research article
None.
1
Value of the Data
The dataset contains 1026 annotated images of tomato plants exhibiting three major disease types, collected in 2024 from greenhouse environments in Sichuan’s Modern Agricultural Demonstration Park. Plant pathology specialists manually labeled all samples. Its technical sOpen asset ↗Mendeley Data · 10.17632/c2×8rynybg.1lines:1-52Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imaginary. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian Splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear ‘Bartlett,’ which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D Gaussian Splattingを用いて果実の3D再構成と空間的成長形質の非破壊計測パイプラインを開発し、手動記録との精度比較で検証しているため、フェノタイピング手法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics
Reproduction assets foundThe paper's data availability statement deposits a subset of the generated 3DGS fruit reconstruction models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data is request-only. No author analysis code is explicitly deposited.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Code / dataset availability 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 confirmedarXiv · checked 6 Sept 2026
Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.
Why it matches plant phenotyping methods植物の多視点画像取得、アノテーション付きデータセット、深層学習による分類・検出・セグメンテーションを中心に開発・検証した、明確な植物フェノタイピング手法研究です。
abstractwe developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols.
Reproduction assets foundThe paper's TomatoMAP dataset (images, annotations) is publicly deposited in e!DAL at IPK with an explicit DOI URL given in the Data Records section.Dataset · publicDataset is deposited in e!DAL (electronic data archive library) of IPK (Leibniz Institute of Plant Genetics
and Crop Plant Research): https://doi.ipk-gatersleben.de/DOI/10bb9f14-ce90-4747-836f-cf61dfb5eea1/Open asset ↗e!DAL · 10bb9f14-ce90-4747-836f-cf61dfb5eea1pdf-page:7 lines:1-73Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods.
Why it matches plant phenotyping methods植物のマルチビュー画像から樹木の3D骨格・枝構造を推定する計算手法を開発し、実植物で既存法と比較検証しているため、植物形態フェノタイピング手法が中心である。
abstractwe introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks
Reproduction assets foundThe paper's authors explicitly state their implementation is publicly available on GitHub, which is the paper-specific computational analysis code for the mask-guided tree skeleton extraction method.Code · publicOur implementation is available in the public GitHub repository ( https://github.com/huntorochi/Masks-to-Skeleton , accessed on 18 May 2025).Open asset ↗huntorochi/Masks-to-Skeletonlines:27-41Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
MaizeSugarcaneTomatoLiDAR / point cloudLeafStem / branchSegmentation
Organ instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation. However, most current cloud segmentation methods are usually designed for specific crop, hardly fit for both monocotyledonous and dicotyledonous crops which have significant structural differences. This study therefore proposed a two-stage method with higher generalization ability for single-plant organ instance segmentation based on PointNeXt and Quickshift++. The effectiveness of this method was tested on different types of crops. The dataset includes point clouds of 122 self-acquired sugarcanes, 49 open-accessed maizes, and 77 open-accessed tomatoes. The improved PointNeXt model was trained to implement the semantic segmentation of stems and leaves. The average mOA and mIoU on the test set reaches 96.96 % and 87.15 %, respectively. The Quickshift++ algorithm was then applied to encode the global spatial structure and local connections of plants for rapid localization and segmentation of leaf instance. Our approach outperformed four SOTA methods, ASIS, JSNet, DFSP, and PSegNet in terms of both quantitative and qualitative segmentation results, achieving average values for mPrec, mRec, mF1, and mIoU of 93.32 %, 85.60 %, 87.94 %, and 81.46 %, respectively. The proposed method also yields excellent results for several other plants in their early stages, indicating its generalization ability and applicability for organ instance segmentation for different plants, thus providing a powerful tool for plant phenotypic research.
Why it matches plant phenotyping methods植物器官の3D点群から茎・葉のインスタンスを抽出する手法を開発・比較検証しており、器官レベル表現型推定のための中心的なフェノタイピング手法である。
abstractOrgan instance segmentation of 3D plant point clouds is a crucial prerequisite for organ-level phenotype estimation.
Reproduction assets foundThe authors state their dataset and code were uploaded to a public GitHub repository, and the paper's maize/tomato point cloud inputs come from the public Pheno4D dataset. Both are paper-specific, public, and actionable.Code · publicThe dataset and the code have been uploaded to Github: https://github.com/ice3664/3d-plant-organ-segmentation/tree/master.Open asset ↗https://github.com/ice3664/3d-plant-organ-segmentation/tree/masterhtml-lines:555-579Dataset · publicthe point clouds of maize and tomato were selected from the Pheno4D dataset [25] which can be accessed via https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗html-lines:109-124Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Dendrometry is the main non-invasive macroscopic technique commonly used in plant physiology and ecophysysiology studies. Over the years several types of dendrometric techniques have been developed, each with their respective strengths and drawbacks. Automatic and continuous monitoring solutions are being developed, but are still limited, particularly for non-invasive monitoring of large-diameter trunks. In this study, we propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference, is non-invasive and has no upper limit on the trunk diameter on which it can be installed. We performed a 3-month validation experiment during which we deployed a fibre-optic cable at three localities around the trunks of two specimens of Brachychiton. We verified the accuracy of this new method through comparison with a conventional point-dendrometer, and we observed a consistent time lag between the various measurement locations that varies with the meteorological conditions. Finally, we discuss the feasibility of the fibre-based dendrometer in the context of existing dendrometric techniques and practical experimental considerations.
Why it matches plant phenotyping methods植物幹の周囲長変化を連続測定する新規ファイバー光学式デンドロメータを開発し、従来法との比較で精度を検証しており、植物表現型取得法が研究の中心です。
abstractwe propose a new type of automated dendrometer based on distributed fibre-optic sensing that continuously measures the change in stem circumference
Reproduction assets foundThe authors explicitly state that all data (DSTS strain recordings, dendrometer time series) and analysis scripts needed to reproduce the paper's figures are publicly deposited on Figshare.Dataset · publicnuscript. For the analysis we made use of the following Python libraries: Matplotlib 3.7.2 332
[Hunter, 2007], NumPy 1.25.1 [Harris et al., 2020], Pandas 2.0.3 [Pandas Development Team, 2023], 333
SciPy 1.11.1 [Virtanen et al., 2020]. All the data and scripts needed to reproduce the figures in this 334
study are available here: https://doi.org/10.6084/m9.figshare.25773432. 335
References
T. Ameglio and P. Cruiziat. Daily Variations of Stem and Branch Diameter: Short Overview from a Developed Example. In
T. K. Karalis, editor, Mechanics of Swelling, NATO ASI Series, pages 193–204, Berlin, Heidelberg, 1992. Springer. ISBN
978-3-642-84619-9. doi: 10.1007/978-3-642-84619-9 9.
T. Ameglio, H. CochOpen asset ↗figshare · 10.6084/m9.figshare.25773432pdf-raw-page:14 lines:1-45Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The present dataset is a collection of multispectral images designed for development of detection algorithms for grapevine diseases like Flavescence dorée (FD) and Esca (ED). Although FD severely threatens viticulture, there are few public datasets and none with multispectral data collected in the field. The collected images have been taken from a frontal perspective of vineyard plants that highlights details of leaves and trunks facilitating detailed disease analysis. The data were collected using a Micasense RedEdge-P multispectral camera, capturing six spectral bands across 172 image captures of three different grapevine varieties used in Lambrusco wines: Ancellotta, Marani, and Salamino. The dataset includes raw and processed images, calibration images for the multispectral camera, annotations detailing plant health conditions, and Python-based usage examples for researchers. Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. The dataset is a valuable resource for advancing remote and proximal sensing in precision agriculture.
Why it matches plant phenotyping methodsブドウ樹の病害状態を対象とするマルチスペクトル画像データセットで、画像・校正・アノテーション・利用例を含む再利用可能な資源として構築されており、植物フェノタイピング手法の基盤が中心です。
titleA dataset for vineyard disease detection via multispectral imaging.
Reproduction assets foundThe paper is a data descriptor for a multispectral vineyard disease detection dataset deposited by the authors on Zenodo, including raw/processed images, annotations, and Python usage examples. The two Micasense GitHub repositories are generic vendor libraries, not paper-specific assets.Dataset · publicgio Emilia, Emilia-Romagna, Italy). It is managed by the RIMLab laboratory at the University of Parma, Parco Area delle Scienze 181/A, 43100 Parma, Italy.
Data accessibility
Repository name: A Dataset for Vineyard Disease Detection via Multispectral Imaging
Data identification number: 10.5281/zenodo.14936376
Direct URL to data: https://zenodo.org/records/14936376
Related research article
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The dataset features grapevine images which is a high-value plant used for wine production. Italy and other European nations are among the world's largest wine exporters. For this reason, diseases such as Flavescence Dorée (FD) and Esca, that cause severe damage to both the plant aOpen asset ↗Zenodo · 10.5281/zenodo.14936376lines:1-49Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Introduction Recent advancements in sensor technologies have enabled collection of many large, high-resolution plant images datasets that could be used to non-destructively explore the relationships between genetics, environment and management factors on phenotype or the physical traits exhibited by plants. The phenotype data captured in these datasets could then be integrated into models of plant development and crop yield to more accurately predict how plants may grow as a result of changing management practices and climate conditions, better ensuring future food security. However, automated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking. In this study, we explore interdisciplinary application of MapReader, a computer vision pipeline for annotating and classifying patches of larger images that was originally developed for semantic exploration of historical maps, to time-series images of whole oilseed rape (Brassica napus) plants. Methods Models were trained to classify five plant structures in patches derived from whole plant images (branches, leaves, pods, flower buds and flowers), as well as background patches. Three modelling methods are compared: (i) 6-label multi-class classification, (ii) a chain of binary classifiers approach, and (iii) an approach combining binary classification of plant and background patches, followed by 5-label multi-class classification of plant structures. Results A combined plant/background binarization and 5-label multi-class modelling approach using a ‘resnext50d_4s2x40d’ model architecture for both the binary classification and multi-class classification components was found to produce the most accurate patch classification for whole B. napus plant images (macro-averaged F1-score = 88.50, weighted average F1-score = 97.71). This combined binary and 5-label multi-class classification approach demonstrate similar performance to the top-performing MapReader ‘railspace’ classification model. Discussion This highlights the potential applicability of the MapReader model framework to images data from across scientific and humanities domains, and the flexibility it provides in creating pipelines with different modelling approaches. The pipeline for dynamic plant phenotyping from whole plant images developed in this study could potentially be applied to imagery from varied laboratory conditions, and to images datasets of other plants of both agricultural and conservation concern.
Why it matches plant phenotyping methods植物全体画像から葉・花・莢などの構造を自動抽出・分類する動的フェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractautomated methods capable of reliably and efficiently extracting meaningful measurements of individual plant components (e.g. leaves, flowers, pods) from imagery of whole plants are currently lacking.
Reproduction assets foundThe paper's phenotyping analysis is based on a public RGB image dataset of Brassica napus plants, explicitly deposited by the authors with a public URL. MapReader is a generic pre-existing library and the HuggingFace railspace models are cited prior work, not paper-specific assets.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://research.aber.ac.uk/en/datasets/collection-of-side-view-and-top-view-rgb-images-of-brassica-napus .Open asset ↗research.aber.ac.uklines:982-1027Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R 2 ) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.
Why it matches plant phenotyping methods3D点群の分割・個体抽出ワークフローを開発し、綿花の果実数と枝・果実の空間分布という植物形質を定量化・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 developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants.
Reproduction assets foundThe authors explicitly state that the code, data, and trained PointNet++ weights for cotton boll and branch mapping are publicly available in their GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicThe code, data, and training weights for cotton boll and branch mapping are available at https://github.com/UGA-BSAIL/cotton_organ_mapping.git .Open asset ↗UGA-BSAIL/cotton_organ_mappinglines:101-109Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
Large-scale manual measurements of plant architectural traits in tomato growth are laborious and subjective, hindering deeper understanding of temporal variations in gene expression heterogeneity. This study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system. The SegFormer with fusion of multispectral and depth imaging modalities was employed to semantically segment plant organs from the registered RGB-D and multispectral images. Organ point clouds were then generated and clustered into instances. Finally, six key architectural traits, including fruit spacing (FS), inflorescence height (IH), stem thickness (ST), leaf spacing (LS), total leaf area (TLA), and leaf inclination angle (LIA) were extracted and the temporal broad-sense heritability folds were plotted. The root mean square errors (RMSEs) of the estimated FS, IH, ST, and LS were 0.014, 0.043, 0.003, and 0.015 m, respectively. The visualizations of the estimated TLA and LIA matched the actual growth trends. The broad-sense heritability of the extracted traits exhibited different trends across the growth stages: (i) ST, IH, and FS had a gradually increased broad-sense heritability over time, (ii) LS and LIA had a decreasing trend, and (iii) TLA showed fluctuations (i.e. an M-shaped pattern) of the broad-sense heritability throughout the growth period. The developed system and analytical approach are promising tools for accurate and rapid characterization of spatiotemporal changes of tomato plant architecture in controlled environments, laying the foundation for efficient crop breeding and precision production management in the future.
Why it matches plant phenotyping methods植物形態形質を取得するUGV型マルチモーダル画像フェノタイピングシステムと解析手法の開発・定量評価が研究の中心であり、誤差検証も行っているため。
abstractThis study develops a high-throughput approach for characterizing tomato architectural traits at different growth stages and mapping temporal broad-sense heritability using an unmanned ground vehicle-based plant phenotyping system.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' trait-extraction pipeline code and example data on a public GitHub repository, matching the allowed URL.Code · publicThe pipeline code and example data related to this project are available as open source on GitHub ( https://github.com/DigBigPigForU/Tomato-architectural-trait-extraction ).Open asset ↗Tomato-architectural-trait-extractionlines:822-958Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Premise The quantification of plant drought resistance, particularly embolism formation, within and across species, is critical for ecosystem management and agriculture. We developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity ( P 50 ) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method. Methods and results Our protocol uses inexpensive USB microscopes, which are secured along with the plants to a pegboard base to avoid movement. A Python program automatized the image acquisition. This method was applied to quantify P 50 in an exotic species ( Nicotiana glauca ) and native species ( Rhus integrifolia ) of the Mediterranean vegetation in Baja California, Mexico. Conclusions The intra- and interspecific patterns of variation in stem P 50 of N. glauca and R. integrifolia were obtained using the low-cost optical method with widely available and affordable materials that can be easily replicated for other species.
Why it matches plant phenotyping methods植物の茎の水理的脆弱性（P50）を測定する低コスト光学プロトコルを開発し、USB顕微鏡とPythonによる画像取得を用いて適用・検証しており、表現型取得法が研究の中心である。
abstractWe developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity ( P 50 ) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicGranados (CICESE) for the initial design of the microscope stands, and Alexis Crespo Michel (CICESE) for his assistance in developing the multi‐threaded version of the image capture Python program.
DATA AVAILABILITY STATEMENT
Data of all experiments are provided in the Supporting Information. The Python Program is available at: https://github.com/miguel-aalonso/lowcost_P50 .
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McAdaOpen asset ↗miguel-aalonso/lowcost_P50lines:264-337Code / dataset availability confirmedOpenAlex · checked 6 Sept 2026
MaizeTomatoGreenhouseLiDAR / point cloudLeafStem / branchSegmentation
Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. Furthermore, their heavy reliance on high-quality annotated data restricts their use in high-throughput settings. To address these limitations, we propose a novel multi-stage region-growing algorithm based on an octree structure for efficient stem-leaf segmentation in maize point cloud data. The method first extracts key geometric features through octree voxelization, significantly improving segmentation efficiency. In the region-growing phase, a preliminary structural segmentation strategy using fitted cylinder parameters is applied. A refinement strategy is then applied to improve segmentation accuracy in complex regions. Finally, stem segmentation consistency is enhanced through central axis fitting and distance-based filtering. In this study, we utilize the Pheno4D dataset, which comprises three-dimensional point cloud data of maize plants at different growth stages, collected from greenhouse environments. Experimental results show that the proposed algorithm achieves an average precision of 98.15% and an IoU of 84.81% on the Pheno4D dataset, demonstrating strong robustness across various growth stages. Segmentation time per instance is reduced to 4.8 s, offering over a fourfold improvement compared to PointNet while maintaining high accuracy and efficiency. Additionally, validation experiments on tomato point cloud data confirm the proposed method’s strong generalization capability. In this paper, we present an algorithm that addresses the shortcomings of traditional methods in complex agricultural environments. Specifically, our approach improves efficiency and accuracy while reducing dependency on high-quality annotated data. This solution not only delivers high precision and faster computational performance but also lays a strong technical foundation for high-throughput crop management and precision breeding.
Why it matches plant phenotyping methodsトウモロコシの3D点群から茎・葉を分割し、表現型パラメータ抽出を可能にするアルゴリズムを開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstract3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction.
Reproduction assets foundThe paper's stem-leaf segmentation experiments are performed on the public Pheno4D maize/tomato point cloud dataset, which the authors explicitly state is publicly available at the IPB Bonn URL. No author analysis code or trained models are reported as publicly released.Dataset · publicThe dataset is available at https://www.ipb.uni-bonn.de/data/pheno4d/ (accessed on
20 January 2025).Open asset ↗Pheno4Dpdf-page:4 lines:1-52Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can help to automate this process. To do so, they need to be able to perceive the surrounding environment to identify target objects such as trees and plants. In this paper, we introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors. Our approach is able to simultaneously provide semantic segmentation, instance segmentation of trunks and fruits, and instance segmentation of trees (a trunk with its fruits). This allows us to identify relevant information such as individual plants, fruits, and trunks, and capture the relationship among them, such as precisely estimate the number of fruits associated to each tree in an orchard. To efficiently evaluate our approach for hierarchical panoptic segmentation, we provide a dataset designed specifically for this task. Our dataset is recorded in Bonn, Germany, in a real apple orchard with a variety of sensors, spanning from a terrestrial laser scanner to a RGB-D camera mounted on different robots platforms. The experiments show that our approach surpasses state-of-the-art approaches in 3D panoptic segmentation in the agricultural domain, while also providing full hierarchical panoptic segmentation. Our dataset is publicly available at https://www.ipb.uni-bonn.de/data/hops/. The open-source implementation of our approach is available at https://github.com/PRBonn/hapt3D.
Why it matches plant phenotyping methodsリンゴ樹・果実・幹を3Dセグメンテーションし、樹ごとの果実数を推定する手法と専用データセットを中心に開発・評価しており、植物の器官形態・収量関連形質の取得に該当する。
abstractwe introduce a novel approach to address the problem of hierarchical panoptic segmentation of apple orchards on 3D data from different sensors.
Reproduction assets foundThe paper introduces the HOPS dataset of annotated 3D apple orchard point clouds (TLS, UAV, UGV, SfM) for hierarchical panoptic segmentation, publicly available at the authors' IPB Bonn page, and releases the open-source implementation (hapt3D) on GitHub. Both are paper-specific, public, and actionable.Code · publicThe open-source implementation of our approach is available at https://github.com/PRBonn/hapt3D .Open asset ↗PRBonn/hapt3Dlines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R 2 = 0.934) and RGB (R 2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15 , were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant ( pag1 ), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
Why it matches plant phenotyping methodsUAV搭載RGB・LiDARによる綿花草丈の高スループット取得・推定プラットフォームの開発と精度検証が研究の中心であり、GWASや遺伝子機能解析は応用部分です。
abstracta high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' source code, UAV-captured images, and analysis datasets in a public GitHub repository, which directly supports this paper's cotton plant-height phenotyping measurements and computational analysis.Code · publicThe source code, images captured by UAVs, data obtained from the analysis, and other datasets supporting the results presented here are available at https://github.com/Liqiangfan/419-cotton-plant-height-datasets .Open asset ↗Liqiangfan/419-cotton-plant-height-datasetslines:142-154Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity and sustaining ecosystem services in cities. However, comprehensive field assessments are resource-intensive, and landscape-level approaches may overlook heterogeneity within urban regions. To address this challenge, we combined remote sensing with field inventories to comprehensively map and analyze urban forest attributes in forest patches across the Minneapolis-St. Paul Metropolitan Area (MSPMA) in a multistep process. First, we developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP). These models enabled accurate predictions of forest attributes, specifically nine metrics of plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, dbh, and canopy density), and structural complexity (variability in canopy height, dbh, and canopy density) with relative errors ranging between 11% and 21%. Second, we applied these machine learning models to predict diversity metrics for 804 additional plots from GEDI and Sentinel-2. Finally, we applied Bayesian multilevel models to the predicted diversity metrics to assess the influence of multiple factors-patch dimensions, landscape attributes, plot position, and jurisdictional agency-on these forest attributes across the 804 predicted plots. The models showed all predictors have some degree of effect on forest attributes, presenting varying explanatory power with R 2 values ranging from 0.071 to 0.405. Overall, plot characteristics (e.g., distance to nearest trail, proximity to forest edge) and jurisdictional agency explained a large portion of the variability across patches, whereas patch and landscape characteristics did not. The relative effect of plot versus management sets of predictors on the marginal ΔR 2 was heterogeneous across metrics and ecological subsections (an ecological classification designation). The multiplicity of determinants influencing urban forests emphasizes the intricate nature of urban ecosystems and highlights nuanced, heterogeneous relationships between urban ecological and anthropogenic factors that determine forest properties. Effectively enhancing biodiversity in urban forests requires assessments, management, and conservation strategies tailored for context-specific characteristics.
Why it matches plant phenotyping methodsGEDI・Sentinel-2と機械学習を統合し、植物の多様性・構造属性を予測する測定手法を開発、誤差評価し、追加プロットへ適用しているため、表現型取得が中心的である。
abstractwe developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP).
Reproduction assets foundThe paper's data availability statement provides three paper-specific public assets: the field vegetation inventory data on EDI, the machine learning ensemble R script on Zenodo, and the Bayesian model summaries on Zenodo.Dataset · publicVegetation data are available (Marcilio‐Silva et al., 2022 ) on the Environmental Data Initiative (EDI) data portal: https://doi.org/10.6073/pasta/166a4b954ecaaabcda75bd51004804a5Open asset ↗Environmental Data Initiative · 10.6073/pasta/166a4b954ecaaabcda75bd51004804a5lines:317-357Code · publicThe R script used for the machine learning model ensemble (Marcilio‐Silva, 2024 ) is available on Zenodo: https://doi.org/10.5281/zenodo.14395998Open asset ↗Zenodo · 10.5281/zenodo.14395998lines:317-357Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotNeRF / 3D Gaussian SplattingLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches.
Why it matches plant phenotyping methods森林内の樹木DBHという個体形態形質を、NeRF・MLS再構成と凸包モデルで推定し、手法比較と精度評価を行っているため、植物形質取得法が中心です。
abstractIn this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest.
Reproduction assets foundThe authors explicitly state that their forest datasets (SLAM and NeRF reconstructions, imagery) and TreeTool modeling code contributions are freely available on their public GitHub repository, which is listed in the allowed URLs.Code · publicAR-inertial SLAM with regards to DBH estimation accuracy.
• Improved DBH estimation accuracy via a trunk modeling approach using convex-hull
and density-based filtering methods.
• Open-source modeling code and forest datasets, including SLAM and NeRF recon-
structions of a mixed-evergreen Redwood forest, are freely available at
https://github.com/harelab-ucsc/RedwoodNeRF (accessed on 7 January 2025).
2. Theoretical Background
2.1. The SLAM Approach
The SLAM problem can be broken into two tasks: building a map of the environment
and simultaneously estimating the robot’s trajectory within that map. More specifically,Open asset ↗harelab-ucsc/RedwoodNeRFpdf-raw-page:2 lines:1-50Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
In this paper, we present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans. Our goal was to enhance the precision of radiative transfer models for modelling tree shading by using highly resolved 3D tree models. The approach was tested on a single apple tree (Malus domestica (Suckow) Borkh.) in a peri-urban setting and was validated by utilising an open-source radiative transfer model and comparing the simulation output with in-situ measurements of photosynthetically active radiation (PAR) as well as simulations utilizing turbid voxels of 0.2 m and 1 m edge length. The in-situ measurements of 60 PAR sensors showed a correlation coefficient (r) of 0.92 with the simulated light intensities for the reconstructed polygons which was higher than for the voxel-based approaches (0.2 m: r = 0.85, 1 m: r = 0.73). We were able to demonstrate that our approach effectively simulates light extinction through the canopy. This innovative method has the potential to easily provide detailed insights into high resolution radiation patterns within forests, which are connected to multiple ecosystem functions like species and habitat diversity.
Why it matches plant phenotyping methodsTLSデータから樹木の木部・樹皮・葉の3D形状を抽出する手法を開発し、PARシミュレーションとの比較で検証しており、植物形態の取得が中心的です。
abstractwe present a new methodology that directly extracts the geometry of woody features (wood and bark) and foliage from 3D data originating from terrestrial laser scans.
Reproduction assets foundThe authors state that all study data (TLS-derived point clouds, PAR measurements) and the full R code for the leaf/wood polygon reconstruction are openly available in their GitHub repository, archived as Frey & Kröner 2024 (JulFrey/dotshadow, Zenodo DOI 10.5281/zenodo.14204435, cited in the references). The Zenodo URLCode · publicAll data relevant to the study and the full R code for the reconstruction of the leaves and woody
compartments can be found at our GitHub repository under open source license (Frey and Kröner
2024).Open asset ↗pdf-page:10 lines:1-56Code / dataset availability confirmedCrossref · Europe PMC · checked 13 Sept 2026
Abstract Functional–structural plant (FSP) models are useful tools for understanding plant functioning and how plants react to their environment. Developing tree FSP models is data-intensive and measuring tree architecture using conventional measurement tools is a laborious process. Light detection and ranging (LiDAR) could be an alternative nondestructive method to obtain structural information about tree architecture. This research investigated how terrestrial LiDAR (TLS)-derived tree traits could be used in the design and parameterization of tree FSP models. A systematic literature search was performed to create an overview of tree parameters needed for FSP model development. The resulting structural parameters were compared to LiDAR literature to get an overview of the possibilities and limitations. Furthermore, a tropical tree and Scots pine FSP model were selected and parametrized with TLS-derived parameters. Quantitative structural models were used to derive the parameters and a total of 37 TLS-scanned tropical trees and 10 Scots pines were included in the analysis. Ninety papers on FSP tree models were screened and eight papers fulfilled all the selection criteria. From these papers, 50 structural parameters used for FSP model development were identified, from which 28 parameters were found to be derivable from LiDAR. The TLS-derived parameters were compared to measurements, and the accuracy was variable. It was found that branch angle could be used as model input, but internode length was unsuitable. Outputs of the FSP models with TLS-derived branch angle differed from the FSP model outcomes with default branch angle. Results showed that it is possible to use TLS for FSP model inputs, although with caution as this has implications for the model variable outputs. In the future, LiDAR could help improve efficiency in building new FSP models, increase the accuracy of existing models, add metrics for optimization, and open new possibilities to explore previously unobtainable plant traits.
Why it matches plant phenotyping methodsTLSによる樹木構造形質の取得・精度比較と、機能構造モデルへの適用が研究の中心であり、植物表現型計測法の実質的な検証・応用に該当する。
abstractLight detection and ranging (LiDAR) could be an alternative nondestructive method to obtain structural information about tree architecture.
Reproduction assets foundThe paper's Data availability statement points to a public 4TU.Centre for Research Data deposit (DOI 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142) containing the data underlying the study, i.e. the TLS-derived tree structural measurements (Guyana tropical trees and Loobos Scots pines) used for FSP model parameterizatonDataset · publicThe data underlying this article are available in 4TU.Centre for Research Data, at https://dx.doi.org/10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142 .Open asset ↗4TU.Centre for Research Data · 10.4121/2b7e832f-12e9-4d0e-92e2-5aef9bcf9142lines:728-782Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Rice tiller angle is a key agronomic trait that regulates plant architecture and plays a critical role in determining rice yield. Given that tiller angle is regulated by multiple genes, it is important to identify quantitative trait loci (QTL) associated with tiller angle. Recently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations. In this study, we extracted tiller angle and various image-based parameters from Red-Green-Blue (RGB) images of a recombinant inbred line (RIL) population derived from a cross between Milyang23 (Indica) and Giho (Japonica). Correlations among the obtained data were analyzed, and through dynamic QTL mapping, five major QTLs (qTA1, qTA1-1, qTA2, qTA2-1, and qTA9) related to tiller angle were detected on chromosomes 1, 2, and 9. Among them, 26 candidate genes related to auxin signaling and plant growth, including the TAC1 (Tiller Angle Control 1) gene, were identified in qTA9 (RM257-STS09048). These results demonstrate the potential of image-based phenotyping to overcome the limitations of traditional manual measurements in crop structure research. Furthermore, the identification of key QTLs and candidate genes related to tiller angle provides valuable genetic insights for the development of high-yielding varieties through crop morphology control.
Why it matches plant phenotyping methodsRGB画像からイネの分げつ角度と画像パラメータを抽出する画像ベース表現型解析を、育種集団で実質的に適用・評価しており、表現型取得法が中心です。
abstractRecently, with the advancement of imaging technology for plant phenotyping, it has become possible to quickly and accurately measure agronomic traits of breeding populations.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13233288/s1 , Figure S1: Quantitative trait loci (QTL) analysis associated with tiller angle in rice using RIL population; Figure S2: QTL distribution for tiller angle across different development stages.Open asset ↗lines:89-100Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed us to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection in images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70% and 86.5% with inference times of around 89 ms for an input size of 1280 × 1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.
Why it matches plant phenotyping methodsブドウの節という明示的な植物器官形質をYOLO画像解析で検出する手法を開発・比較検証し、異なる品種・環境で評価しているため、フェノタイピング手法が中心である。
abstractIn this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10.
Reproduction assets foundThe paper's authors created and openly released a paper-specific grapevine node-detection image dataset (Dão and Douro vineyard images) on Zenodo, cited in the Data Availability Statement.Dataset · publicThe data presented in this study are openly available in the digital repository Zenodo: Douro & Dão Grapevines Dataset for Node Detection— https://doi.org/10.5281/zenodo.10991688 .Open asset ↗Zenodo · 10.5281/zenodo.10991688lines:552-565Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools.
Why it matches plant phenotyping methodsUAS-SfMの処理パラメータと複数ソフトウェアを比較し、個体樹の抽出精度、樹高・DBH、林分断面積を評価する技術検証が中心である。
abstractThis study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms.
Reproduction assets foundThe paper's Data Availability Statement explicitly publishes the project's data and analysis source code to a public GitHub repository, which qualifies as a paper-specific public code asset. No separate phenotype dataset deposit is stated beyond this repository.Code · publicData Availability Statement: Data and analysis source code for this project has been published to the
public domain at: https://github.com/georgewoolsey/uas_sfm_tree_detection.Open asset ↗georgewoolsey/uas_sfm_tree_detectionpdf-page:19 lines:1-56Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing, and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE, which consistently outperformed standard cylinder modeling approaches. Our code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF.
Why it matches plant phenotyping methodsNeRFおよびMLSによる森林再構成から樹木DBHを推定し、凸包モデルによる推定法を提案・比較検証しているため、植物形質取得手法が中心です。
abstractIn this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest.
Reproduction assets foundThe authors explicitly state their code contributions and forest datasets (SLAM and NeRF reconstructions used for DBH estimation) are freely available in a public GitHub repository. The other URLs are a cited third-party tool (NeRFCapture) and a background reference (USDA aerial survey), neither of which is a paper-ownDataset · publicOur code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF .Open asset ↗harelab-ucsc/RedwoodNeRFlines:1-52Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The monitoring of plant diseases in nurseries, breeding farms and orchards is essential for maintaining plant health. Fire blight ( Erwinia amylovora ) is still one of the most dangerous diseases in fruit production, as it can spread epidemically and cause enormous economic damage. All measures are therefore aimed at preventing the spread of the pathogen in the orchard and containing an infection at an early stage [1-6]. Efficiency in plant disease control benefits from the development of a digital monitoring system if the spatial and temporal resolution of disease monitoring in orchards can be increased [7]. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Between 2021 and 2024, data was collected on nine dates under different weather conditions and with different cameras. The data source locations in Germany were the experimental orchard of the Julius Kühn Institute (JKI), Institute of Plant Protection in Fruit Crops and Viticulture in Dossenheim, the experimental greenhouse of the Julius Kühn Institute for Resistance Research and Stress Tolerance in Quedlinburg and the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz. The RGB images were taken on different apple genotypes after artificial inoculation with Erwinia amylovora , including cultivars, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 × 1280 pixels of fire blight infected shoots, flowers and leaves in different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were acquired and integrated into the ERWIAM dataset as a separate class. Each fire blight symptom was annotated with the Computer Vision Annotation Tool (CVAT [8]) using 2-point annotations (bounding boxes) and presented in YOLO 1.1 format (.txt files). The dataset contains a total of 1611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital systems for plant disease monitoring.
Why it matches plant phenotyping methodsRGB画像から植物病徴を検出するための注釈付きデータセットを開発・提示しており、植物病害状態の画像ベース表現型評価が中心である。
abstracta digital disease monitoring system for fire blight based on RGB images was developed for orchards.
Reproduction assets foundThe paper is a data descriptor for the ERWIAM dataset of annotated RGB images of fire blight symptoms, publicly deposited on Mendeley Data with a direct URL and DOI given in the text.Dataset · publicTolerance located in Quedlinburg (Germany) [51°46ʹ22″N 11°08ʹ41″E] and at the experimental orchard of the JKI for Breeding Research on Fruit Crops located in Dresden-Pillnitz (Germany) [51°00ʹ01″N 13°53ʹ12"E].
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/fpmnncmg84.1
Direct URL to data: https://data.mendeley.com/datasets/fpmnncmg84/1
1
Value of the Data
•
In the experimental greenhouse of the JKI-Quedlinburg Institute, around 2000 different genotypes of apple breeding material were artificially inoculated with Erwinia amylovora in 2021 and 2022, which could be used to record fire blight symptoms. The JKI-Dossenheim Institute has a heterogeneous appleOpen asset ↗Mendeley Data · 10.17632/fpmnncmg84.1lines:42-82Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. Despite the proliferation of deep neural network architectures for segmentation and phenotyping of 3D plant models in the last decade, the amount of data, and diversity in terms of species and data acquisition modalities are far from sufficient for evaluation of such tools for their generalization ability. To contribute to closing this gap, we introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. PLANesT-3D introduces diversity to existing datasets by adding point clouds of two new species and providing 3D data acquired with the low-cost SfM/MVS technique as opposed to laser scanning or expensive setups. Point clouds reconstructed with SfM/MVS modality exhibit challenges such as missing data, variable density, and illumination variations. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. The advantages of SP-LSCnet over other deep learning methods are its modular structure and increased interpretability. Two existing deep neural network architectures, PointNet++ and RoseSegNet, were also tested on the point clouds of PLANesT-3D for semantic segmentation.
Why it matches plant phenotyping methods3D植物点群の注釈付きデータセットを構築し、植物器官のセマンティック・インスタンス分割手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractwe introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants.
Reproduction assets foundThe paper introduces PLANesT-3D, an annotated 3D plant point cloud dataset, and SP-LSCnet segmentation code, both explicitly stated as publicly available at the authors' Aperta record and GitHub repository.Dataset · publicThe PLANesT-3D dataset is publicly available at https://aperta.ulakbim.gov.tr/record/286354 and https://github.com/visionlab-ogu/PLANesT-3D/tree/main/dataOpen asset ↗aperta.ulakbim.gov.tr · 286354lines:83-145Dataset · publicThe 2D color images for all the 34 plants together with their estimated camera poses and parameters are also open to the public to provide input data for recent 3D reconstruction techniques 3 3
3
The data is available at https://github.com/visionlab-ogu/PLANesT-3D/tree/main/data .Open asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:494-505Code · publicThe code for SP-LSCnet is available at https://github.com/visionlab-ogu/PLANesT-3DOpen asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:146-154Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy ( F-score = 0.77) and branch angle calculation accuracy ( r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine ( F-score = 0.53) and density-based methods ( F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.
Why it matches plant phenotyping methodsLiDAR点群からダイズの枝を検出し枝角度などの形態形質を抽出するアルゴリズムを開発・検証しており、植物表現型取得手法が研究の中心である。
abstractwe developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data
Reproduction assets foundThe paper's Soybean3D point cloud dataset, source code, and software are explicitly stated as openly available on the authors' public GitHub repository, directly supporting the paper's soybean branch angle phenotyping analysis.Code · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractorlines:178-220Dataset · publicThe Soybean3D datasets, source code, software, and other supporting data are openly available on GitHub ( https://github.com/Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor ).Open asset ↗Jinlab-9AiPhenomics/SCAG_PlantAngleExtractor · Soybean3Dlines:291-296Code / 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 confirmedCrossref · Europe PMC · checked 15 Sept 2026
ABSTRACT Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro.
Why it matches plant phenotyping methods植物プロトプラストのクロマチン状態を高スループット画像とカスタム画像解析で定量し、100以上の特徴を抽出・ベンチマークしており、表現型取得法が研究の中心である。
abstractwe used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts.
Reproduction assets foundThe paper's chromatin feature datasets (Dryad doi:10.5061/dryad.pnvx0k6wp) and images (BioStudies S-BIAD1157) are paper-specific public assets, but their repository URLs are not among the allowed URLs, so they cannot be listed. The authors' adapted entropy analysis script is publicly available on the authors' GitHub (aCode · publicinterval (a new observation from the same group will fall inside the ellipse with probability P= 0.95).
Entropy analysis
The initial script for computing Shannon Entropy is described in Dussiau et al. (2022) and is available at https://osf.io/9mcwg/ . The adapted script for computing entropy of chromatin features is provided at https://github.com/barouxlab/ChromatinEntropy . When all cells (segmented nuclei) express the same value for a given feature, this entropy of the feature will be null. The more cell-to-cell variability for a given chromatin feature, the higher value of entropy.
Plots and statistical tests
Box plots, violin plots, scatter plots, 2D contours and histograms were created Open asset ↗barouxlab/ChromatinEntropylines:121-147Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Published11 Jun 2024The Plant journal : for cell and molecular biologyCited by 9 · OpenAlex ↗
Stem is important for assimilating transport and plant strength; however, less is known about the genetic basis of its structural characteristics. In this study, a high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles, which allows the establishment of a stem cross-section phenotype dataset containing anatomical information of 1738 images from hand-cut transections of stems collected from 387 rice germplasm accessions grown over two successive seasons. Then, the phenotypic diversity of the rice accessions was evaluated. Genome-wide association studies identified 94, 83, and 66 significant single nucleotide polymorphisms (SNPs) for the assayed traits in 2 years and their best linear unbiased estimates, respectively. These SNPs can be integrated into 29 quantitative trait loci (QTL), and 11 of them were common in 2 years, while correlated traits shared 19. In addition, 173 candidate genes were identified, and six located at significant SNPs were repeatedly detected and annotated with a potential function in stem development. By using three introgression lines (chromosome segment substitution lines), four of the 29 QTLs were validated. LOC_Os01g70200, located on the QTL uq1.4, is detected for the area of small vascular bundles (SVB) and the rate of large vascular bundles number to SVB number. Besides, the CRISPR/Cas9 editing approach has elucidated the function of the candidate gene LOC_Os06g46340 in stem development. In conclusion, the results present a time- and cost-effective method that provides convenience for extracting rice stem anatomical traits and the candidate genes/QTL, which would help improve rice.
Why it matches plant phenotyping methodsイネ茎断面画像から解剖学的形質を抽出する高スループット手法を開発し、形質データセット構築と検証に用いており、表現型取得法が研究の中心である。
abstracta high-throughput method, "LabelmeP rice" was developed to generate 14 traits related to stem regions and vascular bundles
Reproduction assets foundThe paper's phenotyping/analysis Python code (LabelmeP rice pipeline) is explicitly provided as authors' supporting information Data S1, publicly available with the online article. Other referenced URLs (SNP-Seek, Q-TARO, RiceRNA, rmbreeding) are external databases, not paper-specific assets.Code · publicThe Python code was provided
as Data S1.Open asset ↗pdf-raw-page:10 lines:1-91Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.
Why it matches plant phenotyping methods小麦の葉・穂・茎・根の病害や損傷を画像から認識するAIモバイルアプリの開発・性能評価が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。害虫・雑草識別も含むが、病害認識の技術的評価が明示されている。
abstractThis paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region.
Reproduction assets foundThe article's data availability statement explicitly states that the authors' weed phenotyping image dataset is publicly available on Zenodo (DOI 10.5281/zenodo.7598372), a paper-specific public asset. No author analysis code or trained model checkpoints are described with a public URL.Dataset · publicThe original contributions presented in the study are publicly available (see the weed phenotyping image dataset). This data can be found here: https://doi.org/10.5281/zenodo.7598372 .Open asset ↗Zenodo · 10.5281/zenodo.7598372lines:460-508Code / dataset availability confirmedOpenAlex · checked 7 Sept 2026
Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
Why it matches plant phenotyping methodsマルチスペクトル画像とVision Transformerを用いて、トウモロコシ個体の干ばつストレス状態を推定する解析パイプラインを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform.
Reproduction assets foundThe paper's raw maize drought-stress imaging dataset (three trials, downsampled NGB/NIR images) is openly deposited on Zenodo with DOI 10.5281/zenodo.10991581. No author analysis code, trained models, or annotations are stated as publicly available.Dataset · publicData Availability Statement: The original data presented in the study are openly available on
the data sharing platform Zenodo https://zenodo.org/records/10991581 ( accessed on 18 April
2024) with DOI 10.5281/zenodo.10991581. The repository contains raw images before any of the
pre-processing steps mentioned in Section 3.Open asset ↗Zenodo · 10.5281/zenodo.10991581pdf-page:12 lines:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Key message Analyzing fine branch length characteristics in beech trees using single-tree QSMs derived from laser scanning reveals insights into drought-induced changes in vitality, which include branch shedding and reduced shoot growth. Abstract Climate change causes increasing temperatures and precipitation anomalies, which result in deteriorations of tree health and declines in ecosystem services of forests. It is therefore crucial to monitor tree vitality to preserve forests and their functions. However, methods describing tree vitality in situ are lacking reproducibility or are too laborious. Thus, we tested a laser-scanning based approach, assuming that an objective measurement of a tree’s outer shape should reveal changes according to tree vitality. QSMs of similarly sized beech trees from stands with varying degrees of drought damage were used. Absolute and relative fine branch lengths, their ratio to lower order branches’ lengths and their progressions over relative height were targeted to identify fine branch dieback and reduced growth. The absolute fine branch length was significantly lower for less vital beech trees, especially within the upper crown, leading to a less top-heavy vertical distribution of fine branches and a reduced fine-to-base order branch length ratio. Hence, height-dependent characteristics of fine branch lengths differed between vitalities. We conclude that using fine branch length characteristics derived from QSMs can be helpful in vitality assessments of beech trees. Still, uncertainties with regard to the plotwise assessment and problems with QSM quality are present.
Why it matches plant phenotyping methods3DレーザースキャンとQSMから枝長形質を抽出し、樹木活力を客観評価する手法が研究の中心であるため。
abstractThus, we tested a laser-scanning based approach, assuming that an objective measurement of a tree’s outer shape should reveal changes according to tree vitality.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analyzed (QSM-derived fine branch length measurements of beech trees) in the GRO.data repository with a public DOI, making it a paper-specific, publicly actionable phenotype dataset.Dataset · publicand the
Federal Ministry for the Environment, Nature Conservation, Nuclear
Safety and Consumer Protection (BMUV) through the Fachagen-
tur Nachwachsende Rohstoffe e. V. (FNR) (Reference Number
2220WK10C1).
Data availability The datasets generated and analyzed during the cur-
rent study are available in the GRO.data repository, https://doi.org/10.25625/ZCPNBN
Declarations
Conflict of interest The authors have no relevant financial or non-fi-
nancial interests to disclose.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as youOpen asset ↗GRO.data · 10.25625/ZCPNBNpdf-raw-page:12 lines:1-80Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branch2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots exhibit intricate geometries at an exceptionally detailed spatial scale. Describing the complete structure of a conifer shoot, which contributes to a radiation scattering pattern, has been difficult, and the previous respective components of radiative transfer models for conifer stands were rather coarse. This paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots. The data collection was conducted in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The dataset includes 3-dimensional surface information on 10 shoots of two conifer species present in the stand (5 shoots per species) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 26th July 2022, and subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. For each of these samples, the dataset comprises of a photo of the sampled shoot and its obtained 3-dimensional surface reconstruction. Scanned shoots may replace previous, artificially generated models and contribute to the more realistic representation of 3D forest representations and, consequently, more accurate estimates of related parameters and processes by radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3次元形状を高解像度スキャンで取得した再利用可能なデータセットであり、植物形態の計測・表現が中心。
abstractThis paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots.
Reproduction assets foundThe paper describes a public Mendeley Data repository containing the paper's own 3D surface geometry (.stl) models and photos (.jpg) of 10 scanned conifer shoots, directly reproducing the paper's phenotyping measurements.Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/rs3f6trdvw.1
Direct URL to data: https://data.mendeley.com/datasets/rs3f6trdvw/1Open asset ↗Mendeley · 10.17632/rs3f6trdvw.1lines:1-53Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential, forming the foundation for precision apple management. Traditionally, 2D imaging technologies were employed to delineate the architectural traits of apple trees, but their accuracy was hampered by occlusion and perspective ambiguities. This study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization, utilizing point clouds collected by a terrestrial laser scanner (TLS). The pipeline consisted of four modules: (a) data preprocessing module, (b) tree instance segmentation module, (c) tree structure segmentation module, and (d) architectural trait extraction module. The developed pipeline was used to analyze 84 trees of two representative apple cultivars, characterizing architectural traits such as tree height, trunk diameter, branch count, branch diameter, and branch angle. Experimental results indicated that the established pipeline attained an R 2 of 0.92 and 0.83, and a mean absolute error (MAE) of 6.1 cm and 4.71 mm for tree height and trunk diameter at the tree level, respectively. Additionally, at the branch level, it achieved an R 2 of 0.77 and 0.69, and a MAE of 6.86 mm and 7.48° for branch diameter and angle, respectively. The accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding programs. Moreover, bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.
Why it matches plant phenotyping methodsTLS点群を用いて樹体構造を分割し、樹高・幹径・枝数・枝径・枝角度を抽出する3D表現型計測パイプラインの開発と精度評価が中心である。
abstractThis study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization, utilizing point clouds collected by a terrestrial laser scanner (TLS).
Reproduction assets foundThe paper's AppleQSM pipeline source code is explicitly stated as publicly available on the authors' GitHub repository. Raw TLS point cloud data are only available upon reasonable request, so they do not qualify as a public asset. TreeQSM and FLIP_main repositories are cited prior work/tools, not paper-specific assets.Code · publicThe source code is available at the project GitHub repository ( https://github.com/suptimq/Apple_Crop_Potential_Prediction/tree/master ). Raw data used in this study will be shared upon reasonable request.Open asset ↗https://github.com/suptimq/Apple_Crop_Potential_Prediction/tree/masterlines:293-327Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error ( MAPE ) of 3.01%, a Mean Absolute Error ( MAE ) of 0.75 mm, a Root Mean Square Error ( RMSE ) of 1.07 mm, and a coefficient of determination ( R ²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
Why it matches plant phenotyping methodsRGB-Dカメラと画像・3D処理によりトウモロコシ茎径を非破壊測定する手法を開発し、誤差指標で精度検証しており、フェノタイピング手法が中心である。
abstractthis study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter
Reproduction assets foundThe paper's data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.25450039) containing the study's datasets (RGB/depth imagery and stem diameter measurements used for the maize stem diameter phenotyping analysis). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://dx.doi.org/10.6084/m9.figshare.25450039 .Open asset ↗figshare · 10.6084/m9.figshare.25450039lines:909-917Code / 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 confirmedOpenAlex · checked 15 Sept 2026
Trunk diameter is related to the overall health and level of carbon sequestration in a tree. Trunk diameter measurement, therefore, is a key task in both forest plot and urban settings. Unlike the traditional approach of manual measurement with a measuring tape or calipers, several recent approaches rely on sophisticated technologies such as Terrestrial Laser Scanning (TLS), LiDAR, and time-of-flight sensors that provide fine-grain depth maps, which are used for depth-assisted image segmentation in downstream processing. These technologies are supported only on specialized devices or high-end smartphones. We present a mobile application that uses coarse-grain depth maps derived from an optical sensor, and so can be run on most common Android devices. Moreover, we use a state-of-the-art deep neural network to estimate trunk diameter from an image and its corresponding coarse depth map (RGB-D). We tested our app using a dataset collected from four countries and under challenging conditions including occlusion, leaning trees, and irregular shapes and found that our algorithm has a MAE of 2.58 cm and an RMSE of 3.57 cm, which is comparable to accuracy from fine-grain depth maps. Moreover, diameter measurement using our app is more than 5 times faster than traditional manual surveying.
Why it matches plant phenotyping methodsRGB-D画像と粗い深度マップから樹幹直径を推定するモバイル手法を開発し、複数国のデータセットと困難条件で精度検証しており、植物形質取得が中心である。
abstractWe present a mobile application that uses coarse-grain depth maps derived from an optical sensor
Reproduction assets foundThe paper's DBH estimation evaluation dataset (154 RGB + depth tree images with metadata and ground-truth DBH) is publicly deposited on Zenodo, and the app/algorithm source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicing the quality of the DBH estimate, includ-
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ing non-cylindrical trunks, burl presence, degrees of leaning and occlusion, and poor lighting, as illustrated in Fig. 4. Sample images
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from the dataset are available in Supplement 10.9, and the complete set of RGB and depth images, along with metadata, is acces-
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sible at https://zenodo.org/records/10199711.200
In Thailand, we collected data in Bangkok’s Lumphini Park and Chulalongkorn Centenary Park. As a tropical location, Bangkok is
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home to many tropical trees, such as rain trees (Samanea saman), banyan trees, palm trees, and coconut trees52. At Lumphini Park,
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where most of our data came from, small forests grow next to watOpen asset ↗zenodo.org · 10199711pdf-raw-page:8 lines:1-31Code · public; Z.F. analyzed the data and led the writing of the manuscript.
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A.H. and S.K. reviewed the manuscript and provided constructive suggestions. All authors contributed critically to the drafts and
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gave final approval for publication.
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8 DATA AVAILABILITY
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The algorithm and app code are publicly available on GitHub at https://github.com/MingyueX/GreenLens, with APK available from
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APKPure at https://apkpure.com/p/com.cleeg.greenlens. All the data for the app evaluation can be accessed at https://zenodo.org/357
records/10199711.
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9 AUTHOR COMPETING INTERESTS
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The authors declare no conflict of interest.
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REFERENCES
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[1] Kenneth G MacDicken. Global forest resourOpen asset ↗github.com/MingyueX/GreenLenspdf-raw-page:15 lines:1-92Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Background In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO 2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO 2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.
Why it matches plant phenotyping methods低コスト2D画像フェノタイピングを用いて、シュート構造やキャノピー被覆を定量化し、圃場での植物形態評価法としての有用性を扱っているため、フェノタイピング手法が実質的に中心である。
abstractIn addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Material 4: Table S3: (download XLSX )
Reliability estimates, BLUPs calculated for the traits measured in this study and data associated with each trait measured in the study.Open asset ↗lines:429-492Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta , which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non- living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and fastinitiation in morning than in evening. These observations suggest that Cuscuta , despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.
Why it matches plant phenotyping methodsCuscutaのコイリングや運動動態を画像から自動抽出するPythonパイプラインの開発・信頼性検証が研究の中心であり、植物状態・形態動態のフェノタイピング手法に該当する。
abstractSubsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between.
Reproduction assets foundThe paper's data availability statement points to two public, paper-specific assets: the authors' Python/Jupyter notebook phenotyping pipeline on GitHub and the time-lapse video datasets used in the study hosted as a YouTube playlist. Both are directly tied to this paper's Cuscuta coiling phenotyping measurements and分析Dataset · publicNT
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All authors declare that they have no conflicts of interest.
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DATA AVAILABILITY STATEMENT
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The developed Python-based pipeline is available as a collection of Jupyter notebooks at
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https://github.com/ejamezquita/cuscuta/ . The datasets used and/or analyzed during the current
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study are available here:
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https://youtube.com/playlist?list=PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxru&feature=shared
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ORCID
411
Max Bentelspacher https://orcid.org/0009-0004-7357-917X
412
Erik J. Amézquita https://orcid.org/0000-0002-9837-0397
413
Supral Adhikari https://orcid.org/0000-0002-9914-2986
414
Jaime Barros https://orcid.org/0000-0002-9545-312X
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So-Yon Park https://orcid.org/0000-Open asset ↗PLZkYcVyQr2u4tT0yoZAkrMqzQxRIDvxrupdf-raw-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
MaizeSunflowerField / plotLaboratory / benchtopStem / branchPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
There is currently a need for inexpensive, continuous, non-destructive water potential measurements at high temporal resolution ( Helianthus annuus ) (petioles and stems) and a monocotyledon ( Zea mays ) species (stems) for 1 week during dehydration and re-watering treatments under laboratory conditions. We also demonstrated the ability of the device to record branch and trunk diameter variation of a woody dicotyledon ( Rhus typhina ) in the field. Under laboratory conditions, we compared our device (hereafter 'contact' dendrometer) with modified versions of another open-source dendrometer (the 'optical' dendrometer). Overall, contact and optical dendrometers were well aligned with one another, with Pearson correlation coefficients ranging from 0.77 to 0.97. Both dendrometer devices were well aligned with direct measurements of xylem water potential, with calibration curves exhibiting significant non-linearity, especially at water potentials near the point of incipient plasmolysis, with pseudo R 2 values (Efron) ranging from 0.89 to 0.99. Overall, both dendrometers were comparable and provided sufficient resolution to detect subtle differences in stem water potential (ca. 50 kPa) resulting from light-induced changes in transpiration, vapour pressure deficit and drying/wetting soils. All hardware designs, alternative configurations, software and build instructions for the contact dendrometers are provided.
Why it matches plant phenotyping methods安価なオープンソース樹幹径計を開発し、水ポテンシャルと茎径成長を高時間・空間分解能で測定する手法を比較検証しており、植物表現型取得が研究の中心である。
titleDevelopment and application of an inexpensive open-source dendrometer for detecting xylem water potential and radial stem growth at high spatial and temporal resolution.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all sensor software, 3D prints, photos, and schematics in a public GitHub repository, and the dendrometer measurement data are provided as a CSV in the supplementary information (no allowed URL for the SI itself). The GitHub repository is a paper-specific,公开,可Code · publicins, CO 80526, USA.
Data Availability
All data used in this study are available for download from the supplemental information as a CSV file (sup_data_all_dendro.csv). All software needed for operating sensors, 3D prints, photos, and schematics have been included in the SI materials, and can also be freely accessed via github ( https://github.com/sean-gl/dendrometer_water_potential_device ).
Sources of Funding
J.J.S. was supported by an NSF Postdoctoral Research Fellowship in Biology, Grant No. IOS-1907338.
Contributions by the Authors
All authors contributed meaningfully to the manuscript. S.M.G., J.J.S., B.A., S.K.P. and J.M. designed the experiment and collected the data. S.M.G. wrote theOpen asset ↗https://github.com/sean-gl/dendrometer_water_potential_devicelines:224-283Code / dataset availability 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit.•Efficient methods to measure biomechanical traits and record metadata related to stalk lodging.•Can be used in studies with large sample sizes (i.e., > 1,000).
Why it matches plant phenotyping methodsトウモロコシの倒伏抵抗性に関わる生体力学的形質を高スループットに測定する、実験室・圃場対応の表現型解析パイプラインを開発・実装した研究であり、方法が中心的である。
abstractA phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented.
Reproduction assets foundThe paper's internode-length phenotyping workflow (YOLOv5m node detection, LabelImg verification, custom R scripts) is publicly available as the authors' InterMeas repository on GitHub, including scripts, example images, and a tutorial. Other assets (Instron methods file, DARLING data) are only supplementary or on-dataCode · publicodal annotations across individual stalks, convert pixel distances to physical distances using the known dimensions of the imaging background, and output internodal lengths as a single excel file of stalk/internode identifiers and lengths. All scripts, example images, and a tutorial on setup and usage are available on Github at https://github.com/nbo245/InterMeas and a Shiny dashboard can be run locally to implement the internode measurement workflow within an interactive GUI environment.
Measurements of minor diameter, rind thickness, rind penetration resistance and integrated puncture scoreOpen asset ↗nbo245/InterMeaslines:85-89Code / 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 7 Sept 2026
Limited access to low-cost tools to measure, report, and verify (MRV) tree growth with smallholder farmers limits the scaling of tree planting efforts in developing countries. Artificial Intelligence (AI) offers the potential for low-cost, reliable, and accessible measurement and verification tools to be developed for an MRV platform to scale tree planting efforts in developing countries. Here, we present an AI-powered non-contact tree diameter measurement and verification tool. We have developed an AI-powered algorithm that accurately estimates the diameter of a tree from an image of the tree with a reference object. This non-contact measurement method utilizes semantic segmentation and image processing techniques to analyze an image of the tree with the reference object. The performance of the proposed method was evaluated on 142 trees with tape-measured diameters at breast height ranging from 5 to 60 cm. A regression analysis between predicted and measured diameter values had an R 2 and an RMSE of 0.97 and 2.23 cm, respectively. Thus, using a smartphone application, the non-contact method developed here can empower anyone to accurately measure and report tree growth by just taking pictures of the trees with the reference object. The images submitted with on-farm measurements serve as data for future verification operations using the AI-powered algorithm. With the reference object serving as a unique tree identifier, a tree’s survival and diameter measurements can be tracked over time. The MRV system described here, with the developed AI-powered non-contact tree diameter measurement and verification tool, can empower organizations to plant, grow, and monitor trees with anyone, including smallholder farmers.
Why it matches plant phenotyping methods画像から樹木の胸高直径という植物形質を推定するAI・セグメンテーション手法を開発し、実測値との性能検証も行っており、植物フェノタイピング手法が中心である。
abstractHere, we present an AI-powered non-contact tree diameter measurement and verification tool.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides three public, paper-specific assets: the tree image dataset used to calibrate/evaluate the diameter algorithm (ScholarSphere), the statistical analysis data/code repository (GitHub), and the containerized diameter estimation tool (Docker Hub). PixelAnnotationTDataset · publicThe image dataset that was used to calibrate and evaluate the algorithm can be found on
the ScholarShpere repository of the Pennsylvania State UniversityOpen asset ↗pdf-page:15 lines:1-22Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Rapid and precise tree Diameter at Breast Height (DBH) measurement is pivotal in forest inventories. While the recent advancements in LiDAR and Structure from Motion (SFM) technologies have paved the way for automated DBH measurements, the significant equipment costs and the complexity of operational procedures continue to constrain the ubiquitous adoption of these technologies for real-time DBH assessments. In this research, we introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology. Firstly, we improved the YOLOv5-seg segmentation module with a Channel and Spatial Attention (CBAM) module, augmenting its efficiency in extracting the tree’s edge features in intricate forest scenarios. Subsequently, we devised an image processing algorithm for real-time key point localization and DBH measurement, leveraging historical data to fine-tune current frame assessments. This system facilitates real-time image data upload via wireless LAN for immediate host computer processing. We validated our approach on seven sample plots, achieving bbAP50 and segAP50 scores of: 90.0%(+3.0%), 90.9%(+0.9%), respectively with the improved YOLOv5-seg model. The method exhibited a DBH estimation RMSE of 17.61∼54.96 mm (R2=0.937), and secured 78% valid DBH samples at a 59 FPS. Our system stands as a cost-effective, portable, and user-friendly alternative to conventional forest survey techniques, maintaining accuracy in real-time measurements compared to SFM- and LiDAR-based algorithms. The integration of WLAN and its inherent scalability facilitates deployment on Unmanned Ground Vehicles (UGVs) to improve the efficiency of forest inventory. We have shared the algorithms and datasets on Github for peer evaluations.
Why it matches plant phenotyping methodsRGB-D画像とアルゴリズムを用いて樹木のDBHという明示的な形態形質をリアルタイム推定し、精度検証とシステム実装を行った研究であり、植物フェノタイピング手法が中心である。
abstractwe introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology.
Reproduction assets foundThe authors explicitly state that the code used in this DBH measurement research is publicly available on GitHub (KAN-Forest repository), making it a paper-specific, public, actionable code asset. The phenotype/trait datasets (DBH measurements and forest images) are only available upon request from the corresponding作者,Code · publicwe have made
the code used in this research available on GitHub at: https://github.com/CharmingZh/KAN-ForestOpen asset ↗CharmingZh/KAN-Forestpdf-page:27 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
We present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance. We examined changes in bidirectional red light reflectance during irradiation with blue light, known to trigger chloroplast relocations. Experiments on the model plant Arabidopsis thaliana , wild-type, and several mutants with disrupted chloroplast movements showed that the chloroplast avoidance response, induced by high blue light, led to a substantial increase in diffuse reflectance of unpolarized red light. The effects of the accumulation response in low blue light were the opposite. The specular reflectance of the leaf was unaffected by the chloroplast positioning. To further improve the specificity of the detection, we examined the effects of chloroplast relocations on the leaf reflectance of a linearly polarized incident beam. The greatest relative change associated with chloroplast movements was observed when the planes of polarization of the incident and detected beams were perpendicular. Further experiments revealed that the chloroplast positioning affected the magnitude of depolarization of light by the leaf. We applied the developed approach to examine chloroplast relocations in four angiosperm species collected in the field. The method allowed us to detect the chloroplast avoidance response in the green stems of bilberry, a sample not amenable to transmittance-based detection. Despite the importance of chloroplast movements for the optimization of photosynthetic efficiency and biomass production, high throughput reflectance-based methods are not routinely used for their detection. This method opens the possibility of non-invasive, non-contact detection of chloroplast relocations in a manner insensitive to the orientation of the leaf.
Why it matches plant phenotyping methods葉のクロロプラスト移動という植物状態を、偏光反射によって非接触・非侵襲的に検出する手法を開発し、複数種で適用しているため、植物フェノタイピング手法が研究の中心である。
abstractWe present a sensitive method for non-contact detection of chloroplast movements in leaves and other photosynthetic tissues, based on changes in the magnitude of cross-polarized reflectance.
Reproduction assets foundThe paper's Data availability statement openly deposits the paper's own reflectance/transmittance phenotype recordings (Arabidopsis WT/mutants and wild plants) on FigShare, and provides authors' public code: BeamJ (Java control software for the phenotyping setup) and openRayTracer (Mathematica ray-tracing package used,Dataset · publiced on the manuscript.
Conflict of interest
The authors declare no conflict of interest.
Funding
This study was supported by the National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at httOpen asset ↗FigShare · 10.6084/m9.figshare.21082654pdf-raw-page:14 lines:1-47Dataset · publicthe National Science Centre Poland within the MINIATURA 4
project to P.H., number 2020/04/X/NZ4/01256.
Data availability
The data that support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś,Open asset ↗FigShare · 10.6084/m9.figshare.24424843pdf-raw-page:14 lines:1-47Code · publicthat support the findings of this study are openly available in FigShare at
https://doi.org/10.6084/m9.figshare.21082654 (reflectance and transmittance recordings for
Arabidopsis WT and mutants) and https://doi.org/10.6084/m9.figshare.24424843 (wild
plants). Java source code for the software is publicly available via GitHub at
https://github.com/pawelHerm/beamJ/tree/master/BeamJ. Wolfram Mathematica package for
ray tracing is available at https://github.com/plantPhotobiologyLab/openRayTracer.References
Banaś, A. K., Aggarwal, C., Łabuz, J., Sztatelman, O., Gabryś, H. (2012). Blue light
signalling in chloroplast movements. Journal of Experimental Botany, 63(4), 1559–
1574.
Baránková, B., LazáOpen asset ↗GitHub · pawelHerm/beamJpdf-raw-page:14 lines:1-47Code · publicuorescence. The filtered light was focused on a
photodetector (amplified silicon photodiode, PDA100A2, Thorlabs) with a plano-convex lens
(LA1074-A, Thorlabs). The angular size of the clear aperture of the collecting lens with
respect to the sample center was 0.019 steradian (calculated using our ray-tracing
Mathematica package https://github.com/plantPhotobiologyLab/openRayTracer). To control
the observation angle, the detector was mounted at the RBB300A/M rotation board
(Thorlabs). The experiments were performed with two angular positions of the polarizer: its
transmission axis was either parallel (transmits P) or perpendicular (transmits S component)
to the plane of incidence. The LEDs suOpen asset ↗GitHub · plantPhotobiologyLab/openRayTracerpdf-raw-page:6 lines:1-45Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (¿7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.
Why it matches plant phenotyping methodsTLSによる個体樹木の体積という植物形質の推定手法を、破壊的実測データと比較検証しており、測定・推定法が研究の中心である。
abstractTerrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees
Reproduction assets foundThe paper's Data availability statement points to a public EnviDat deposit (DOI 10.16904/envidat.403) containing the data associated with this TLS-based tree volume estimation study, including the TLS point clouds and destructive reference measurements collected in the SwissBiomass project. No author analysis code URL,Dataset · publicvolume by integrating them into new allometric models.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data associated with this article is available at http://dx.doi.org/10.16904/envidat.403.Acknowledgements
The authors would like to thank everyone involved in the WSL
project ‘‘SwissBiomass’’, in the course of which the data used in this
study were collected. In particular, we thank Marina Beck for project
coordination, as well as all those who helped with the field and lab-
oratory measurements. We also thank DanielOpen asset ↗envidat · 10.16904/envidat.403pdf-raw-page:11 lines:1-93Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate detection and reconstruction of branches aid the accuracy of harvesting robots and extraction of plant phenotypic information. However, the complex orchard background and twisting growing branches of vine fruit trees make this challenging. To solve these problems, this study adopted a Mask Region-based convolutional neural network (Mask R-CNN) architecture incorporating deformable convolution to segment branches in complex backgrounds. Based on the growth posture, a branch reconstruction algorithm with bidirectional sector search was proposed to adaptively reconstruct the segmented branches obtained by an improved model. The average precision, average recall, and F1 scores of the improved Mask R-CNN model for passion fruit branch detection were found to be 64.30%, 76.51%, and 69.88%, respectively, and the average running time on the test dataset was 0.75 s per image, which is better than the compared model. We randomly selected 40 images from the test dataset to evaluate the branch reconstruction. The branch reconstruction accuracy, average error, average relative error of reconstructed diameter, and mean intersection-over-union (mIoU) were 88.83%, 1.98 px, 7.98, and 83.44%, respectively. The average reconstruction time for a single image was 0.38 s. This would promise the proposed method to detect and reconstruct plant branches under complex orchard backgrounds.
Why it matches plant phenotyping methodsCNNによる枝の検出・再構成と直径推定を中心に、植物形態情報の抽出手法を開発・評価しているため、ロボット収穫支援に加えて再利用可能な表現型計測法に該当する。
abstractAccurate detection and reconstruction of branches aid the accuracy of harvesting robots and extraction of plant phenotypic information.
Reproduction assets foundThe paper's Data Availability statement explicitly provides all training/test data (passion fruit branch images and annotations) freely via the authors' public GitHub repository, which matches an allowed URL.Dataset · public. M.C. revised this manuscript with constructive discussions. S.L. suggested amendments to the manuscript and supervised the project. Competing interests: The authors declare that they have no competing interests.
Data Availability
All data used to train and test the model presented in this study could be downloaded freely from https://github.com/abyssbjc/Reconstruction-of-passion-fruit-tree-branches .
References
1. Lin G, Tang Y, Zou X, Wang C.
Three-dimensional reconstruction of guava fruits and branches using instance segmentation and geometry analysis. Comput Electron Agric. 2021;184:
Article 106107.
2. Zhao Y, Gong L, Huang Y, Liu C.
A review of key techniques of vision-based control foOpen asset ↗abyssbjc/Reconstruction-of-passion-fruit-tree-brancheslines:288-343Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionVisualization / data managementPlant / canopy height
Introduction Rubber trees are an important cash crop in Hainan Province; thus, monitoring sample plots of these trees provides important data for determining growth conditions. However, existing monitoring technology and rubber forest sample plot analysis methods are relatively simple and present widespread issues, such as limited monitoring equipment, transportation difficulties, and relatively poor three-dimensional visualization effects in complex environments. These limitations have complicated the development of rubber forest sample plot monitoring. Method This study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of sample plot information were performed in the study area for trees to explore the practical significance of this method for monitoring rubber forest sample plots. Furthermore, the relationship between the height of the first branch, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree standard volume model was established. Results The Bias, relative Bias, RMSE, and RRMSE of the height of the first branch measured by this method were −0.018 m, −0.371%, 0.562 m, and 11.573%, respectively. The Bias, relative Bias, RMSE, and RRMSE of DBH were −0.484 cm, −1.943%, −2.454 cm, and 9.859%, respectively, which proved that the method had high monitoring accuracy and met the monitoring requirements of rubber forest sample plots. The fitting results of rubber tree standard volume model had an R2 value of 0.541, and the estimated values of each parameter were 1.745, 0.115, and 0.714. The standard volume model accurately estimated the volume of rubber trees and forests using the first branch height and DBH. Discussion This study proposed an innovative planning scheme for a terrestrial photogrammetry system for 3D visual monitoring of rubber tree forests, thus providing a novel solution to issues observed in current sample plot monitoring practices. In the future, the application of terrestrial photogrammetry systems to monitor other types of forests will be explored.
Why it matches plant phenotyping methods地上 photogrammetry と3D点群再構成を用いて樹高関連形質、DBH、樹木体積を取得・検証する方法を開発し、精度評価も行っており、植物形質計測が中心である。
abstractThis study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data.
Reproduction assets foundThe paper's data availability statement deposits the study's dataset (3D visual sustainable management of rubber forest based on terrestrial photogrammetry system) on Figshare with a public DOI, making the paper-specific phenotyping data (DBH, first branch height, point-cloud measurements) publicly available.Dataset · publics in the future. Such monitoring is important for the sustainable development of tropical agriculture and forestry in Hainan Province.
Statements
Data availability statement
The datasets [3D Visual Sustainable Management of Rubber Forest Based on Terrestrial Photogrammetry System] for this study can be found in the [FIGSHARE] [ https://doi.org/10.6084/m9.figshare.22133126 ].
Author contributions
ZQ and SL contributed to the conception and design of the study and wrote the first draft of the manuscript. SL, LL, YX, CW, NL, RL, and DY organized the database and performed the statistical analysis. LL, YX, CW, NL, RL, and DY wrote the sections of the manuscript. All authors contributed to the maOpen asset ↗FIGSHARE · 10.6084/m9.figshare.22133126lines:623-661Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.
Why it matches plant phenotyping methods植物の画像から種子・シュート・根を認識し、形態形質を自動抽出して統計評価するオープンソースツールの開発・精度検証が中心である。
abstractIn this study, we developed Morley, a free, open-source graphical user interface written in Python.
Reproduction assets foundThe paper's authors publicly released the Morley analysis code (GitHub repo dashabezik/Morley) and example data/user guide (dashabezik/plants), both explicitly stated in the Data Availability Statement and Methods. These directly support the paper's seedling image analysis and morphometric measurements.Code · publicths and plant surface areas, and figures characterizing
distributions of measured parameters, bar plots with mean values and standard deviations (95% CI),
and heatmaps visualizing the conclusions on statistical significance of the morphometric differences.
Code, graphical user interface, user guide and examples are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/, respectively.
Morley is available as a graphical user interface and a command line tool.
3. Results
3.1. Comparison of Morley with ImageJ and Manual Measurements Demonstrates Agreement between
Results
ImageJ [23] is widely applied for image analysis of plants and seedlings [24–28] andOpen asset ↗dashabezik/Morleypdf-layout-page:6 lines:1-47Dataset · publicon,
IAT; funding acquisition, IAT. All authors have read and agreed to the published version of the manuscript.
Funding: The study was supported by Russian Science Foundation, grant #22‐26‐00109.
Data Availability Statement: Program code, GUI, user guide and example data are available at
https://github.com/dashabezik/Morley and https://github.com/dashabezik/plants/.
Acknowledgments: The authors thank Dr. Olga M. Zhigalina and Dr. Dmitri N. Khmelenin (Shubnikov Institute
of Crystallography, FSRC “Crystallography and Photonics”, RAS) for collecting high‐quality TEM images of
iron nanoparticles and Dr. Nadezhda G. Berezkina (N.N. Semenov Federal Research Center for Chemical
Physics, RAS) forOpen asset ↗dashabezik/plantspdf-layout-page:13 lines:1-65Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Uprooting caused by flood events is a significant disturbance factor that affects the establishment, growth, and mortality of riparian vegetation. If the hydraulic drag force acting on riparian plants exceeds the peak uprooting force originate from their below-ground portion, it may result in the uprooting of these plants. Despite previous studies have documented and investigated the uprooting processes and factors influencing the peak uprooting force of plants, most of these studies have focused on how the root morphological traits of tree and shrub seedlings affect peak uprooting force or mainly collected data in indoor experiments, which may limit the extrapolation of the results to natural environments. To address these limitations, we assume that the peak uprooting force can be estimated by the morphological traits of the above-ground portion of the vegetation. In this study, we conducted in-situ vertical uprooting tests on three locally dominant species: Conyza canadensis , Daucus carota , and Leonurus sibiricus , in a typical riverine environment. The three species were found to have the highest abundance based on the outcomes of the quadrat method. We measured the peak uprooting force, plant height, stem basal diameter, shoot and root wet biomass, and shoot and root dry biomass of each plant and compared them between species. Furthermore, we quantified the influence of morphology on peak uprooting force. Our results showed significant differences in morphological traits and peak uprooting force among the three species. We found a significant positive correlation between peak uprooting force and the morphological traits of the three species. The peak uprooting force increases with plant size following a power law function which is analogous to allometric equations. The allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants, which may help to overcome the limitations of measuring root morphological traits.
Why it matches plant phenotyping methods植物の地上部形態から地下部に由来する最大引抜抵抗力を推定する非破壊的なアロメトリック手法を開発・提示しており、形質取得・推定が研究の中心である。
abstractThe allometric equation provided a convenient and non-destructive method to estimate the peak uprooting force based on the above-ground morphological traits of the plants
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.6476708) containing the datasets from this study (in-situ uprooting tests and morphological trait measurements of three riparian species). This is a paper-specific, publicly accessible phenotype dataset. No author analysis代码或补Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accessionnumber(s) can be found below: https://doi.org/10.5281/zenodo.6476708 .Open asset ↗zenodo · 10.5281/zenodo.6476708lines:667-709Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.
Why it matches plant phenotyping methods大豆全生育期の3D再構成、器官ラベル付き点群データセットの構築と検証が中心で、植物表現型自動取得を支援する再利用可能な基盤である。
abstractThe study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition.
Reproduction assets foundThe paper's own 3D soybean phenotyping assets are publicly available: the original reconstructed 3D models and the annotated Soybean-MVS point cloud dataset are on Kaggle, and the authors' analysis code (BAAF-Net and RandLA-Net segmentation implementations used to train/test the dataset) is on GitHub with explicit dataDataset · publicNatural Science Foundation of Heilongjiang Province of
China (LH2021C021).
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Original models are available in a publicly accessible repository: The
original contributions presented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authorsOpen asset ↗soberguo/soybean-original-modelpdf-raw-page:15 lines:1-47Dataset · publicesented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Image collection quantity of soybean plants of different varieties in different stages.
V1 V2 V3 V4 V5 R1 R2 R3 R4 R5 R6 R7 R8
2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 20Open asset ↗soberguo/soybeanmvspdf-raw-page:15 lines:1-47Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis , formerly known as Mycosphaerella fijiensis . Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.
Why it matches plant phenotyping methodsバナナの健全・罹病状態を画像で記録した公開データセットが論文の中心であり、植物病害状態の画像ベース表現型解析に利用できる。
abstractThe dataset of curated banana crop image is presented in this article.
Reproduction assets foundThe paper's banana leaf/stem image dataset (16,092 images) is publicly deposited on Harvard Dataverse (doi:10.7910/DVN/LQUWXW), and annotation was done with the Makerere AI Lab public web annotation tool on GitHub. Both are paper-specific, public, and actionable.Dataset · publiccation
• Institution: The Nelson Mandela African Institution of Science and Technology (NM-AIST), The International Institute of Tropical Agriculture (IITA)
• City/Town/Region: Arusha
• Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/LQUWXW
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LQUWXW
Value of the Data
•
Machine learning models for early detection of Black Sigatoka and Fusarium Wilt Race 1 diseases that affect productivity can be trained using this dataset.
•
Researchers in the field of machine learning can use the collected imagery dataset of bananas to develop the endOpen asset ↗Harvard Dataverse · doi:10.7910/DVN/LQUWXWlines:1-53Code · publict and Remove Duplicate Pictures
2023 https://visipics.en.softonic.com (Accessed 10 February 2023)
3
Chen Q. Zobel J. Zhang X. Verspoor K.
Supervised learning for detection of duplicates in genomic sequence databases
PLoS ONE 11 2016 1 15
10.1371/journal.pone.0159644
PMC4973881
27489953
4
Makerere AI Lab
Web Annotation Tool
2023 https://github.com/AI-Lab-Makerere/web-annotation-tool (Accessed 5 March 2023)
5
Mduma N. Leo J. Loyani L. Jomanga K. Kamara A. Msaki I. Sanga S.
Banana Dataset Tanzania, Havard Dataverse
2022
10.7910/DVN/LQUWXW
Data Availability
Bananas Dataset Tanzania (Original data) (Dataverse).
Acknowledgments
The authors would like to extend their gratitude to Rockefeller FoundaOpen asset ↗github.com/AI-Lab-Makerere/web-annotation-toollines:89-126Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Field / plotStem / branchGrowth / time-series analysisGrowth / development / phenology
Traditional phenological models use chilling and thermal forcing (temperature sum or degree-days) to predict budbreak. Because of the heightening impact of climate and other related biotic or abiotic stressors, a model with greater biological support is needed to better predict budbreak. Here, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers. As a general principle, we assume that phenology is driven by the carbon status of the plant, which is closely related to environmental variables and the annual cycle of dormancy-activity. The carbon balance of a branch was modelled from autumn to winter with cold acclimation and dormancy and from winter to spring when deacclimation and growth resumption occur. After being calibrated in a field experiment, the model was validated across a large area (> 34 000 km 2 ), covering multiple conifers stands in Québec (Canada) and across heated plots for the SPRUCE experiment in Minnesota (USA). The model accurately predicted the observed dates of budbreak in both Québec (±3.98 d) and Minnesota (±7.98 d). The site-independent calibration provides interesting insights on the physiological mechanisms underlying the dynamics of dormancy break and the resumption of vegetative growth in spring.
Why it matches plant phenotyping methods針葉樹の芽吹き時期という植物状態を予測する機 mechanistic phenology modelを開発し、野外実験と複数地域・加温プロットで較正および検証しており、方法が研究の中心である。
abstractHere, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers.
Reproduction assets foundThe paper's Data availability section points to the public SPRUCE experiment dataset (used for model validation of black spruce budbreak under warming treatments) hosted on the mnspruce.ornl.gov site, which is an allowed URL. The Canadian sugar/phenology dataset (doi: 10.5683/SP3/MVRZMQ) is also public but its URL is不在Dataset · publico Cartenı̀ https://orcid.org/0000-0001-8985-1132
Annie Deslauriers https://orcid.org/0000-0002-4994-5772
Stefano Mazzoleni https://orcid.org/0000-0002-1132-2625
Data availability
Sugar and phenological datasets for the Canadian experiments
are available at doi: 10.5683/SP3/MVRZMQ. Data for the
SPRUCE experiment can be found at: https://mnspruce.ornl.gov/datasets/public.References
Adams HD, Germino MJ, Breshears DD, Barron-Gafford GA, Guardiola-
Claramonte M, Zou CB, Huxman TE. 2013. Nonstructural leaf
carbohydrate dynamics of Pinus edulis during drought-induced tree mortality
reveal role for carbon metabolism in mortality mechanism. New Phytologist
197: 1142–1151.
Ainsworth EA, Bush DR. 2011Open asset ↗mnspruce.ornl.govpdf-raw-page:11 lines:1-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The grapevine is vulnerable to diseases, deficiencies, and pests, leading to significant yield losses. Current disease controls involve monitoring and spraying phytosanitary products at the vineyard block scale. However, automatic detection of disease symptoms could reduce the use of these products and treat diseases before they spread. Flavescence dorée (FD), a highly infectious disease that causes significant yield losses, is only diagnosed by identifying symptoms on three grapevine organs: leaf, shoot, and bunch. Its diagnosis is carried out by scouting experts, as many other diseases and stresses, either biotic or abiotic, imply similar symptoms (but not all at the same time). These experts need a decision support tool to improve their scouting efficiency. To address this, a dataset of 1483 RGB images of grapevines affected by various diseases and stresses, including FD, was acquired by proximal sensing. The images were taken in the field at a distance of 1-2 meters to capture entire grapevines and an industrial flash was ensuring a constant luminance on the images regardless of the environmental circumstances. Images of 5 grape varieties (Cabernet sauvignon, Cabernet franc, Merlot, Ugni blanc and Sauvignon blanc) were acquired during 2 years (2020 and 2021). Two types of annotations were made: expert diagnosis at the grapevine scale in the field and symptom annotations at the leaf, shoot, and bunch levels on computer. On 744 images, the leaves were annotated and divided into three classes: 'FD symptomatic leaves', 'Esca symptomatic leaves', and 'Confounding leaves'. Symptomatic bunches and shoots were, in addition of leaves, annotated on 110 images using bounding boxes and broken lines, respectively. Additionally, 128 segmentation masks were created to allow the detection of the symptomatic shoots and bunches by segmentation algorithms and compare the results to those of the detection algorithms.
Why it matches plant phenotyping methodsブドウ病害の症状を画像から抽出するための専門家アノテーション付きデータセットであり、植物体・葉・枝・果房の病徴状態を対象とするフェノタイピング手法・ベンチマークとして中心的です。
abstractTo address this, a dataset of 1483 RGB images of grapevines affected by various diseases and stresses, including FD, was acquired by proximal sensing.
Reproduction assets foundThis Data Brief describes a paper-specific grapevine disease image dataset (1483 RGB images with expert annotations) publicly deposited on Mendeley Data, with a direct URL provided in the article.Dataset · publice:
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Plot 1: 44.6992974, -0.3924154
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City/Town/Region: Rions, Gironde
Latitude and longitude:
○
Plot 1: 44.6704526, -0.3561660
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Plot 2: 44.6726088, -0.3610193
•
City/Town/Region: Saint-Martin, Gironde
Latitude and longitude:
○
Plot 1: 44.5712274, -0.1697558
Data accessibility
Repository name: Mendeley Data
Direct URL to data: https://data.mendeley.com/datasets/3dr9r3w3jn/2
Related research article
Tardif, M., Amri, A., Keresztes, B., Deshayes, A., Martin, D., Greven, M., & Da Costa, J.-P. (2022). Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study. OENO One, 56(3), 371–384. https://doi.oOpen asset ↗Mendeley Data · 3dr9r3w3jn/2lines:46-137Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Even though mechanization has dramatically decreased labor requirements, vineyard management costs are still affected by selective operations such as winter pruning. Robotic solutions are becoming more common in agriculture, however, few studies have focused on grapevines. This work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines. The Faster R-CNN network was fine-tuned using 1215 RGB images collected in different vineyards and annotated through bounding boxes. The network was tested on 232 RGB images, PRs were categorized by wood type (W), orientation (Or) and visibility (V), and performance metrics were calculated. PR detection was dramatically affected by visibility. Highest detection was associated with visible intermediate complex spurs in Merlot (0.97), while most represented coplanar simple spurs allowed a 74% detection rate. The Mask R-CNN network was trained for grapevine organs (GOs) segmentation by using 119 RGB images annotated by distinguishing 5 classes (cordon, arm, spur, cane and node). The network was tested on 60 RGB images of light pruned (LP), shoot-thinned (ST) and unthinned control (C) grapevines. Nodes were the best segmented GOs (0.88) and general recall was higher for ST (0.85) compared to C (0.80) confirming the role of canopy management in improving performances of hi-tech solutions based on artificial intelligence. The two fine-tuned and tested networks are part of a larger control framework that is under development for autonomous winter pruning of grapevines. Supplementary information The online version contains supplementary material available at 10.1007/s11119-023-10006-y.
Why it matches plant phenotyping methods深層学習によるブドウ樹の剪定領域検出と器官セグメンテーションを開発・評価しており、植物器官状態の画像ベース取得が中心である。
abstractThis work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines.
Reproduction assets foundThe paper's annotated grapevine organ segmentation dataset (images with polygon/bounding-box annotations for cordon, arm, spur, cane, node) is publicly deposited on Zenodo. The pruning region detection dataset is not public and must be requested from the corresponding author. No author analysis code is available (code:Dataset · publicd Research, PRIN 20172HHNK5 Project.
Data availability
The pruning region detection dataset generated and/or analyzed during the presented study is currently not publicly available, but can be requested from the corresponding author on reasonable request. The annotated segmentation dataset is published on the zenodo platform at https://zenodo.org/record/5501784 .
Code availability
Not applicable.
Declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
The authors comply with the Journal’s Ethics guidelines confirming to respect third parties rights such as copyright and/or moral rights.
Consent to participate
NoOpen asset ↗zenodo · 5501784lines:583-615Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A monitoring of apple fruit, shoot and trunk growth was performed on 15 trees, equally split according to three treatments, which determined heavily contrasting carbon assimilate availability: unmanipulated trees (FRU), thinned trees (THI) and defruited trees (DEF). Several variables describe the vegetative growth on FRU and DEF trees (shoot length, base diameter, number of fruits on shoot, and height, diameter, pruning intensity and number of fruits of the branch carrying the shoot; trunk circumference), as well as the fruit growth on FRU and THI trees (3 fruit diameters). Additional measurements from ancillary shoots (apical diameter, number of leaves, leaf dry weight, stem dry weight, fresh mass, volume) and fruits (3 diameters, dry weight) from trees undergoing the same treatments, provide a more complete (destructive) characterization of organs growth, thanks to several measurements performed across the growing season. Organs are provided with categorical variables indicating the treatment, tree, canopy height, orientation (for both shoots and fruit), as well as branch and shoot identifiers, so that hierarchical modeling of the dataset can be performed. The dataset is completed with dates and day of the year of the measurements and the accumulated growing degree days from full bloom. Data can be used to calculate apple tree absolute and relative growth rates, maximum potential growth rates, as well as shoot growth responses to thinning and pruning. The dataset can also be used to calibrate allometric relationships, estimate structural apple tree growth parameters and their variability.
Why it matches plant phenotyping methodsリンゴ器官の成長形質を階層的・反復的に収録した再利用可能なデータセットであり、成長率やアロメトリーの推定・モデル較正に用いるデータ資源として方法論的価値がある。
titleA hierarchical dataset of vegetative and reproductive growth in apple tree organs under conventional and non-limited carbon resources.
Reproduction assets foundThe paper is a Data in Brief article describing its own apple tree growth phenotype dataset (shoot, fruit, trunk measurements) deposited publicly on Mendeley Data with DOI 10.17632/852r5dnzd5.1 and a direct URL. This is a paper-specific, public, actionable phenotype dataset.Dataset · publiccommercial orchard
City: Caldaro, Bolzano/Bozen province, Trentino Alto Adige region
Country: Italy
Latitude and longitude collected samples/data: 46° 21’ N, 11° 16’ E, Altitude 240 m
Period: May-November 2014
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/852r5dnzd5.1
Direct URL to data: https://data.mendeley.com/datasets/852r5dnzd5/1
Related research article
F. Reyes, T. DeJong, P. Franceschi, M. Tagliavini, D. Gianelle, Maximum growth potential and periods of resource limitation in apple tree, Frontiers in Plant Science 7 (2016). doi: 10.3389/fpls.2016.00233
Value of the Data
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The dataset allows analysis of the impact of fruit load, on vegetative aOpen asset ↗Mendeley Data · 10.17632/852r5dnzd5.1lines:1-61Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
In this paper, we present a direct drive brush-shaped tool developed for the use of robotic vine suckering. Direct drive design philosophy allows for precise and high bandwidth control of the torque exerted by the brush. Besides limiting the torque exerted onto the plant, this kind of design philosophy allows the brush to be used as a torque sensor. High bandwidth torque feedback from the tool is used to enable a position controlled robot arm to perform the suckering task without knowing the exact position and shape of the trunk of the vine. An experiment was conducted to investigate the dependency of the applied torque on the overlap between the brush and the obstacle. The results of the experiment indicate a quadratic relationship between torque and overlap. This quadratic function is estimated and used for compliant trunk shape following. A trunk shape following experiment demonstrates the utility of the presented tool to be used as a sensor for compliant robot arm control. The shape of the trunk is estimated by tracking the motion of the robot arm during the experiment.
Why it matches plant phenotyping methodsブドウ樹の幹形状をトルクセンサ付きロボット工具で推定する手法を開発・実験検証しており、植物形状の取得が中心的である。
abstractDirect drive design philosophy allows for precise and high bandwidth control of the torque exerted by the brush. Besides limiting the torque exerted onto the plant, this kind of design philosophy allows the brush to be used as a torque sensor.
Reproduction assets foundThe paper's authors explicitly state that their implementation of the prioritized task-space control algorithm (used for the compliant trunk shape following experiments) is publicly available on GitHub. No phenotype/trait datasets or image/sensor data deposits are mentioned; the video link is supplementary footage of aCode · publicThis implementation of the prioritized task-space control
algorithm is available on GitHub (https://github.com/ivatavuk/ptsc_eigen, accessed on
29 November 2022).Open asset ↗github.com/ivatavuk/ptsc_eigen · ivatavuk/ptsc_eigenpdf-page:10 lines:1-29Code / dataset availability confirmedCrossref · checked 14 Sept 2026
The characterization, analysis, and evaluation of morphology and structure are crucial in wheat research. Quantitative and fine characterization of wheat morphology and structure from a three-dimensional (3D) perspective has great theoretical significance and application value in plant architecture identification, high light efficiency breeding, and cultivation. This study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept. Specifically, 3D plant architecture parameters at the organ, phytomer, single stem, and individual plant scales were extracted based on the geometric models. Furthermore, plant architecture vector (PA) was proposed to comprehensively evaluate wheat plant architecture, including convergence index (C), leaf structure index (L), phytomer structure index (PHY), and stem structure index (S). The proposed method could quickly and efficiently achieve 3D wheat plant modeling by assembling 3D phytomers. In addition, the extracted PA quantifies the plant architecture differences in multi-scales among different cultivars, thus, realizing a shift from the traditional qualitative to quantitative analysis of plant architecture. Overall, this study promotes the application of the 3D phytomer concept to multi-tiller crops, thereby providing a theoretical and technical basis for 3D plant modeling and plant architecture quantification in wheat.
Why it matches plant phenotyping methods3D小麦モデルを開発し、複数スケールの植物構造形質を抽出・定量化することが研究の中心であるため。
abstractThis study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12030445/s1 , Table S1 shows the detailed parameter definition and description of 3D phytomers of wheat. Table S2 shows the plant architecture data for all plants used in this study.Open asset ↗MDPI · 10.3390/plants12030445/s1lines:95-296Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Maize is the world's most produced cereal crop, and the selection of maize cultivars with a high stem elastic modulus is an effective method to prevent cereal crop lodging. We developed an ultra-compact sensor array inspired by earthquake engineering and proposed a method for the high-throughput evaluation of the elastic modulus of maize cultivars. A natural vibration analysis based on the obtained Young's modulus using finite element analysis (FEA) was performed and compared with the experimental results, which showed that the estimated Young's modulus is representative of the individual Young's modulus. FEA also showed the hotspot where the stalk was most deformed when the corn was vibrated by wind. The six tested cultivars were divided into two phenotypic groups based on the position and number of hotspots. In this study, we proposed a non-destructive high-throughput phenotyping technique for estimating the modulus of elasticity of maize stalks and successfully visualized which parts of the stalks should be improved for specific cultivars to prevent lodging.
Why it matches plant phenotyping methodsトウモロコシ茎の弾性率を非破壊・高スループットに推定するセンサーアレイと数値検証を開発しており、植物表現型取得法が研究の中心である。
abstractWe developed an ultra-compact sensor array inspired by earthquake engineering and proposed a method for the high-throughput evaluation of the elastic modulus of maize cultivars.
Reproduction assets foundThe paper states that all source code used (the plantFEM finite-element analysis software for the maize elastodynamics simulations) is publicly available on GitHub at the authors' URL. No phenotype dataset or raw sensor waveform deposit is explicitly stated.Code · publicThe authors declare no competing financial interests.
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Additional information
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All source code used in this paper is available at Github
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(https://github.com/kazulagi/plantFEM)
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1. Berry, P. M. et al. Understanding and reducing lodging in cereals. Adv. Agron. 84, 217–271 (2004).
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2. Tirado, S. B., Hirsch, C. N. & Springer, N. M. Utilizing temporal measurements from UAVs to
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assess root lodging in maize and its impact on productivity. F. Crop. Res. 262, 108014 (2021Open asset ↗kazulagi/plantFEMpdf-raw-page:18 lines:1-63Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Lodging impedes the successful cultivation of cereal crops. Complex anatomy, morphology and environmental interactions make identifying reliable and measurable traits for breeding challenging. Therefore, we present a unique collaboration among disciplines for plant science, modelling and simulations, and experimental fluid dynamics in a broader context of breeding lodging resilient wheat and oat. We ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions. Measured phenotypes from experiments concluded that the wheat stems response is stiffer than the oat. However, these observations did not in themselves establish causal relationships of this observed behaviour with the physical traits of the plants. To further investigate we created an independent finite-element simulation framework integrating our recently developed multi-scale material modelling approach to predict the mechanical response of wheat and oat stems. All the input parameters including chemical composition, tissue characteristics and plant morphology have a strong physiological meaning in the hierarchical organization of plants, and the framework is free from empirical parameter tuning. This feature of our simulation framework reveals the multi-scale origin of the observed wide differences in the stem strength of both cereals that would not have been possible with purely experimental approach.
Why it matches plant phenotyping methods風洞実験と有限要素シミュレーションを統合し、植物茎の曲げ挙動・強度という表現型を予測・説明する手法が研究の中心である。
abstractWe ran comprehensive wind tunnel experiments to quantify the stem bending behaviour of both cereals under controlled aerodynamic conditions.
Reproduction assets foundThe paper's wind tunnel plant phenotyping assets are publicly available: raw wind tunnel videos of the cereal plants (DRUM repository), the authors' video-analysis scripts (GitHub), and the multi-scale finite-element model code (Dryad). Supplementary material with sample video and analysis details is on Figshare.Code · publiche scripts used and location of the data analysed from the wind tunnel experiment. Multi-scale material model codes in Python, Abaqus model file and python script for automatized simulations at different wind speed levels pertaining to multi-scale finite-element model simulations are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.612jm644j [ 53 ].
Supplementary material is available online [ 54 ].
Authors' contributionsOpen asset ↗Dryad Digital Repository · 10.5061/dryad.612jm644jlines:229-239Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
Why it matches plant phenotyping methodsUAV画像から作物のキャノピー高を抽出し、時系列モデルで生育・形質を予測する枠組みが研究の中心であり、実質的な植物フェノタイピング手法の開発・適用に該当する。
abstractHere we provide a novel framework for trait prediction using CH data in rice.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) in 2019, 2020, and 2021 from the aspect of genetics and examined how to use the CH data for the prediction of traits usually measured by hand ( Figure 1 ).Open asset ↗lines:342-376Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Plant height (PH) is an essential trait in maize (Zea mays) that is tightly associated with planting density, biomass, lodging resistance, and grain yield in the field. Dissecting the dynamics of maize plant architecture will be beneficial for ideotype-based maize breeding and prediction, as the genetic basis controlling PH in maize remains largely unknown. In this study, we developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages. Time-resolved i-traits with novel digital phenotypes and complex correlations with agronomic traits were characterized to reveal the dynamics of maize growth. An i-trait-based genome-wide association study identified 4945 trait-associated SNPs, 2603 genetic loci, and 1974 corresponding candidate genes. We found that rapid growth of maize plants occurs mainly at two developmental stages, stage 2 (S2) to S3 and S5 to S6, accounting for the final PH indicators. By integrating the PH-association network with the transcriptome profiles of specific internodes, we revealed 13 hub genes that may play vital roles during rapid growth. The candidate genes and novel i-traits identified at multiple growth stages may be used as potential indicators for final PH in maize. One candidate gene, ZmVATE, was functionally validated and shown to regulate PH-related traits in maize using genetic mutation. Furthermore, machine learning was used to build predictive models for final PH based on i-traits, and their performance was assessed across developmental stages. Moderate, strong, and very strong correlations between predictions and experimental datasets were achieved from the early S4 (tenth-leaf) stage. Colletively, our study provides a valuable tool for dissecting the spatiotemporal formation of specific internodes and the genetic architecture of PH, as well as resources and predictive models that are useful for molecular design breeding and predicting maize varieties with ideal plant architectures.
Why it matches plant phenotyping methods自動化された高スループット画像表現型プラットフォームを開発し、77種類の画像形質を定量化・検証し、機械学習による草丈予測も評価しており、表現型取得・抽出法が研究の中心である。
abstractwe developed an automated high-throughput phenotyping platform (HTP) to systematically and noninvasively quantify 77 image-based traits (i-traits) and 20 field traits (f-traits) for 228 maize inbred lines across all developmental stages.
Reproduction assets foundThe paper explicitly states that all images and phenotypic data are available on figshare and that the HTP/RGB image and GWAS analysis pipeline code is available on the authors' GitHub repository (maizeHTP). Both are paper-specific, public, and actionable.Dataset · publicAll the images and phenotypic data are available at https://figshare.com/account/home#/projects/141743 .Open asset ↗figshare · projects/141743lines:168-200Code · publicThe code for HTP from LemnaTec and the code for the RGB image and GWAS analysis pipelines can be downloaded at https://github.com/GUOWEIJUN/maizeHTP .Open asset ↗GitHub · GUOWEIJUN/maizeHTPlines:168-200Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of Chenopodium album (wild spinach) and Solanum lycopersicum (tomato plant).
Why it matches plant phenotyping methods植物3D点群から器官を自動分割・識別するグラフベース手法を開発し、合成および実データで評価しており、植物表現型取得の技術が中心である。
abstractAccurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping.
Reproduction assets foundThe paper's Chenopodium 3D point cloud dataset (with ground truth annotations) is publicly deposited on Zenodo, and the reconstruction pipeline code is open source on GitHub (romi/plant-3d-vision).Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/6962994#.YuvYkS8itqs .Open asset ↗zenodo · 6962994lines:641-715Code · publicThe entire code is open source and available online ( https://github.com/romi/plant-3d-vision ).Open asset ↗github · romi/plant-3d-visionlines:428-438Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
The automatic, and accurate plant phenotyping plays important role to improve the crop yield through enabling efficient plant analysis and plant breeding studies. The 3d deep learning has allows automatic segmentation of plant parts from point cloud data. However, the network architecture is designed manually and performance is limited to prior experience. The aim of this study is to search for optimal 3d deep networks to perform the plant part segmentation. We perform the 3d neural architecture search by training a super network composed of candidate networks. Using the trained super network, the evolutionary searching is used to search for top performing architecture. The results demonstrate the searched architecture outperforms manually designed architectures by attaining mean IoU and accuracy of more than 90% and 96%, respectively. The searched architecture achieves more than 83% class-wise IoU for all main stem, branches, and boll class. These plant part segmentation method shows promising results and holds potential to be utilized by plant breeders for enhancing the production quality.
Why it matches plant phenotyping methods植物点群から茎・枝・ボールなどの器官を自動分割する3Dニューラルネットワーク探索手法を開発・評価しており、植物表現型取得が研究の中心です。
abstractThe aim of this study is to search for optimal 3d deep networks to perform the plant part segmentation.
Reproduction assets foundThe paper's cotton plant LiDAR point cloud dataset (with plant part annotations) is publicly available via a DOI in the data availability statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe collected dataset used in this study is available at https://doi.org/10.25739/vnr9-xt59.
ACKNOWLEDGMENTS
Authors gratefully thank Dr. Shangpeng Sun and Javier Rodriguez for data collection. Authors
additionally thank Bio-sensing and Instrumentation Lab (BSAIL) members for their helpful discussions.
Authors further gratefully thank for computing resources and technical expertise from Georgia Advanced
Computing ResoOpen asset ↗10.25739/vnr9-xt59pdf-layout-page:6 lines:1-29Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Lodging is one of the major constraints in attaining high yield in crop production. Major factors associated with stalk lodging involve morphological traits and anatomical features along with the chemical composition of the stem. However, little relevant research has been carried out in sorghum, particularly on the anatomical aspects. In this study, with a high-throughput procedure newly developed by our research group, the nine parameters related to stem regions and vascular bundles were generated in 58 sorghum germplasm accessions grown in two successive seasons. Correlation analysis and principal component analysis were conducted to investigate the relationship between anatomical aspects and stalk mechanical traits (breaking force, stalk strength and lodging index). It was found that most vascular parameters were positively associated with breaking force and lodging index with the correlation coefficient r varying from −0.46 to 0.64, whereas stalk strength was only associated with rind area with the r = 0.38. The germplasm resources can be divided into two contrasting categories (classes I with 23 accessions and II with 30 accessions). Compared to class II, the class I was characterized by a larger number (+40.7%) and bigger vascular bundle (+30%), thicker stem (+19.6%) and thicker rind (+36.0%) but shorter internode (plant) (−91.0%). This study provides the methodology and information for the studies of the stem anatomical parameters in crops and facilitates the selective breeding of sorghum.
Why it matches plant phenotyping methodsソルガム茎の断面形態・維管束形質を抽出する新規ハイスループット手順が研究の中心であり、機械的特性との関連も評価しているため。
abstractwith a high-throughput procedure newly developed by our research group, the nine parameters related to stem regions and vascular bundles were generated
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe Python codes were provided as
Supplementary Material in PDF format (Figure S1).Open asset ↗pdf-page:4 lines:1-59Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In cereals with hollow internodes, lodging resistance is influenced by morphological characteristics such as internode diameter and culm wall thickness. Despite their relevance, knowledge of the genetic control of these traits and their relationship with lodging is lacking in temperate cereals such as barley. To fill this gap, we developed an image analysis-based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions. Analysis of culm trait data collected from field trials in seven different environments revealed high heritability values (>50%) for most traits except thickness and stiffness, as well as genotype-by-environment interactions. The collection was structured mainly according to row-type, which had a confounding effect on culm traits as evidenced by phenotypic correlations. Within both row-type subsets, outer diameter and section modulus showed significant negative correlations with lodging (<-0.52 and <-0.45, respectively), but no correlation with plant height, indicating the possibility of improving lodging resistance independent of plant height. Using 50k iSelect SNP genotyping data, we conducted multi-environment genome-wide association studies using mixed model approach across the whole panel and row-type subsets: we identified a total of 192 quantitative trait loci (QTLs) for the studied traits, including subpopulation-specific QTLs and 21 main effect loci for culm diameter and/or section modulus showing effects on lodging without impacting plant height. Providing insights into the genetic architecture of culm morphology in barley and the possible role of candidate genes involved in hormone and cell wall-related pathways, this work supports the potential of loci underpinning culm features to improve lodging resistance and increase barley yield stability under changing environments.
Why it matches plant phenotyping methods大麦の穂軸径と穂軸壁厚を測定する画像解析プロトコルを開発し、多数アクセッション・複数環境で適用しており、表現型取得法が研究の中心的要素である。
abstractwe developed an image analysis-based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions.
Reproduction assets foundThe paper's culm morphology phenotype measurements (BLUEs per genotype, trait statistics, heritability inputs) are distributed via the article's public supplementary material, which also contains the image-analysis phenotyping protocol (Supplementary Method 1) and analysis methods (Supplementary Methods 3-4). No authorSupplement · publicwe initially obtained the best linear unbiased estimates (BLUEs; Supplementary Table 1 ) of each genotype from the analysis of individual environmentsOpen asset ↗lines:49-59Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Background Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. Results For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. Conclusion NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.
Why it matches plant phenotyping methodsブドウのスペクトルを用いたフェノミック予測法を開発・評価し、ゲノム予測との比較や予測能力の検証を行っており、植物形質推定手法が研究の中心である。
abstractThis study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour.
Reproduction assets foundThe paper explicitly deposits its grapevine phenotypic/genotypic data and its NIRS spectra, R analysis scripts, and result tables in the INRAE data portal under two DOIs, both listed in allowed_urls. These are paper-specific, publicly actionable assets directly reproducing the phenotyping measurements and computationalDataset · publicGenotypic values and genotypic data for half-diallel and diversity panel populations are available at https://doi.org/10.15454/PNQQUQOpen asset ↗10.15454/PNQQUQlines:204-268Dataset · publicSpectra, R scripts and result tables have been deposited in the INRAE data portal: https://doi.org/10.15454/BICRFXOpen asset ↗INRAE data portal · 10.15454/BICRFXlines:204-268Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
Why it matches plant phenotyping methods3D LiDARとRGBによる苗のスキャン基盤を開発し、植物フェノタイピング用データベースを構築・検証しているため、取得手法と再利用可能なデータセットが中心である。
abstractthis paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research.
Reproduction assets foundThe paper's maize seedling LiDAR phenotyping database (362 plants, 7 campaigns, raw/processed point clouds with stem/leaf labels, RGB images, rosbags) is publicly released on OSF across seven campaign-specific repositories, explicitly stated in the Data Availability Statement and Table 3. The GitHub links (sick_scan, uDataset · publicOur generated dataset is available online at: 1st campaign: https://osf.io/fcgwk/ ;Open asset ↗osf · fcgwklines:435-442Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 15 Sept 2026
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.) in vineyards where sustainability was threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of wood quality and plant health across plant species.
Why it matches plant phenotyping methodsブドウ樹内部組織と病害状態を、MRI・X線CT・自動ボクセル分類によって非破壊的に定量する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractwe developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants
Reproduction assets foundThe paper's imaging datasets (MRI, X-ray CT, photographic volumes, annotations) are only available 'upon reasonable request', but the authors' extended Trainable Segmentation plugin used for the machine-learning voxel classification is explicitly open-source on GitHub.Code · publicFernandez et al. 24
DATA AND CODE AVAILABILITY
The datasets generated and analyzed during the current study are available from the corresponding
author upon reasonable request.
The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable
Segmentation on GitHub: https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted February 3, 2023.
;
https://doi.org/10.1101/2022.06.09.495457
doOpen asset ↗Rocsg/Trainable_Segmentation · Hyperwekapdf-raw-page:24 lines:1-16Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the help of computer vision and image processing methods. Despite the attempts that have been made to automate grapevine pruning, the task remains hard for the abovementioned domains. The reason for this is that several challenges such as cane overlapping or complex backgrounds appear. Additionally, there is no public image dataset for this problem which makes it difficult for the research community to approach it. Motivated by the above facts, an image dataset is proposed for grapevine canes’ segmentation for a pruning task. An experimental analysis is also conducted in the proposed dataset, achieving a 67% IoU and 78% F1 score in grapevine cane semantic segmentation with the U-net model.
Why it matches plant phenotyping methodsブドウ樹の枝を画像から分割する公開データセットを提案し、セグメンテーション性能も評価しており、植物器官の画像計測手法・ベンチマークが中心です。
abstractan image dataset is proposed for grapevine canes’ segmentation for a pruning task.
Reproduction assets foundThe paper's own grapevine pruning image dataset (100 RGB images with hand-annotated segmentation masks) is publicly available on GitHub under CC BY 4.0, as stated in the abstract and Data Availability Statement. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · publicData Availability Statement: Data are available at https://github.com/humain-lab/Buds-Dataset
under Creative Commons Attribution 4.0 International license.Open asset ↗humain-lab/Buds-Datasetpdf-page:9 lines:1-60Code / 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 · checked 15 Sept 2026
Submergence during germination impedes aerobic metabolisms and limits the growth of most higher plants. However, some wetland plants including rice can germinate under submerged conditions. It has long been hypothesized that the first elongating shoot tissue, the coleoptile, acts as a snorkel to acquire atmospheric oxygen (O 2 ) to initiate the first leaf elongation and seminal root emergence. Here, we obtained direct evidence for this hypothesis by visualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system. In parallel with the O 2 imaging, we tracked the anatomical development of shoot and root tissues in real-time using an automated flatbed scanner. Three hours after the coleoptile tip reached the water surface, O 2 levels around the embryo transiently increased. At this time, the activity of alcohol dehydrogenase (ADH), an enzyme critical for anaerobic metabolism, was significantly reduced, and the coleorhiza covering the seminal roots in the embryo was broken. Approximately 10 h after the transient burst in O 2 , seminal roots emerged. A transient O 2 burst around the embryo was shown to be essential for seminal root emergence during submerged rice germination. The parallel application of a planar O 2 optode system and automated scanning system can be a powerful tool for examining how environmental conditions affect germination in rice and other plants.
Why it matches plant phenotyping methods水中イネ発芽時の酸素動態と器官発達を、平面O2オプトードおよび自動スキャナーで時空間的に可視化・追跡する手法が研究の中心であり、植物の生理状態・形態発達を測定する実質的なフェノタイピング手法である。
abstractvisualizing the spatiotemporal O 2 dynamics during submerged germination in rice using a planar O 2 optode system.
Reproduction assets foundThe paper's phenotyping measurements (time-lapse growth images and planar optode O2 imaging of submerged rice germination) are publicly available as Supplementary Videos 1–3 hosted at the Frontiers supplementary material URL. No author analysis code or trained models are deposited; ImageJ and UWSC are generic third‑dayDataset · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.946776/full#supplementary-material
Supplementary Video 1
Time-lapse images showing the germination process of submerged rice with normoxic or anoxic atmospheres.
Click here for additional data file.
Supplementary Video 2
Time-lapse images of the growth process following a shift from an anoxic to a normoxic atmosphOpen asset ↗lines:107-237Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
Abstract 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 at organ level from plant images. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. PhenoTrack3D improves a former method limited to 3D reconstruction at a single time point [Artzet et al ., 2019] by (i) a novel stem detection method based on deep-learning and (ii) a new and original multiple sequence alignment method to perform the temporal tracking of ligulated leaves. Our method exploits both the consistent geometry of ligulated leaves over time and the unambiguous topology of the stem axis. Growing leaves are tracked afterwards 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 < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants x 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 to 355 plants. Conclusions We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise automatically and at a high-throughput the development of maize architecture at organ level. It has been validated for hundreds of plants during the entire development cycle, showing its applicability to the GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D再構成・時系列追跡による表現型抽出パイプラインを開発し、大規模データセットで技術検証しており、方法が研究の中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D+t reconstruction of maize at organ level from plant images.
Reproduction assets foundThe paper explicitly states that the PhenoTrack3D pipeline source code and examples are publicly available on GitHub under an Open Source licence (Cecill-C). This is the authors' analysis code for the paper's maize phenotyping pipeline. No public phenotype dataset or trained model checkpoint URL is stated in the blocksCode · publicThe source code and examples are available on Github
(https://github.com/openalea/phenotrack3d) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dpdf-page:28 lines:1-62Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.
Why it matches plant phenotyping methods深層学習と指向性探索アルゴリズムを開発し、成熟ダイズの茎関連形質を画像から抽出・手測定と検証しており、植物フェノタイピング手法が研究の中心です。
abstractIn this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm.
Reproduction assets foundThe paper's mature soybean stem-node image dataset (1,523 original images, augmented to 6,092) is publicly deposited on Kaggle by the authors. No author analysis code or trained model checkpoints are explicitly released; supplementary material contains only figures/tables, not datasets or code.Dataset · publicThe dataset is available at https://www.kaggle.com/datasets/soberguo/soybeannode .Open asset ↗Kaggle · soberguo/soybeannodelines:331-343Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In recent times, the classification and identification of different fruits and food crops have become a necessity in the field of agricultural science; for sustainable growth. Probable processes have been developed worldwide to improve the production of food crops. Problem-specific, clean and crisp datasets are also lagging in the sector. This article introduces an image dataset of varieties of banana plants and the diseases related to them. The varieties of Banana plants that we have considered in the dataset are the Malbhog ( Musa assamica ), Jahaji ( Musa chinensis ), Kachkol ( Musa paradisiaca L. ), Bhimkol ( M. Balbisiana Colla ). And the diseases and pathogens that we have considered here are the Bacterial Soft Rot, Banana Fruit Scarring Beetle, Black Sigatoka, Yellow Sigatoka, Panama disease, Banana Aphids, and Pseudo-Stem Weevil. A dataset of Potassium deficiency has been also considered in this article. A total of 8000+ processed images are present in the dataset. The purpose of this article is to provide the Researchers and Students in getting access to our dataset that would help them in their research and in developing some machine learning models.
Why it matches plant phenotyping methodsバナナ植物の器官・品種・病害・カリウム欠乏を画像化した大規模データセットを提供しており、植物の状態推定に再利用できるデータ基盤が中心である。
abstractThis article introduces an image dataset of varieties of banana plants and the diseases related to them.
Reproduction assets foundThe paper is a data descriptor for the PSFD-Musa banana image dataset, publicly deposited on Mendeley Data with an explicit URL and DOI.Dataset · publics of different backgrounds to train, test, and validate classification models.
Data source location
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BORTARI VILLAGE,
Chaygaon, Kukurmara, District – Kamrup (Rural), Assam, India.
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HAJO VILLAGE,
District – Kamrup (Rural), Assam, India.
Data accessibility
Data is available at Mendeley Data, under the DOI: 10.17632/4wyymrcpyz.1 https://data.mendeley.com/datasets/4wyymrcpyz/1
Value of the Data
•
The dataset provided here is the collection of different varieties of banana plants, some common diseases that affect them, and their deficiency. These varieties of banana plants are indigenously found in Assam. The data can be useful in the way to classifying the different diseases and pathogens whicOpen asset ↗Mendeley Data · 10.17632/4wyymrcpyz.1lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The existing methods of callose quantification include epifluorescence microscopy and fluorescence spectrophotometry of aniline blue-stained callose particles, immuno-fluorescence microscopy and indirect assessment of both callose synthase and β-(1,3)-glucanase enzyme activities. Some of these methods are laborious, time consuming, not callose-specific, biased and require high technical skills. Here, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA). Tissue culture-derived banana plantlets were inoculated with Xanthomonas campestris pv. musacearum ( Xcm ) bacteria as a biotic stress factor inducing callose production. Banana leaf, pseudostem and corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification. Callose levels were significantly different in banana tissues of Xcm -inoculated and control groups except in the pseudostems of both banana genotypes. The method described here could be applied for the quantification of callose in different plant species with satisfactory level of specificity to callose, and reproducibility. Additionally, the use of 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases. We provide step-by-step detailed descriptions of the method.
Why it matches plant phenotyping methods植物組織中のカロース量を定量するELISA法を開発・再現性評価し、高スループット測定への適用性を示した研究であり、植物状態の取得方法が中心です。
abstractHere, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA).
Reproduction assets foundThe paper's supplementary material (Table S1) publicly hosts the callose quantification measurements (concentrations in leaves, pseudostems, corms of Xcm-inoculated vs. control banana plantlets) underlying this study's analysis. No author analysis code, images, or trained models are deposited; the R statistical packageDataset · publicor up to 12 months).
Dissolve para-nitrophenyl phosphate (pNPP) in substrate buffer to a working concentration of 1 mg/mL.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/mps5040054/s1 , Table S1: Analysis of callose concentration in the leaves, pseudostems and corms of banana plants inoculated and non-inoculated (control) with Xcm (Independent sample t-test, α ≤ 0.05).
Click here for additional data file.
Author Contributions
Conceptualization, A.K.T.; methodology, A.S.M., A.K.T. and P.S.; validatiOpen asset ↗lines:167-297Code / dataset availability confirmedbioRxiv · checked 8 Sept 2026
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.
Why it matches plant phenotyping methods植物細胞内オルガネラの3D形態を取得・解析する電子顕微鏡プロトコルと深層学習ワークフローが研究の中心であり、植物CCV形態の技術的スクリーニング手法を提供している。
abstractHere we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs)
Reproduction assets foundThe paper's Data Availability statement explicitly deposits example data (SEM replica images, training image pairs) and the analysis code (Cellpose-based CCV segmentation workflow) generated in this study at a public Zenodo DOI, making it a paper-specific, publicly actionable asset. The temography.com URLs are vendor/mCode · publicand round; LF, large and
354
flat) using an area threshold of 8500 nm2
(a CCV diameter of 105 nm) and a 3D value of 1.52 (the
355
average of the 3 smallest CCVs in control conditions determined to be spherical by the experimenter).
356
Data Availability
357
Example data and the code generated in this study is available at:
358
https://doi.org/10.5281/zenodo.6563819
359
Acknowledgements
360
This research was supported by the Scientific Service Units of Institute of Science and Technology
361
Austria (ISTA) through resources provided by the Electron Microscopy Facility, Lab Support Facility and
362
the Imaging and Optics Facility. A.J. is supported by funding from the Austrian Science FundOpen asset ↗zenodo · 10.5281/zenodo.6563819pdf-raw-page:12 lines:1-46Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
The vascular bundle of the shank is an important 'flow' organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality.
Why it matches plant phenotyping methods植物茎部のマイクロCT画像から36形質を自動抽出するパイプラインが研究の中心であり、ハイスループット表現型解析手法の実質的な適用に該当する。
abstractshank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm.
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 S3: The BLUP values for 30-item phenotypic traits of the 202 inbred lines; Supplementary Table S4: The result data of GWASOpen asset ↗lines:712-726Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Abstract Background: A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. Results: We conducted experiments using a dataset of Setaria viridis. Experiments show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method. The best mean absolute error between the hand-labeled and estimated tiller numbers by the proposed method is 0.57. Conclusions: We realized applying DNN methods to tiller number estimation methods by using pretext tasks. The proposed method outperformed the conventional approach.
Why it matches plant phenotyping methods深層ニューラルネットワークを用いて植物の分げつ数を自動推定する手法を開発・比較しており、植物表現型の取得が研究の中心である。
abstractan automated approach is necessary for high-throughput phenotyping
Reproduction assets foundThe paper's tiller estimation experiments use the Setaria viridis image dataset (25,570 images, 576 with hand-labeled tiller numbers), which the authors explicitly state is publicly available in the figshare repository. No author analysis code or trained models are reported as deposited.Dataset · publicThe dataset analyzed during the current study are available in the figshare repository,
https://figshare.com/articles/dataset/DDPSC_Phenotyping_Manuscript_1_Files/1272859 [11].Open asset ↗figshare · 1272859pdf-page:12 lines:1-70Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Field / plotRootStem / branchWhole plant / canopy / plot / fieldObject detectionSegmentationRoot system architecture
Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. The minirhizotron data stem from various wetland types on organic soils. RootDetector showed a high capability to correctly segmenting root pixels in minirhizotron images from field observations (F1 = 0.6044; r² compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average almost 3-times higher root length/cm² per image compared to expert analysts. Analyses with RootDetector save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.
Why it matches plant phenotyping methodsミニライゾトロン画像から根を自動検出・セグメンテーションするCNN手法を開発し、人間の専門家と性能・再現性を比較しており、植物形態形質の取得方法が中心です。
abstractHere, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise.
Reproduction assets foundThe authors state RootDetector is supplied as usable code on GitHub, with the Data Accessibility section giving the repository URL, which matches an allowed URL.Code · publicRootDetector is supplied as readily usable code on GitHub, enabling easy use byOpen asset ↗pdf-page:20 lines:1-51Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudRootStem / branch2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometry
Living architecture, changing in structure with annual growth, requires precise, regular characterisation. However, its geometric irregularity and topological complexity make documentation using traditional methods difficult and presents challenges in creating useful models for mechanical and physiological analyses. Two kinds of living architecture are examined: historic living root bridges grown in Meghalaya, India, and contemporary 'Baubotanik' structures designed and grown in Germany. These structures exhibit common features, in particular network-like structures of varying complexity that result from inosculations between shoots or roots. As an answer to this modelling challenge, we present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow that solves two problems related to the anastomoses and varying nearby elements specific to living architecture. Photogrammetry was used as a low cost method, supplying detailed point clouds of the structures' visible surfaces. A workflow based on voxel-thinning (using deletion templates and adjusted p-simplicity criteria) provides efficient, accurate skeletons. A volume reconstruction method is derived from the thinning process. The workflow is assessed on seven characteristics beneficial in representing living architecture in comparison with alternative skeleton extraction methods. The resulting models are ready for use in analytical tools, necessary for functional, responsible design.
Why it matches plant phenotyping methods植物の生体構造をフォトグラメトリで取得し、点群から骨格・体積を再構成するワークフロー自体が中心的な方法開発であり、植物構造の表現・解析に用いるため。
abstractwe present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' skeletonisation source code on GitHub and the photogrammetric point clouds (Freiburg pavilion, Ficus joint, Baubotanik joint) on the TUM media repository. Both are paper-specific, public, and actionable.Code · publicThe source code is available at: https://github.com/QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinningOpen asset ↗QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinninglines:141-214Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Abstract Background Stalk lodging (mechanical failure of plant stems during windstorms) leads to global yield losses in cereal crops estimated to range from 5% to 25% annually. The cross-sectional morphology of plant stalks is a key determinant of stalk lodging resistance. However, previously developed techniques for quantifying cross-sectional morphology of plant stalks are relatively low-throughput, expensive and often require specialized equipment and expertise. There is need for a simple and cost-effective technique to quantify plant traits related to stalk lodging resistance in a high-throughput manner. Results A new phenotyping methodology was developed and applied to a range of plant samples including, maize ( Zea mays ), sorghum ( Sorghum bicolor ), wheat ( Triticum aestivum ), poison hemlock ( Conium maculatum ), and Arabidopsis (Arabis thaliana). The major diameter, minor diameter, rind thickness and number of vascular bundles were quantified for each of these plant types. Linear correlation analyses demonstrated strong agreement between the newly developed method and more time-consuming manual techniques (R 2 > 0.9). In addition, the new method was used to generate several specimen-specific finite element models of plant stalks. All the models compiled without issue and were successfully imported into finite element software for analysis. All the models demonstrated reasonable and stable solutions when subjected to realistic applied loads. Conclusions A rapid, low-cost, and user-friendly phenotyping methodology was developed to quantify two-dimensional plant cross-sections. The methodology offers reduced sample preparation time and cost as compared to previously developed techniques. The new methodology employs a stereoscope and a semi-automated image processing algorithm. The algorithm can be used to produce specimen-specific, dimensionally accurate computational models (including finite element models) of plant stalks.
Why it matches plant phenotyping methods植物茎の横断面形態を高スループットに定量する画像ベースの表現型計測法を開発し、手作業法との一致性検証と有限要素モデルへの応用を行っており、方法が研究の中心である。
abstractA new phenotyping methodology was developed and applied to a range of plant samples
Reproduction assets foundThe paper's MATLAB image-processing algorithm (authors' analysis code) and sample cross-sectional images are publicly available as supplementary files (Additional files 2 and 3) attached to this open-access article, along with standard operating protocols (Additional file 1). These directly reproduce the paper's phenotCode · publicThe code for the image-processing algorithm is also provided as Additional file 2 . Sample images and instructions are provided as Additional file 3 .Open asset ↗lines:110-119Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modelling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang–Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose–responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose–response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度温度データと反復草丈データから小麦の成長・温度応答形質を抽出するモデルを開発・評価しており、表現型抽出手法が研究の中心である。
titlePhenomics data processing: extracting dose–response curve parameters from high-resolution temperature courses and repeated field-based wheat height measurements
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the source code supporting its phenotyping analysis (dose–response extraction from wheat height and temperature data) in a public ETH GitLab repository, archived in the ETH Research Collection with a DOI. Both URLs are allowed and the repository/identifier verCode · publicting—original draft. H.-P.P.: Conceptualization,
methodology, writing—review & editing. A.H.: Conceptualization,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 20Open asset ↗gitlab.ethz.ch/crop_phenotyping/htfp_data_processingpdf-raw-page:13 lines:1-88Code · publication,
supervision, project administration, funding acquisition, writing—
review & editing.
DATA AVAILABILITY
Data and source code that support the findings of this study are openly
available in the ETH gitlab repository at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH research
collection (http://doi.org/10.5905/ethz-1007-385).LITERATURE CITED
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018.
Translating high-throughput phenotyping into genetic gain. Trends
in Plant Science 23:451–466. doi:10.1016/j.tplants.2018.02.001.
Bonhomme R. 2000. Bases and limits to using ‘degree.day’
units. European Journal of Agronomy 13:1–10. doi:10.1016/
SOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:13 lines:1-88Code / dataset availability 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 confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
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Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
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thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
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experimental resources.
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Data Availability
483
All the phenotypic data and images can be viewed and downloaded via the link
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(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
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References
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Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
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Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
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of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
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An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
Why it matches plant phenotyping methods植物の3D点群から分枝構造を骨格化し、分枝角度を推定するアルゴリズムを開発・比較評価しており、植物表現型の抽出手法が研究の中心です。
abstractHere, we studied the skeletonization of plant architectures from 3D point clouds
Reproduction assets foundThe paper's Data Availability Statement explicitly states that data and code executable are publicly available at the authors' GitHub repository iziamtso/P3D, which is an allowed URL. This covers the paper-specific plant point cloud data and skeletonization analysis code.Code · publicData Availability Statement: Data and code executable are available at: https://github.com/iziamtso/P3D.Open asset ↗iziamtso/P3Dpdf-page:14 lines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
The European mistletoe ( Viscum album ) is a dioecious epiphytic evergreen hemiparasite that develops an extensive endophyte enabling the absorption of water and mineral salts from the host tree, whereas the exophytic leaves are photosynthetically active. The attachment mode and host penetration are well studied, but little information is available about the effects of mistletoe age and sex on haustorium-host interactions. We harvested 130 plants of Viscum album ssp. album growing on host branches of Aesculus flava for morphological and anatomical investigations. Morphometric analyses of the mistletoe and the (hypertrophied) host interaction site were correlated with mistletoe age and sex. We recorded the morphology of the endophytic systems of various ages by using X-ray microtomography scans and corresponding stereomicroscopic images. For detailed anatomical studies, we examined thin stained sections of the mistletoe-host interface by light microscopy. The diameter and length of the branch hypertrophy showed a positive linear correlation with the age of the mistletoe. Correlations with their sex were only found for ratios between host branch and hypertrophy size. A female bias of about 76% was found. In a 4-year-old mistletoe, several small, almost equally sized sinkers and the connected cortical strands extend over more than 5 cm within the host branch. In older mistletoes, one main sinker was predominant and occupied an increasingly large proportion of the stem cross-section. Bands of vessels ran along the axis of the wedge-shaped haustoria and sinkers and bent sideways toward the mistletoe-host interface. At the interface, the vascular elements of the host wood changed their direction and formed vortices near the haustorium.
Why it matches plant phenotyping methodsマイクロCTとステレオ画像による植物内部構造の3D可視化・形態計測が研究の中心的手法であり、ミストルの内生系や宿主との相互作用部位という植物形態形質を取得している。
titleAdvances on the Visualization of the Internal Structures of the European Mistletoe: 3D Reconstruction Using Microtomography
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 1
Raw data of diameters and lengths of host branch (hypertrophy) and mistletoe.Open asset ↗lines:257-351Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC [Formula] of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling "The sum of cross sectional areas after a branch junction equals the one before the branch junction" (Pipe Model Theory) and "Twigs are self-similar" (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B0 * RPRB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC [Formula] of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript. Upload statementSelf-uploaded pre-print for peer-review submitted manuscript. The manuscript was submitted on 26th of July 2021 to Plos Computational Biology: I, Jan Hackenberg uploaded this manuscript because the automated journal upload was rejected for the following reason: Thank you for considering posting your manuscript "SimpleForest - a comprehensive tool for 3d reconstruction of tree from forest plot point clouds." as a preprint. Your manuscript does not meet bioRxivs criteria and therefore we will not be sending it for posting as a preprint. For more information about our checks, see link. We have noted that it contains material that is potentially subject to copyright. In particular, screenshot in Figure 1. Preprints posted to bioRxiv following submission to PLOS journals are done so under the CC BY license. To avoid a potential breach of the copyright that applies to the material listed above, we are unable to make the manuscript publicly available. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/454344v1_fig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1094b17org.highwire.dtl.DTLVardef@120d7a3org.highwire.dtl.DTLVardef@12d2fbcorg.highwire.dtl.DTLVardef@1990a4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO Submission system screenshot. C_FIG Please note that this decision does not affect the editorial process at PLOS Computational Biology. Your manuscript is being separately assessed with regards to sending for peer review. From section Abstract on, the pdf you see is same as submitted one.
Why it matches plant phenotyping methods森林プロットの点群から樹木を3D再構成し、DBH・体積などの植物構造形質を定量化するソフトウェアと自動解析パイプラインを開発・検証しており、フェノタイピング手法が中心である。
abstractSimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders.
Reproduction assets foundThe paper explicitly publishes its TLS point cloud datasets (5 datasets with harvested ground-truth volumes), SimpleForest processing/QSM scripts, R validation scripts, combined results table, and GPL v3 source code in a public Zenodo repository (10.5281/zenodo.5131717) and GitLab repository, all directly reproducing QCode · publicipts SimpleForest scripts to process S1 Dataset. 75
• Erythrophleum fordii denoising scripts: 76
https://zenodo.org/record/5131717/files/hackenbergErythrophleumDenoisingScripts.zip 77
• Pinus massoniana denoising scripts: 78
https://zenodo.org/record/5131717/files/hackenbergPinusDenoisingScripts.zip 79
• QSM modeling script: 80
https://zenodo.org/record/5131717/files/hackenbergQsm.xsct2 81
S2 Processing scripts SimpleForest scripts to process S2 Dataset. 82
• Denoising scripts: 83
https://zenodo.org/record/5131717/files/deTanagoDenoisingScriptsDenoisedClouds.zip 84
• Poisson reconstruction buttress script: 85
https://zenodo.org/record/5131717/files/deTanagoButtressPoisson.xsct2 86
• QSM modeOpen asset ↗zenodopdf-raw-page:4 lines:1-48Code · publicipt: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin publishOpen asset ↗zenodopdf-raw-page:5 lines:1-46Dataset · publicripts to validate results of S4 Processing scripts. 108
• Statistical plotting script: 109
https://zenodo.org/record/5131717/files/wythamAnalysis.R 110
S5 Validation scripts SimpleForest scripts to validate results of S1 Processing scripts, S2 Processing 111
scripts, S3 Processing scripts. 112
• Combined results data table: 113
https://zenodo.org/record/5131717/files/tableAll.csv 114
• Volume validation: 115
https://zenodo.org/record/5131717/files/ValidationScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code withOpen asset ↗zenodopdf-raw-page:5 lines:1-46Code · publiconScriptAll.R 116
1.2 Software 117
S1 Software Software code repository. 118
• Under the GPL version 3 license: 119
https://gitlab.com/SimpleForest/computree/-/blob/master/pluginSimpleForest/GPL_v3_template 120
• we provide source code with compilation instructions for the here presented SimpleForestv5.3.1 plugin published: 121
https://gitlab.com/SimpleForest/computree/-/commits/v5.3.1. 122
• Inside a subfolder this repository contains a Win10 compiled executable : 123
https://gitlab.com/SimpleForest/computree/-/tree/master/bin. 124
• Persistent 5.1.3: 125
https://doi.org/10.5281/zenodo.5138255 126
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CC-BY-NC 4.0 International license
available under a
was not certified by peer review) Open asset ↗gitlab · SimpleForest/computreepdf-raw-page:5 lines:1-46Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, for field-derived data, temperature response modeling bears flaws and pitfalls concerning the interpretation of derived parameters. In this study, climate data from five growing seasons with differing temperature distributions served as starting point for a growth simulation of wheat stem elongation, based on a four-parametric temperature response function (Wang-Engel) including all cardinal temperatures. In a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data. The collection of such data is common in field phenotyping platforms. To take advantage of the lack of supra-optimal temperatures during the stem elongation, simpler (linear and asymptotic) models to predict temperature-response parameters were investigated. The asymptotic model extracted the base temperature of growth and the maximum absolute growth rate with high precision, whereas simpler, linear models failed to do so. Additionally, the asymptotic model provided a proxy estimate for the optimum temperature. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. In a field study with three winter wheat varieties, significant differences were found for all three asymptotic dose-response curve parameters. We conclude that the asymptotic model based on high-resolution temperature courses is suitable to extract meaningful parameters from field-based data.
Why it matches plant phenotyping methods高解像度の温度データと低解像度の草丈データから、作物の温度応答パラメータを抽出するモデル手法が研究の中心であり、植物フェノタイピング手法に該当する。
abstractIn a novel approach, we re-extracted dose-responses from the simulation by combining high-resolution (hours) temperature courses with low-resolution (days) height data.
Reproduction assets foundThe paper's data and source code (phenotyping analysis for temperature dose-response extraction) are openly available in the ETH GitLab repository and archived in the ETH research collection.Code · publiconceptualization, Methodology, Writing - Review & Editing. Andreas Hund: Con-
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ceptualization, Supervision, Project administration, Funding acquisition, Writing - Review & Editing.
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
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https://doi.org/10Open asset ↗crop_phenotyping/htfp_data_processingpdf-raw-page:16 lines:1-23Code · publicProject administration, Funding acquisition, Writing - Review & Editing.
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Data availability
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Data and source code that support the findings of this study are openly available in the ETH gitlab reposi-
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tory at https://gitlab.ethz.ch/crop_phenotyping/htfp_data_processing and archived in the ETH
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research collection (http://doi.org/10.5905/ethz-1007-385).358
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint
this version posted July 23, 2021.
;
https://doi.org/10.1101/2021.07.23.453040
doi:
bioRxiv preprintOpen asset ↗10.5905/ethz-1007-385pdf-raw-page:16 lines:1-23Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
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fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
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montage image was then created to quickly estimate segmentation quality. The imageJ macro
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and sample images are available in the Zenodo repository,
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http://doi.org/10.5281/zenodo.4809980.573
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AI Segmentation
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The Machine learning approach is based on the “Waikato Environment for Knowledge
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Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
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classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
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middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A collection of flax accessions from Russian Federal Research Center for Bast Fiber Crops was characterised to evaluate its phenotypic diversity. 406 samples representing different morphotypes were selected for thorough quantitative assessment of various agronomic traits. We measured height, length of technical part of the stem, technical part weight, inflorescence length, number of bolls and seeds per plant, 1000 seed weight, the diameter of the stem, the number of internodes and finally, distance between internodes. The fiber quality was estimated by calculating stem slenderness, stem taperingness and elementary fiber length. The dataset was produced in a framework of a project focused on characterization of diversity of flax genotypes and phenotypes, as well as on identification of genomic regions associated with various traits, it is hosted on Figshare.
Why it matches plant phenotyping methods植物遺伝資源の多形質表現型を体系的に収集したデータセットであり、表現型データセットとして中心的な対象である。
titleA comprehensive dataset of flax ( Linum uitatissimum L.) phenotypes.
Reproduction assets foundThe paper is a Data in Brief article describing a flax phenotype dataset (406 accessions, agronomic and fiber quality traits) hosted publicly on Figshare. The Figshare link is explicitly given as the direct URL to the data and matches an allowed URL, making it a paper-specific, publicly accessible phenotype dataset.Dataset · publicear of phenotyping.
Data source location
Institution: Federal Research Center for Bast Fiber Crops
City/Town/Region: Torzhok/Tver Region
Country:Russia
Latitude and longitude for collected samples/data: 57°02′N, 34°58′E; Altitude: 165 m
Data accessibility
Repository name: Figshare
Data identification number:
Direct URL to data: https://figshare.com/s/86a68ecfacf6872ef239
Value of the Data
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The data on flax phenotypic diversity provides insight into flax domestication history and facilitates flax breeding efforts.
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Flax raw material has multiple uses in various sectors of the economy including textile, medical, food and chemical industries as a source of fiber, linseed and oil. This data haOpen asset ↗Figsharelines:47-144Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4-10 minutes to segment a maize shoot and consumes 10-20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.
Why it matches plant phenotyping methodsトウモロコシの3D点群を器官レベルに分割・注釈するツールを開発し、植物形態の表現型推定と深層学習用データ構築を技術的に支援するため、フェノタイピング手法が中心です。
abstractWe propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots.
Reproduction assets foundThe paper's authors publicly released the Label3DMaize toolkit (MATLAB source code and executable) on GitHub, which implements the paper's point cloud segmentation/annotation analysis for maize shoots. A supporting data deposit (GigaScience Database, 10.5524/100884) is cited but its URL is not among the allowed URLs,soCode · publicgmented point clouds. The segmentation algorithm and this toolkit will be extended to other crops according to their morphological characteristics, which will promote the automatic 3D point cloud segmentation of plants.
Availability of Supporting Source Code and Requirements
Project name: Label3DMaize Toolkit
Project home page: https://github.com/syau-miao/Label3DMaize.git
Source code and executable program: [ 57 ]
Operating systems: Windows
Programming languages: MATLAB
License: GNU General Public License (GPL)
RRID:SCR_021029
biotools ID: label3dmaize
Data AvailabilityOpen asset ↗Label3DMaize · syau-miao/Label3DMaizelines:277-291Code / 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 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
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https://esajournals.onlinelibrary.wiley.Open asset ↗Zenodo · 10.5281/zenodo.3991815pdf-raw-page:15 lines:1-105Dataset · publicanalyses is available on Zenodo (Marconi 2020): https://doi.org/10.5281/zenodo.3991797. The derived data set for
approximately five million trees at two NEON sites is available on Zenodo (Marconi et al. 2020): http://doi.org/10.5281/zenodo.3991815. Trait data and complete metadata are available on Zenodo (Marconi et al. 2021): https://doi.org/10.5281/zenodo.4434481.NEON data products and sources are as described in Table 1.
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Conditions
(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 · checked 9 Sept 2026
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R 2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R 2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.
Why it matches plant phenotyping methods深層学習統合micro-CT画像解析パイプラインを開発し、イネ茎の形態・倒伏抵抗性関連形質を非破壊かつ高スループットに抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits.
Reproduction assets foundThe paper's micro-CT rice culm phenotyping pipeline source code is explicitly stated to be publicly available on the authors' GitHub repository and their Crop Phenomics Group website; phenotypic data are in Supplemental Data 1 (not directly linked here).Code · publicThe source code and user guidelines are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action and https://github.com/diwu861125/diwu123456 .Open asset ↗diwu861125/diwu123456lines:329-354Code · publicthe main source code is provided in Supplemental Video 1 , Supplemental Note 2 , our Crop Phenomics Group website ( http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action ), and a GitHub website ( https://github.com/diwu861125/diwu123456 ).Open asset ↗lines:145-217Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Increased plant density markedly affects canopy morphophysiological activities and crop productivity. This study aims to model maize canopy final morphology under increased interplant competition by revising a functional-structural plant model, i.e., ADEL-Maize. A 2-year field experiment was conducted at Mengcheng, Anhui Province, China, in 2016 and 2018. A randomized complete block design of five plant densities (PDs), i.e., 4.5, 6, 7.5, 9, and 15 plants m -2 , with three replications was applied using a hybrid, i.e., Zhengdan 958. Canopy morphology at different PDs was measured with destructive samplings when maize canopy was fully expanded. The relationship of changes of organ morphology in relation to increased plant density was analyzed based on 2016 data. The ADEL-Maize was first calibrated for the hybrid at 4.5 plants m -2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field data. A heatmap visualization was shown to clearly illustrate the effects of increased plant density on final morphology of laminae, sheaths, and internodes. The logarithmic + linear equations were found to fit changes for the organ size versus increased plant density for phytomers excluding ear position or linear equations for the phytomer at ear position based on 2016 field data. The revision was then further tested independently by having achieved satisfactory agreements between the simulations and observations in canopy size under different PDs with 2018 field data. In conclusion, this study has characterized the relationship between canopy morphology and increased interplant competition for use in the ADEL-Maize and realized the simulations of final size of laminae, sheaths, and internodes, as affected by increased plant density, laying a foundation to test an ideotype for maize withstanding high interplant competition.
Why it matches plant phenotyping methodsADEL-Maizeを改訂してトウモロコシ群落形態をシミュレーションし、独立圃場データで検証しており、植物形態の取得・モデル化が研究の中心である。
abstractThe ADEL-Maize was first calibrated for the hybrid at 4.5 plants m -2 and then revised by introducing relationships identified from 2016 data, followed by independent validation with 2018 field data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll datasets generated for this study are included in the article/ Supplementary Material .Open asset ↗lines:496-519Code / 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 · 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 confirmedbioRxiv · checked 14 Sept 2026
Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics, making it difficult to understand which algorithms perform best under what circumstances. There is a need for an open evaluation benchmark to minimize differences in reported results due to data quality, forest type and evaluation metrics, and to support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the National Ecological Observatory Networks Airborne Observation Platform with multiple types of evaluation data, we created a novel benchmark dataset to assess individual tree delineation methods. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 424 field-annotated crowns, and 3,777 overstory stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as to overlapping field-annotated crowns. We provide an example submission and score for an open-source baseline for future methods.
Why it matches plant phenotyping methods個体樹冠の画像ベース delineation を評価する標準ベンチマークデータセットと評価用Rパッケージを構築しており、植物形態の抽出・比較手法が中心である。
abstractwe created a novel benchmark dataset to assess individual tree delineation methods.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · public375 developed an R package ( https://github.com/weecology/NeonTreeEvaluation_package) forOpen asset ↗weecology/NeonTreeEvaluation_packagepdf-page:21 lines:1-71Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeField / plotRootStem / branchMorphology / geometry measurementGrowth / development / phenologyRoot system architectureStress response / tolerance
Mechanical failure, known as lodging, negatively impacts yield and grain quality in crops. Limiting crop loss from lodging requires an understanding of the plant traits that contribute to lodging-resistance. In maize, specialized aerial brace roots are reported to reduce root lodging. However, their direct contribution to plant biomechanics has not been measured. In this manuscript, we use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize. These measurements demonstrate that the more brace root whorls that contact the soil, the greater their overall contribution to anchorage, but that the contributions of each whorl to anchorage were not equal. Previous studies demonstrated that the number of nodes that produce brace roots is correlated with flowering time in maize. To determine if flowering time selection alters the brace root contribution to anchorage, a subset of the Hallauer's Tusón tropical population was analyzed. Despite significant variation in flowering time and anchorage, selection neither altered the number of brace root whorls in the soil nor the overall contribution of brace roots to anchorage. These results demonstrate that brace roots provide a rigid base in maize and that the contribution of brace roots to anchorage was not linearly related to flowering time.
Why it matches plant phenotyping methodsトウモロコシの茎基部アンカレッジという植物力学形質を、非破壊の野外機械試験で定量する測定法が研究の中心であり、単なるルーチン測定ではない。
abstractwe use a non-destructive field-based mechanical test on plants before and after the removal of brace roots. This precisely determines the contribution of brace roots to establish a rigid base (i.e. stalk anchorage) that limits plant deflection in maize.
Reproduction assets foundThe paper's data availability statement explicitly deposits all raw data, processing code, and analyzed data (DARLING force-deflection phenotyping measurements) in a public authors' GitHub repository.Code · publicAll raw data, the code used to process data, and the analyzed data are available at: https://github.com/EESparksL/ab/Reneau_et_al_2020 .Open asset ↗EESparksL/ab/Reneau_et_al_2020lines:132-295Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel -1 ) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count ( R 2 = 0.52), but strong for tiller count ( R 2 = 0.86) and GS30 ( R 2 = 0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85-223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.
Why it matches plant phenotyping methodsドローン多視点画像と画像解析により、コムギの出芽・分げつ数・茎伸長を推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractWe aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery.
Reproduction assets foundThe paper publicly releases its authors' phenotyping processing code (multiview image generation, segmentation, early growth trait extraction) on ETH GitLab and secondary plot-based phenotype data (BLUEs, BLUPs, repeatability, heritability) on the ETH Research Collection. Raw UAS images are only available upon request,Code · publicfor the field management and freezing damage ratings at site FIP (all persons ETH Zurich, Zürich, Switzerland). We thank the anonymous reviewers for the thorough evaluation and constructive suggestions.
Additional Points
Source Code . Maintained source code for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentatiOpen asset ↗PhenoFly_data_processing_toolslines:218-251Code · publiccode for processing is publicly available on the ETH Zurich GitLab server ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GiOpen asset ↗PhenoFly_data_processing_tools/ImageProjectionAgisoftlines:218-251Code · publictools ); archived versions can be found in Roth [ 62 , 63 ]. The GitLab repository includes following preprocessing steps: (1) Image mask generation (Standalone Agisoft Metashape Script) ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ImageProjectionAgisoft ). (2) Image segmentation with random forest ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/ActiveLearningSegmentation ). (3) Multiview image generation ( https://gitlab.ethz.ch/crop_phenotyping/PhenoFly_data_processing_tools/MultiViewImage ). The GitLab repository furthermore includes the following trait extraction method: (4) Early growth trait extraction ( https://gitlab.ethz.ch/crop_pOpen asset ↗PhenoFly_data_processing_tools/ActiveLearningSegmentationlines:218-251Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Abstract At the moment, there are increasing trends of using deep learning for plant diseases detection. However, their implementations may be difficult in developing countries due to several reasons. First, existing deep learning models are usually trained with images with adequate resolutions. In developing countries however, with limited internet connection, models that would perform well even when data with low resolution are used are needed. Secondly, the generated models are large. Hence, most deep learning based applications are available on-line. Unfortunately, the trend for new deep learning architectures are either have larger models or require a heavy memory usage. So, models with smaller size would be preferred. In this paper, we evaluate various existing deep learning models for plant diseases detection when low resolution data are used. They are: VGGNet, AlexNet, Resnet, Xception, and MobileNet. Our focus is deep convolutional neural network (DCNN) which is commonly applied for image data. We also propose a new DCNN architecture with two branches of concatenated residual networks. It is well known that the deeper the networks the better performance of DCNN. However, DCNN with very deep networks and large number of training parameters is prone to vanishing gradient problems. One solutions for that is to apply residual networks as branches to DCNN. While it is found that increasing the branch of the networks benefit the performance, larger memory are required to train the networks. So, we apply two concatenated residual networks only. We called it Compact Networks (ComNet). We compare our method other with six popular CNN architectures. We evaluate the performance on the PlantVillage dataset and our own dataset. We collected images of tea leaves which consist of 6 classes: 5 classes of diseases that are commonly found in Indonesia and a healthy class. Our experiments show that our method is generally better than referenced DCNN networks.
Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を提案し、複数モデルおよびデータセットで性能比較・評価しており、植物状態の取得手法が中心である。
abstractWe also propose a new DCNN architecture with two branches of concatenated residual networks.
Reproduction assets foundThe paper evaluates its ComNet and reference DCNN architectures on a subset of the public PlantVillage dataset (Apple, Corn, Potato; 9,176 images), which the authors explicitly link to a public GitHub repository. The authors' own tea disease dataset is not public and requires contacting the corresponding author. No作者-пDataset · publicoding; FF and VPR validated the dataset. All authors are contributed to the data
collections. All authors read and approved the final manuscript.
Funding
This work is partially funded by INSINAS grant from the Indonesian Ministry of Research, Technology, and Higher
Education.
Availability of data and materials
The Plantvillage: https://github.com/spMohanty/PlantVillage-Dataset. The tea dataset that are used during the current
study are not publicly available due to it is in the process of agreement between Research Center for Informatics and
Research Institute for Tea and Cinchona but are available from the corresponding author on reasonable request.
Competing interests
The authors declare tOpen asset ↗spMohanty/PlantVillage-Datasetpdf-raw-page:19 lines:1-50Code / 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 confirmedCrossref · checked 14 Sept 2026
CottonMaizeLaboratory / benchtopRootStem / branch2D/3D reconstructionRoot system architecture
Abstract Aims The flow of electric current in the root-soil system relates to the pathways of water and solutes, its characterization provides information on the root architecture and functioning. We developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system. Methods A current flow is applied from the plant stem to the soil, the proposed geoelectrical approach images the resulting distribution and intensity of the electric current in the root-soil system. The numerical inversion procedure underlying the approach was tested in numerical simulations and laboratory experiments with artificial metallic roots. We validated the method using rhizotron laboratory experiments on maize and cotton plants. Results Results from numerical and laboratory tests showed that our inversion approach was capable of imaging root-like distributions of the current source. In maize and cotton, roots acted as “leaky conductors”, resulting in successful imaging of the root crowns and negligible contribution of distal roots to the current flow. In contrast, the electrical insulating behavior of the cotton stems in dry soil supports the hypothesis that suberin layers can affect the mobility of ions and water. Conclusions The proposed approach with rhizotrons studies provides the first direct and concurrent characterization of the root-soil current pathways and their relationship with root functioning and architecture. This approach fills a major gap toward non-destructive imaging of roots in their natural soil environment.
Why it matches plant phenotyping methods根圏の電流経路を非侵襲的に画像化し、根の構造・機能を推定する新規手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system.
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 · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/ERTpmlines:342-431Code · publicERT and iCSD codes, with data from this study, are maintained at https://github.com/Peruz/ERTpm and https://github.com/Peruz/icsd .Open asset ↗Peruz/icsdlines:342-431Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 14 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Quantitative assessment of the effects of forest management on tree size and shape has been challenging as there has been a lack of methodologies for characterizing differences and possible changes comprehensively in space and time. Terrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation. This data set includes 3D reconstructions of stems of Scots pine (Pinus sylvestris L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). The data set can be used in developing point clouds processing algorithms for single tree stem reconstruction and for investigating variation in stem size and shape of Scots pine trees. Additionally, it offers possibilities in characterizing the effects of various thinning treatments on stem size and shape of Scots pine trees from boreal forests. Data setZenodo https://zenodo.org/record/3701271 Data set licenseAttribution 4.0 International (CC BY 4.0)
Why it matches plant phenotyping methodsTLSと写真測量による樹幹の3D再構成データセットであり、樹幹サイズ・形状という植物形質の抽出アルゴリズム開発と評価に直接利用できるため、方法中心のデータセットとして含める。
abstractTerrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation.
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 data set includes three packed zip files that can be downloaded from
https://zenodo.org/record/3701271. The zip files include text files of stem points of each tree within
the sample plots from the three test sites.Open asset ↗Zenodopdf-page:4 lines:1-36Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move towards the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
Why it matches plant phenotyping methods植物3D点群から生物学的に妥当な骨格を抽出する計算手法を開発し、実データと合成データで既存手法と比較検証しているため、植物表現型取得・抽出法が中心である。
abstractSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies.
Reproduction assets foundThe paper's skeletonization experiments were implemented with the open-source PlantScan3D library, for which the authors provide a public GitHub URL (footnoted in the text and acknowledged as made available for public use). This is the computational tool used to produce the paper's plant point-cloud skeletonization andCode · publicthe open source implementation is available1 . NextOpen asset ↗pdf-page:3 lines:1-74Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.
Why it matches plant phenotyping methodsイネ科植物のハイパースペクトル画像から器官をセグメンテーションし、形態形質を抽出する手法を開発・評価しており、表現型取得法が研究の中心です。
abstractThis study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle).
Reproduction assets foundThe paper deposits its authors' analysis code, extracted phenotypes, and manually annotated sorghum/maize pixel data in a public GitHub repository, and its Zooniverse crowdsourcing project (used to generate the pixel annotations) is publicly accessible. Both are paper-specific, public, and actionable.Code · publicAll the R and python code implemented in this study, phenotypes extracted from segmented sorghum images, and the manually annotated sorghum and maize pixels have been deposited on GitHub at https://github.com/freemao/Sorghum_Semantic_Segmentation .Open asset ↗https://github.com/freemao/Sorghum_Semantic_Segmentationlines:85-113Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
ArabidopsisStem / branchMorphology / geometry measurementGrowth / development / phenology
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis ( Arabidopsis thaliana ). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.
Why it matches plant phenotyping methods幼苗胚軸長を画像から高速・高スループットに推定する深層学習手法の開発であり、植物形質取得が研究の中心です。
abstractTo make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method.
Reproduction assets foundThe paper's authors publicly released their full analysis code (U-Net-based hypocotyl segmentation/measurement pipeline) on GitHub and the training images used for phenotyping on Kaggle. Trained models are only available upon request and are therefore not listed as public assets.Code · publicThe code is fully open source and available at GitHub ( https://github.com/biomag-lab/hypocotyl-UNet ).Open asset ↗biomag-lab/hypocotyl-UNetlines:134-142Dataset · publicImages used for training are also available at https://www.kaggle.com/tivadardanka/plant-segmentation .Open asset ↗tivadardanka/plant-segmentationlines:268-326Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Background Epicormic branches arise from dormant buds patterned during the growth of previous years. Dormant epicormic buds remain just below the surface of trees, pushed outward from the pith during secondary growth, but maintain vascular connections. Epicormic buds can be activated to elongate into a new shoot, either through natural processes or horticultural intervention, to potentially rejuvenate orchards and restructure tree architecture. Because epicormic structures are embedded within secondary growth, tomographic approaches are a useful method to study them and understand their development. Results We apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree. Techniques include the flood fill algorithm to find the pith of the tree, edge detection to approximate the radius, and a conversion to polar coordinates to threshold and segment phyllotactic features. Intensity values from magnetic resonance imaging (MRI) of the trunk are projected onto the surface of a perfect cylinder to find the locations of traces in the "boundary image". Mathematical phyllotaxy provides a means to capture the patterns in the boundary image by modeling phyllotactic parameters. Our cherry tree specimen has the conspicuous parastichy pair (2,3), phyllotactic fraction 2/5, and divergence angle of approximately 143°. Conclusions The methods described provide a framework not only for studying phyllotaxy, but also for processing of volumetric image data in plants. Our results have practical implications for orchard rejuvenation and directed approaches to influence tree architecture. The study of epicormic structures, which are hidden within secondary growth, using tomographic methods also opens the possibility of studying genetic and environmental influences such structures.
Why it matches plant phenotyping methodsMRI画像と画像処理を組み合わせ、樹幹内部の維管束痕と葉序パターンを抽出・定量する方法が研究の中心であり、植物形態のフェノタイピング手法に該当する。
abstractWe apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree.
Reproduction assets foundThe paper's availability statement explicitly provides authors' analysis code on GitHub and the raw MRI data on figshare, both paper-specific and publicly actionable.Code · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗eithun/cherry-phyllotaxylines:174-195Dataset · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗10.6084/m9.figshare.7409843lines:174-195Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plants are as vulnerable by diseases as animals. Citrus is a major plant grown mainly in the tropical areas of the world due to its richness in vitamin C and other important nutrients. The production of the citrus fruit has been widely affected by citrus diseases which ultimately degrades the fruit quality and causes financial loss to the growers. During the past decade, image processing and computer vision methods have been broadly adopted for the detection and classification of plant diseases. Early detection of diseases in citrus plants helps in preventing them to spread in the orchards which minimize the financial loss to the farmers. In this article, an image dataset citrus fruits, leaves, and stem is presented. The dataset holds citrus fruits and leaves images of healthy and infected plants with diseases such as Black spot, Canker, Scab, Greening, and Melanose. Most of the images were captured in December from the Orchards in Sargodha region of Pakistan when the fruit was about to ripen and maximum diseases were found on citrus plants. The dataset is hosted by the Department of Computer Science, University of Gujrat and acquired under the mutual cooperation of the University of Gujrat and the Citrus Research Center, Government of Punjab, Pakistan. The dataset would potentially be helpful to researchers who use machine learning and computer vision algorithms to develop computer applications to help farmers in early detection of plant diseases. The dataset is freely available at https://data.mendeley.com/datasets/3f83gxmv57/2.
Why it matches plant phenotyping methods柑橘の健全・感染状態を画像で収集したデータセット自体が中心で、植物病害状態の画像ベース表現型判定に利用できるため。
titleA citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning.
Reproduction assets foundThis Data in Brief article presents a paper-specific public dataset of 759 citrus fruit and leaf images (healthy and diseased) used for plant disease phenotyping, hosted on Mendeley Data with an explicit public URL.Dataset · publicion of the University of Gujrat and the Citrus Research Center, Government of Punjab, Pakistan. The dataset would potentially be helpful to researchers who use machine learning and computer vision algorithms to develop computer applications to help farmers in early detection of plant diseases. The dataset is freely available at https://data.mendeley.com/datasets/3f83gxmv57/2.
Keywords
Image classification Feature extraction Feature selection pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmOpen asset ↗3f83gxmv57/2lines:1-59Code / 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 confirmedEurope PMC · OpenAlex · bioRxiv · checked 9 Sept 2026
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has been developed from using rulers and millimeter papers to the assessment of digitized images, yet it remained a labour-intensive, monotonous and time consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low quality images, produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets, not restricted to Arabidopsis thaliana . Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet , but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user. One-sentence summary A deep learning-based algorithm, providing an adaptable tool for determining hypocotyl or coleoptile length of different plant species.
Why it matches plant phenotyping methods幼植物の表現型である胚軸・子葉鞘長を画像から高スループットに推定する深層学習手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractTo make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method.
Reproduction assets foundThe paper explicitly states that the full open-source analysis code (U-Net-based hypocotyl segmentation/measurement pipeline) is available on GitHub and that the training images (annotated Arabidopsis, Sinapis, Brachypodium seedling images) are publicly available on Kaggle. Both are paper-specific, public, and directlyCode · publicThe code is fully open source and available at GitHub
(https://github.com/biomag-lab/hypocotyl-UNet).Open asset ↗biomag-lab/hypocotyl-UNetpdf-page:9 lines:1-56Dataset · publicImages used for training are also available
at https://www.kaggle.com/tivadardanka/plant-segmentation.Open asset ↗pdf-page:9 lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples ( Acer platanoides ) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset.
Why it matches plant phenotyping methods植物のTLS時系列から構造運動と概日リズムを抽出する処理フレームワークが研究の中心であり、植物個体で性能評価も実施しているため。
abstractThis study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series.
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 MATLAB tools used in delineating target point clouds are available at ResearchGate ( https://www.researchgate.net/publication/316990245_Point_cloud_cutting_scripts_for_MATLAB ).Open asset ↗ResearchGate · 316990245lines:467-475Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Manual phenotyping of rice tillers is time consuming and labor intensive, and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed. In this study, we developed a high-throughput micro-CT-RGB imaging system to non-destructively extract 739 traits from 234 rice accessions at nine time points. We could explain 30% of the grain yield variance from two tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by genome-wide association study, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at time point 9, which contained a major gene, TAC1. Significant variants associated with tiller angle were enriched in the 3'-untranslated region of TAC1. Three haplotypes for the gene were found, and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci containing associations with both vigor-related traits identified by high-throughput micro-CT-RGB imaging and yield. The superior alleles would be beneficial for breeding for high yield and dense planting.
Why it matches plant phenotyping methods高スループットなmicro-CT-RGB画像システムを開発し、イネの形態形質を多数・経時的に非破壊抽出することが研究の中心であるため、GWAS応用を含む植物フェノタイピング手法研究として含める。
abstractautomated, non-destructive methods of phenotyping rice tiller traits at a high spatial resolution and high throughput for large-scale assessment of rice accessions are urgently needed.
Reproduction assets foundThe paper deposits its rice tiller phenotyping datasets, images, and analysis source code at Dryad (doi:10.5061/dryad.gm18v5f), and makes the raw phenotypic data and CT/RGB images publicly downloadable via the HZAU plant phenomics database. Both are paper-specific, public, and actionable.Dataset · publicData collected from 234 rice accessions, including genotype ID, cultivar name, and all phenotypic traits.
Dataset S1. Rice accession information and phenotypic traits (RGB, CT, and manual traits) used in this work.
Dataset S2. GWAS results.Open asset ↗lines:207-246Dataset · publicAll the phenotypic data and images can be viewed and downloaded via the link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action by following these steps: (i) select ‘rice’; (ii) select ‘2015-tiller’ in the year section; (iii) select one of the accession IDs in the ID section and then press ‘search images’; (iv) nine CT images and nine side-view color images can be viewed and downloaded; (v) a similar process can be used to view and download phenotypic traits by pressing ‘search data’. The detailed procedure for the database is shown in Fig. S10 available at Dryad.Open asset ↗lines:207-246Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models.
Why it matches plant phenotyping methods植物の地下部バイオマスという明示的な形質を推定する汎用アロメトリーモデルを開発し、複数の機能型・林分で予測性能を検証しており、形質測定法が研究の中心です。
abstractWe compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB.
Reproduction assets foundThe paper's below-ground biomass allometry is built from the authors' Australian Individual Tree Biomass Library (Paul et al. 2017b), a public dataset deposited with a DOI and ÆKOS portal URL, which qualifies as a paper-specific public phenotype dataset. The ecoregions map and Dryad wood density database are generic/cdDataset · publicH, England JR, Davies MJ, Luck H (2017a) Measurements of stem diameter:
786 implications for individual- and stand-level errors. Environmental Monitoring and Assessment, 189, 416, 1-
787 14.
788 Paul KI, Larmour, J., Zerihun, A., et al. (2017b) Australian Individual Tree Biomass Library, Version 3.
789 10.4227/05/566629ADA95DA. http://www.aekos.org.au/dataset/223706. Obtained from Australian
790 Ecological Knowledge and Observation System Data Portal (ÆKOS, http://www.portal. aekos.org.au/), ,
Generic allometrics 38Open asset ↗aekos.org.au · 10.4227/05/566629ADA95DApdf-layout-page:38 lines:1-43Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest ( Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.
Why it matches plant phenotyping methodsNIRSを用いて植物組織から表現型関連情報を非破壊・高スループットに取得し、複雑形質を予測する手法自体が研究の中心である。
abstractusing near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits
Reproduction assets foundThe paper's NIRS spectra, phenotypic and SNP datasets are publicly deposited in the INRA Dataverse repository (DOI 10.15454/MB4G3T), and the authors' R functions for cross-validation prediction comparisons are on GitHub (visegura/PS). Supplemental material (including File S1 with variance-partition results) is on FigshDataset · publicThe datasets generated during and/or analyzed during the current study are available in the INRA Dataverse repository ( https://data.inra.fr/ ). They can be accessed with the following link http://dx.doi.org/10.15454/MB4G3T .Open asset ↗INRA Dataverse · 10.15454/MB4G3Tlines:66-74Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Abstract. The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.
Why it matches plant phenotyping methods植物根系の三次元画像化と活動根密度の推定を目的とした非侵襲的センシング手法を提案し、合成データおよび圃場データで検証しているため、植物フェノタイピング手法が中心である。
abstractIn this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM).
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 · publicMeasured and simulated raw data, electrical imaging, and MALM data used to generate the
figures can be accessed at https://doi.org/10.5281/zenodo.1464825 (Mary et al., 2018).Open asset ↗Zenodo · 10.5281/zenodo.1464825lines:872-946Code / 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 · 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 · OpenAlex · checked 10 Sept 2026
Background Maize (Zea mays ssp. mays) is 1 of 3 crops, along with rice and wheat, responsible for more than one-half of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping are currently the largest constraints on plant breeding efforts. Datasets linking new types of high-throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision-based tools. Findings A set of maize inbreds-primarily recently off patent lines-were phenotyped using a high-throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high-density genotyping and scored for a core set of 13 phenotypes in field trials across 13 North American states in 2 years by the Genomes 2 Fields Consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released. Conclusions Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors that influence yield plasticity.
Why it matches plant phenotyping methods高スループット画像データセットの公開と、画像測定を手動測定と比較検証することが中心であり、植物形態形質の抽出・評価に直接関係するため。
abstractA total of 485 GB of image data including RGB, hyperspectral, fluorescence, and thermal infrared photos has been released.
Reproduction assets foundThe paper's phenotyping image dataset (RGB, hyperspectral, fluorescence, thermal) is publicly deposited at GigaScience Database (doi 10.5524/100371); a subset of RGB images is also available at plantvision.unl.edu/dataset; the authors' validation/analysis source code is publicly posted on GitHub (Maize_Phenotype_Map).Dataset · publicA subset of the RGB images within this dataset were previously analyzed in Choudhury et al. [ 18 ], and were made available for download from http://plantvision.unl.edu/dataset under the terms of the Toronto Agreement.Open asset ↗lines:399-408Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Wheat (Triticum aestivum L.) is an important food crop, and biotic and abiotic stresses significantly impact grain yield. Wheat leaf and stem surface waxes are associated with traits of biological importance, including stress resistance. Past studies have characterized the composition of wheat cuticular waxes, however protocols can be relatively low-throughput and narrow in the range of metabolites detected. Here, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems. Further, waxes from four wheat cultivars were assayed to evaluate the potential for GC-MS metabolomics to describe wax composition attributed to differences in wheat genotype. A total of 263 putative compounds were detected and included 58 wax compounds that can be classified (e.g., alkanes and fatty acids). Many of the detected wax metabolites have known associations to important biological functions. Principal component analysis and ANOVA were used to evaluate metabolite distribution, which was attributed to both tissue type (leaf, stem) and cultivar differences. Leaves contained more primary alcohols than stems such as 6-methylheptacosan-1-ol and octacosan-1-ol. The metabolite data were validated using scanning electron microscopy of epicuticular wax crystals which detected wax tubules and platelets. Conan was the only cultivar to display alcohol-associated platelet-shaped crystals on its abaxial leaf surface. Taken together, application of GC-MS metabolomics enabled the characterization of cuticular wax content in wheat tissues and provided relative quantitative comparisons among sample types, thus contributing to the understanding of wax composition associated with important phenotypic traits in a major crop.
Why it matches plant phenotyping methodsGC-MSメタボロミクスを用いた植物表面ワックス組成の包括的な取得・比較を主題とし、SEMによる検証も行っているため、化学的な植物形質の測定法として中心的です。
abstractHere, gas chromatography-mass spectrometry (GC-MS) metabolomics methods were utilized to provide a comprehensive characterization of the chemical composition of cuticular waxes in wheat leaves and stems.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following are available online at http://www.mdpi.com/1422-0067/19/2/249/s1 . Figure S1. Wax density.docx provides a semi-quantitative analysis of wheat epicuticular wax density using image processing tools on SEM micrographs; Table S1. Wax metabolite annotations.txt provides detailed information on detected metabolites.Open asset ↗lines:522-564Code / dataset availability confirmedbioRxiv · Europe PMC · checked 15 Sept 2026
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
Why it matches plant phenotyping methods高スループットのマイクロCT-RGB画像システムを開発し、イネの形態形質を多数・時系列で抽出することが研究の中心であるため。GWASはその応用にあたる。
abstractwe developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points.
Reproduction assets foundThe paper's own micro-CT-RGB phenotyping outputs (CT images, side-view RGB images, and extracted phenotypic traits for 234 rice accessions at 9 time points) are explicitly stated to be publicly viewable and downloadable from the authors' Huazhong Agricultural University plant phenotyping database. RiceVarMap is an exteDataset · public276
277
Phenotyping database extracted by HCR at 9 time points
278
During the tillering stage, 234 rice plants were automatically measured by HCR at 9
279
different development time points (once every 3 d, starting from 41 ~ 67 d after
280
sowing). All the phenotypic data and images can be viewed and downloaded via the
281
link http://plantphenomics.hzau.edu.cn/checkiflogin_en.action and then following
282
these steps: (1) select ‘rice’; (2) select ‘2015-tiller’ in the year section; (3) select one
283
of the accession IDs in the ID section and then press ‘search images’; (4) 9 CT images
284
and 9 side-view color images can be viewed and downloaded; (5) a similar process
285
can be used to viOpen asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:10 lines:1-76Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This paper provides synthesis methods for large-scale semantic image segmentation datasets of agricultural scenes with the objective to bridge the gap between state-of-the art computer vision performance and that of computer vision in the agricultural robotics domain. We propose a novel methodology to generate renders of random meshes of plants based on empirical measurements, including the automated generation per-pixel class and depth labels for multiple plant parts. A running example is given of Capsicum annuum (sweet or bell pepper) in a high-tech greenhouse. A synthetic dataset of 10,500 images was rendered through Blender, using scenes with 42 procedurally generated plant models with randomised plant parameters. These parameters were based on 21 empirically measured plant properties at 115 positions on 15 plant stems. Fruit models were obtained by 3D scanning and plant part textures were gathered photographically. As reference dataset for modelling and evaluate segmentation performance, 750 empirical images of 50 plants were collected in a greenhouse from multiple angles and distances using image acquisition hardware of a sweet pepper harvest robot prototype. We hypothesised high similarity between synthetic images and empirical images, which we showed by analysing and comparing both sets qualitatively and quantitatively. The sets and models are publicly released with the intention to allow performance comparisons between agricultural computer vision methods, to obtain feedback for modelling improvements and to gain further validations on usability of synthetic bootstrapping and empirical fine-tuning. Finally, we provide a brief perspective on our hypothesis that related synthetic dataset bootstrapping and empirical fine-tuning can be used for improved learning.
Why it matches plant phenotyping methods植物部位のセマンティックセグメンテーション用の合成・実画像データセットと生成手法を開発し、性能比較・検証可能な形で公開しており、植物画像から部位を抽出する方法が中心である。
abstractWe propose a novel methodology to generate renders of random meshes of plants based on empirical measurements, including the automated generation per-pixel class and depth labels for multiple plant parts.
Reproduction assets foundThe paper publicly releases its synthetic and empirical Capsicum annuum image datasets (with annotations) via a 4TU/Centre DOI, explicitly stated in the conclusion.Dataset · publicur experiments. Segmentation results
show a promising next step for semantic part localisation in agriculture.
Future efforts should be aimed in further optimising the network ar-
chitectures, focussing on the performance of the infrequent classes. The
datasets and their source material are publicly released and can be
found at: https://doi.org/10.4121/uuid:884958f5-b868-46e1-b3d8-a0b5d91b02c0
Acknowledgements
This research was partially funded by the European Commission in
the Horizon2020 Programme (SWEEPER GA No. 644313). The authors
would like to thank prof.dr. R. D. Howe and dr. D. Perrin for their input
of this research and making computing resources available. The authors
declare that tOpen asset ↗10.4121/uuid:884958f5-b868-46e1-b3d8-a0b5d91b02c0pdf-raw-page:12 lines:81-119Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Background In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. Results We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. Conclusions The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.
Why it matches plant phenotyping methodsトウモロコシの穂と絹糸の成長形質を高スループットに取得する画像・ロボティクス・機械学習パイプラインを開発し、精度検証と遺伝子型・水分欠 deficitへの適用を行っているため、植物表現型計測法が中心である。
abstractWe have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks.
Reproduction assets foundThe paper's ear/silk phenotyping pipeline code (eartrack) is publicly available on GitHub with documentation, and the authors deposited subsets of the whole-plant images and ear images with the Ilastik project/outputs on Zenodo. All are paper-specific, public, and actionable.Code · publicalie Luchaire, Benoît Suard, Thomas Laisné, Luciana Galizia, Alexandra Manset-Sarcos, Awaz Mohamed and Adel Meziane for their help in conducting the experiment.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The source code and examples are available on Github ( https://github.com/openalea/eartrack ) under an Open Source license (CeCILL-C). It has been integrated as a reusable package in the OpenAlea platform [ 54 , 55 ]. User and developer documentation is also available at http://eartrack.readthedocs.io . A subset of whole plant images is available at https://zenodo.org/record/1002675 and a subset of ear images, IlastikOpen asset ↗openalea/eartracklines:170-209Code · publico competing interests.
Availability of data and materials
The source code and examples are available on Github ( https://github.com/openalea/eartrack ) under an Open Source license (CeCILL-C). It has been integrated as a reusable package in the OpenAlea platform [ 54 , 55 ]. User and developer documentation is also available at http://eartrack.readthedocs.io . A subset of whole plant images is available at https://zenodo.org/record/1002675 and a subset of ear images, Ilastik project and outputs are available at https://zenodo.org/record/1002173 . It requires Python 2.7 and OpenCV libraries.
Consent for publication
All the authors have approved the manuscript and have made all requiOpen asset ↗lines:170-209Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Background Crop species are of increasing interest both for cattle feeding and for bioethanol production. The degradability of the plant material largely depends on the lignification of the tissues, but it also depends on histological features such as the cellular morphology or the relative amount of each tissue fraction. There is therefore a need for high-throughput phenotyping systems that quantify the histology of plant sections. Results We developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners. The procedure results in an automated segmentation of the input images into distinct tissue regions. The size and the fraction area of each tissue region can be quantified, as well as the average coloration within each region. The measured features can discriminate contrasted genotypes and identify changes in histology induced by environmental factors such as water deficit. Conclusions The simplicity and the availability of the software will facilitate the elucidation of the relationships between the chemical composition of the tissues and changes in plant histology. The tool is expected to be useful for the study of large genetic populations, and to better understand the impact of environmental factors on plant histology.
Why it matches plant phenotyping methodsトウモロコシ茎切片の組織形態を画像処理で自動分割・定量する手法を開発しており、植物表現型の取得・抽出が研究の中心である。
abstractWe developed custom image processing and an analysis procedure for quantifying the histology of maize stem sections coloured with FASGA staining and digitalised with whole microscopy slide scanners.
Reproduction assets foundThe paper's image segmentation/quantification workflow is publicly released as an ImageJ/Fiji plugin (QuantifFasga) on GitHub, and the authors' in-house Matlab statistical analysis library (MatStats) is also publicly available on GitHub. The phenotype measurement data themselves are only available upon request.Code · publicThe code for the segmentation of tissue regions and the quantification of histology is freely available on the Internet through the GitHub platform at http://github.com/ijpb/fasga-quantif/releases (last accessed: August 8, 2017).Open asset ↗ijpb/fasga-quantiflines:651-651Code / dataset availability confirmedbioRxiv · Europe PMC · checked 14 Sept 2026
How complex developmental-genetic networks are translated into organs with specific 3D shapes remains an open question. This question is particularly challenging because the elaboration of specific shapes is in essence a question of mechanics. In plants, this means how the genetic circuitry affects the cell wall. The mechanical properties of the wall and their spatial variation are the key factors controlling morphogenesis in plants. However, these properties are difficult to measure and investigating their relation to genetic regulation is particularly challenging. To measure spatial variation of mechanical properties, one must determine the deformation of a tissue in response to a known force with cellular resolution. Here we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties. Unlike classical extensometers, ACME is mounted on a confocal microscope and utilizes confocal images to compute the deformation of the tissue directly from biological markers, thus providing cellular scale information and improved accuracy. ACME is suitable for measuring the mechanical responses in live tissue. As a proof of concept we demonstrate that the plant hormone gibberellic acid induces a spatial gradient in mechanical properties along the length of the Arabidopsis hypocotyl.\n\nTerms
Why it matches plant phenotyping methods植物組織の力学的性質を細胞解像度で定量する自動共焦点マイクロ伸展計を開発し、画像から変形を抽出する方法を中心に実証しているため。
abstractHere we present an automated confocal micro-extensometer (ACME), which greatly expands the scope of existing methods for measuring mechanical properties.
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 positioners are controlled by a SmarAct MCS3D (SmarAct GmbH) controller (Figure 1C,
label 15) accompanied by its software library, which in turn is controlled by custom-made software
(available here: https://github.com/ACME-Robinson/InstallPackage)Open asset ↗ACME-Robinson/InstallPackagepdf-page:14 lines:1-54Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 10 Sept 2026
Maize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds - primarily recently off patent lines - were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.
Why it matches plant phenotyping methods高速画像・ハイパースペクトル等を用いた植物表現型データセットの構築と、画像測定値を手動測定と比較する技術的検証が中心であるため、収載する。
abstractA total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released.
Reproduction assets foundThe paper releases ~485 GB of maize phenotyping image data (RGB, hyperspectral, fluorescence, thermal) publicly at plantvision.unl.edu/dataset, and the authors' validation/analysis source code is posted on GitHub (https://github.com/shanwai1234/Maize Phenotype Map). Both are paper-specific, public, and actionable.Dataset · publicA subset of the RGB images within
this dataset were previously analyzed in18 , and were made available for download from http://plantvision.unl.edu/dataset under
the terms of the Toronto Agreement.Open asset ↗pdf-page:6 lines:1-51Code · publicSource codes for all validation analysis are posted online (https://github.com/shanwai1234/Maize Phenotype Map).Open asset ↗shanwai1234/Maizepdf-page:6 lines:1-51Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The vulnerability of plant water transport tissues to a loss of function by cavitation during water stress is a key indicator of the survival capabilities of plant species during drought. Quantifying this important metric has been greatly advanced by noninvasive techniques that allow embolisms to be viewed directly in the vascular system. Here, we present a new method for evaluating the spatial and temporal propagation of embolizing bubbles in the stem xylem during imposed water stress. We demonstrate how the optical method, used previously in leaves, can be adapted to measure the xylem vulnerability of stems. Validation of the technique is carried out by measuring the xylem vulnerability of 13 conifers and two short-vesseled angiosperms and comparing the results with measurements made using the cavitron centrifuge method. Very close agreement between the two methods confirms the reliability of the new optical technique and opens the way to simple, efficient, and reliable assessment of stem vulnerability using standard flatbed scanners, cameras, or microscopes.
Why it matches plant phenotyping methods植物の茎木部の脆弱性を画像で定量する新規光学法を開発し、既存法との比較検証を行っており、植物状態の取得手法が中心である。
abstractHere, we present a new method for evaluating the spatial and temporal propagation of embolizing bubbles in the stem xylem during imposed water stress.
Reproduction assets foundThe paper's optical vulnerability image-capture and analysis scripts are publicly available at the authors' OpenSourceOV site; the caviplace URL is a facility page, not a data/code asset.Code · publicnd could be filtered from slow movements caused by drying. Thresholding of image differences allowed automated counting of cavitation events using the analyze-stack function in ImageJ. Full details, including an overview of the technique, image processing, as well as scripts to guide image capture and analysis, are available at http://www.opensourceov.org .
A time-resolved count of cavitations in each stem, quantified as the number of pixels per event during stem drying, was compiled, and this was converted to a percentage of total pixels cavitated. The psychrometer output was then used to determine a fitted function that described the change in stem water potential over time. TOpen asset ↗www.opensourceov.orglines:175-179Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
A major challenge in plant systems biology is the development of robust, predictive multiscale models for organ growth. In this context it is important to bridge the gap between the, rather well-documented molecular scale and the organ scale by providing quantitative methods to study within-organ growth patterns. Here, we describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs that does not require adding markers at the organ surface. The method allows for the simultaneous analysis of root and hypocotyl growth, provides spatio-temporal information on curvature, growth anisotropy and relative elemental growth rate and can cope with complex organ movements. We demonstrate the performance of the method by documenting previously unsuspected complex growth patterns within the growing hypocotyl of the model species Arabidopsis thaliana during normal growth, after treatment with a growth-inhibiting drug or in a mechano-sensing mutant. The method is freely available as an intuitive and user-friendly Matlab application called KymoRod.
Why it matches plant phenotyping methods植物器官の成長パターン、曲率、成長異方性、相対元素成長率を自動抽出する手法とソフトウェアを開発しており、植物表現型取得が研究の中心です。
abstractwe describe a simple method for the analysis of the evolution of growth patterns within rod-shaped organs
Reproduction assets foundThe paper's KymoRod Matlab application for automated kinematic analysis of rod-shaped plant organs is explicitly stated to be freely available on the authors' public GitHub repository (ijpb/KymoRod). This is the authors' analysis code implementing the paper's phenotyping method. No public phenotype dataset or image de-Code · publicwe have presented a simple and robust
method for the analysis of sub-organ growth patterns in
plant seedlings. Its performance exceeds that of previous
methods that are mostly too laborious for the analysis of
large numbers of samples. The method is packaged in
KymoRod, a user-friendly application freely available on
internet (http://github.com/ijpb/KymoRod), which should
facilitate the study of the cellular basis of organ growth for
non-specialist users.
EXPERIMENTAL PROCEDURES
Plant growth, image acquisition and pre-treatment
Arabidopsis seeds (genotypes Col-0 and fer-4; Duan et al., 2010)
were surface sterilized (Santoni et al., 1994), plated on Arabidop-
sis medium (Santoni etOpen asset ↗ijpb/KymoRodpdf-raw-page:6 lines:1-205