Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Why it matches plant phenotyping methods植物表面の三次元形状を測定するオープンな構造化光プラットフォームと再構成・解析ワークフローを開発し、葉で投影バイアスを評価しており、表現型取得手法が中心である。
abstractHere we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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The reconstructed surfaces supporting this study are available at Zenodo under
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https://doi.org/10.5281/zenodo.22167250.54
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Source data for all graph panels are provided with this paper; for panels showing
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rendered surfaces, the underlying reconstructions are in the same record.
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The analysis notebooks, environment specifications, derived data and per-panel
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source data are available at Zenodo under
1Open asset ↗Zenodo · 10.5281/zenodo.22167250pdf-raw-page:35 lines:1-52Code · public1151
Source data for all graph panels are provided with this paper; for panels showing
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rendered surfaces, the underlying reconstructions are in the same record.
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The analysis notebooks, environment specifications, derived data and per-panel
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source data are available at Zenodo under
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https://doi.org/10.5281/zenodo.22167598.55
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The reconstruction software, build documentation and minimal working examples
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are available at https://doi.org/10.5281/zenodo.22167471.56
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The software and analysis notebooks are released under the MIT licence and the
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hardware design files under CERN-OHL-P v2. The visible-light platform described
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hereOpen asset ↗Zenodo · 10.5281/zenodo.22167598pdf-raw-page:35 lines:1-52Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Code / dataset availability confirmedbioRxiv · checked 5 Sept 2026
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedOpenAlex · checked 11 Sept 2026
Unmanned aerial vehicle (UAV) photogrammetry offers a cost-effective approach to tree-level detection, however, Structure-from-Motion (SfM) outputs are sensitive to processing choices and site conditions, which can alter canopy representation and reduce individual-tree detection accuracy. Here, we systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection under controlled acquisition conditions. Objectives were to (i) identify an optimal SfM-derived point-cloud configuration for delineating individual trees, and (ii) implement and test a segmentation workflow (local-maxima treetop detection plus Dalponte2016 in lidR) for detecting and counting trees. We assessed RGB-only SfM for individual-tree detection (ITD) across thirteen 1.21-ha loblolly pine ( Pinus taeda ) plots located in two counties in the state of Alabama in the southeastern United States; eight even-aged plantations and five mixed pine-hardwood stands, while holding image acquisition parameters constant. Using Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia), dense-cloud quality (Lowest, Low, Medium, High, Ultra High) and depth-map filtering (Disabled, Mild, Moderate, Aggressive) were varied in a 5 × 4 full-factorial design; assessment metrics included point-cloud density, canopy-surface completeness, canopy-height-model (CHM) agreement with field heights, and ITD precision/recall/F1. We identified a single high-resolution configuration (Ultra High + Disabled) by screening parameter sets for structural accuracy and suppression of false peaks. Using this configuration, CHMs matched field heights in Washington County, Alabama (R 2 = 0.96; RMSE = 0.44 m; bias = − 0.01 m) and in Cullman County, Alabama (R 2 = 0.44; RMSE = 1.14 m; bias = − 0.09 m); pooled performance was R 2 = 0.98; RMSE = 0.54 m; bias = − 0.01 m. ITD accuracy at the primary 3 m match radius yielded a precision of 0.03; recall = 0.29; F1 = 0.05 in the even-aged plantations (Washington) and a precision of 0.03; recall = 0.12; F1 = 0.05 in mixed pine–hardwood stands (Cullman); pooled F1 = 0.05. The selected parameters and workflow are reproducible and transferable, provide insight into RGB-SfM ITD performance, and indicate when lidar remains preferable for crown delineation.
Why it matches plant phenotyping methodsRGB-SfMによる個体樹の検出・樹高推定と、SfM設定およびセグメンテーションワークフローの系統的評価が研究の中心であり、植物の樹冠構造・樹高という形態形質を抽出する方法を検証している。
abstractwe systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection
Reproduction assets foundThe paper's Code availability statement deposits the authors' SfM/ITD processing scripts publicly on OSF (DOI 10.17605/OSF.IO/UXBCZ). Phenotype/field datasets are only available on request, so they are not public assets.Code · publicThe workflow and processing scripts used in this study are publicly available through the Open Science Framework
(OSF) repository: Singh and Narine, [32]. Code Repository for Optimizing SfM Parameters for RGB-Only Individual-Tree
Detection in Loblolly Pine and Mixed Pine-Hardwood Stands. https://doi.org/10.17605/OSF.IO/UXBCZ.Open asset ↗10.17605/OSF.IO/UXBCZpdf-page:12 lines:1-70Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Spaceborne laser scanning (SLS) presents a cost-effective means for frequent, global-scale monitoring of forest ecosystem parameters. Compared to airborne laser scanning (ALS), SLS offers substantially greater spatial coverage and revisit frequency, but at the cost of larger footprints, sparser sampling, and attenuated return signals. These constraints typically result in a loss of fine-scale vertical canopy structure in large-footprint waveform LiDAR, thereby limiting the retrieval of ecologically meaningful forest structural metrics. To address this challenge, we developed an encoder–decoder Transformer architecture to reconstruct high-resolution vertical canopy profiles from large-footprint waveform LiDAR observations. Using waveform data acquired by NASA’s Land, Vegetation, and Ice Sensor (LVIS) – a high-altitude ALS instrument commonly used as a proxy for spaceborne missions – we trained the model to recover fine-scale canopy structure by leveraging overlapping ALS point clouds as reference data. The proposed Transformer leverages long-range vertical dependencies within waveform signals to infer canopy structural details that are degraded or unresolved in large-footprint, high-altitude observations. Results show that the proposed approach substantially improves the agreement between LVIS-derived and ALS-derived canopy structural complexity metrics, increasing correlations from R = 0.62 to 0.84 and from R = 0.76 to 0.90 for two representative metrics. This framework is readily transferable to current and future SLS missions, enabling the retrieval of super-resolved vertical canopy profiles and supporting large-area assessment of ecologically meaningful canopy structural metrics.
Why it matches plant phenotyping methodsLiDAR波形から植物キャノピーの垂直構造プロファイルを再構成するTransformer手法の開発と検証が研究の中心であり、植物構造形質を推定している。
abstractwe developed an encoder–decoder Transformer architecture to reconstruct high-resolution vertical canopy profiles from large-footprint waveform LiDAR observations.
Reproduction assets foundThe paper's data availability statement provides two paper-specific public assets: the complete codebase including the best-performing Transformer model checkpoint on GitHub, and the preprocessed LVIS waveforms with corresponding ALS reference canopy profiles on Zenodo. Both are directly used for this paper's canopy-geCode · publicoach could help extend ALS-like structural characterization to broader
734 spatial extents sampled by spaceborne laser scanning.
735 Data and code availability
736 The complete codebase for training and implementing the proposed encoder–decoder
737 Transformer, including the best-performing model checkpoint, is available at
738 https://github.com/tahriribraq/Transformer-waveform-reconstruction. The preprocessed
739 LVIS waveforms and corresponding ALS reference profiles used in the study are
740 available at https://doi.org/10.5281/zenodo.21154804.
741 Acknowledgements
742 This work was supported by the National Aeronautics and Space Administration’s
743 (NASA) Decadal Survey Incubation (DSIOpen asset ↗https://github.com/tahriribraq/Transformer-waveform-reconstructionpdf-layout-page:38 lines:1-48Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.
Why it matches plant phenotyping methods柑橘果実の疎視野CT再構成法を開発・検証し、内部・外部形質を自動抽出するフェノタイピングが中心である。
abstractThis study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting.
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 · publicOur codes are available at https://github.com/Petrichoror/CitrusGS .Open asset ↗Petrichoror/CitrusGSlines:325-387Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · Crossref · checked 15 Sept 2026
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
R ! .
Because the fitted plane passes through the centroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Code / dataset availability 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 confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedOpenAlex · 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 confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Why it matches plant phenotyping methodsマイクロCTと3D再構成で葉の閉鎖運動・曲率を定量化し、幾何モデルでサイズ–曲率関係を推定することが研究の中心であり、植物形態・運動状態のフェノタイピング手法に該当する。
abstractHere, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R 2 = 0.9965) for the Precision Mode and 0.0307 (R 2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.
Why it matches plant phenotyping methodsイネ群落の3D構造を構築・推定する手法を開発し、放射伝達モデルと実測スペクトルで検証しており、植物形質の取得・再現が研究の中心です。
abstractthis study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies.
Reproduction assets foundThe paper's Data Availability statement says the collected phenotype/structural/spectral data are publicly available on the authors' GitHub repository (allowed URL), while the analysis code is only available from the corresponding author upon request (request_only, no public URL).Dataset · publicThe data collected and used in this study are publicly available at: https://github.com/baijc4095-code/2024data . The code used for analysis can be obtained from the corresponding author upon reasonable request.Open asset ↗baijc4095-code/2024datalines:240-256Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitively labor-intensive. We present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters, providing a more comprehensive representation of the plant’s architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we propose a method that generates token sequences containing a procedural definition of the plant architecture. This work uses only synthetic images for training and testing, where “exact” architectural parameters were known, which allowed for testing of the hypothesis that organ-level architectural parameters could be extracted from imagery data using a vision language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. Then, a VLM was trained to predict plant architecture token sequences from images. Our results demonstrate that the model can predict plant architecture tokens with an F1 score of 0.73 in a teacher-forcing method. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. Our model achieves lower MAPE than feature regression-based methods in estimating bulk plant-level traits that require understanding of the occluded 3D structure of the plant, such as leaf count and leaf area. We conclude that generating plant architecture and parameter extraction from synthetic imagery are feasible using a VLM approach, supporting future extension to real imagery.
Why it matches plant phenotyping methods画像から器官レベルの植物構造と形態形質を抽出するVLM手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractWe present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters
Reproduction assets foundThe paper's footnotes explicitly state that the authors' code is available on GitHub and the synthetic cowpea image/XML dataset is available on Hugging Face, both paper-specific and publicly actionable. The Helios URL is a generic third-party simulator library, not a paper-specific asset.Code · public1. ^ Code is available at: https://github.com/GEMINI-Breeding/Image2PlantArchitecture .Open asset ↗GEMINI-Breeding/Image2PlantArchitecturelines:600-676Dataset · public2. ^ Dataset is available at: https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML .Open asset ↗heesup/Cowpea-Architecture-XMLlines:600-676Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Why it matches plant phenotyping methods小麦穂の体積を画像・3D再構成・知識蒸留で推定する手法の開発と性能評価が中心であり、高スループット植物フェノタイピングへの応用も明示されている。
abstractWe therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Code / 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 confirmedOpenAlex · checked 15 Sept 2026
ABSTRACT 3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine‐scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1‐score, mask intersection‐over‐union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1‐score of 86.1%. Pine seedlings yielded higher recall and F1‐scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.
Why it matches plant phenotyping methods針葉樹苗の形状・テクスチャを取得する3D画像再構成ワークフローを開発・比較・定量評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments.
Reproduction assets foundThe paper's Data Availability Statement states that the raw seedling image data and finalized textured meshes (the paper's phenotyping/3D reconstruction inputs and outputs) are freely available on Zenodo under DOI 10.5281/zenodo.19823955, which appears in the allowed URL list.Dataset · publicand without adjusting the scanning parameters, while also re-
Data Availability Statement
taining texture and color. In contrast to prior approaches that
require manual intervention or do not preserve visual informa- Raw image data and finalized textured meshes are freely available at
Zenodo.org with https://doi.org/10.5281/zenodo.19823955.
tion, the proposed workflow enables a semi-automated recon-
struction process suitable for dataset generation. As shown, the
methodology is effective for the digital reconstruction of small References
and slender structured flexible objects and holds potential for
Abbood, S. A., H. A. Ajjah, A. H. H. Alboabidallah, M. U. MohaOpen asset ↗Zenodopdf-layout-page:14 lines:50-74Code / 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 confirmedOpenAlex · Crossref · checked 13 Sept 2026
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Efficient large-scale 3D reconstruction of orchard environments is essential for robotic inspection and precision agriculture, yet existing methods struggle with unstructured scenes, variable illumination, and computational bottlenecks. We propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots. The pipeline integrates an RGB-D-based data acquisition strategy using ORB-SLAM3, which is enhanced by a dense mapping module for robust large-scale pose estimation and point cloud generation. A divide-and-conquer strategy is then employed: individual plant views are extracted via a YOLO-World-based detection and 3D matching algorithm, followed by plant-specific reconstruction using an improved 3D Gaussian Splatting (3DGS) method incorporating depth regularization and region-aware refinement. Experimental results in citrus orchards demonstrate that InspectGaussian achieves 96% average precision and 93% recall in plant view extraction, while surpassing state-of-the-art methods in reconstruction fidelity (31.226 PSNR, 0.915 SSIM, 0.067 LPIPS) and point cloud accuracy (7 mm error). These results confirm its effectiveness in capturing fine structural and textural details while maintaining scalability and efficiency. This framework provides a practical solution for high-throughput, in-field plant phenotyping and lays the foundation for intelligent orchard monitoring and management.
Why it matches plant phenotyping methods植物個体の3D再構成とRGB-D・検出・Gaussian Splattingを統合した手法開発であり、植物の構造的形質取得を目的とするため、フェノタイピング手法が中心である。
abstractWe propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots.
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub. Phenotype datasets (RGB-D orchard image sequences, LiDAR point clouds, manual trait measurements) are only available upon request, so they do not qualify as public assets.Code · publicOur code are available at https://github.com/zlhzau/InspectGaussian.git .Open asset ↗zlhzau/InspectGaussianlines:489-515Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This data article describes a curated RGB-Depth image dataset captured using an Intel RealSense D435 stereo depth camera mounted on an autonomous mobile platform during field deployments at commercial baby broccoli farms in Victoria, Australia. The dataset comprises 1759 paired RGB images (640 × 480 pixels) and corresponding 16-bit depth frames acquired under both daytime (natural sunlight) and night-time (LED illumination) conditions, designed to support research in agricultural computer vision and robotic harvesting. Images were selected from 39,765 raw acquisitions through a reproducible Python curation pipeline applying quality filtering (blur detection, brightness thresholds, corruption detection), perceptual hash-based duplicate removal, and manual review. The final dataset includes 924 daytime and 835 night-time image pairs containing baby broccoli plants at various growth stages. The dataset provides RGB camera intrinsic parameters and pixel-aligned depth maps to enable 3D point cloud reconstruction. Potential applications include developing deep learning models for crop detection and segmentation, validating depth-based size estimation methods, and benchmarking illumination-robust vision systems. All data and curation code are publicly available under a CC BY 4.0 license.
Why it matches plant phenotyping methodsRGB-Depth画像データセットの構築と再現可能なキュレーションを中心とし、作物検出に加えてサイズ推定という植物形質の評価・ベンチマークに利用できるため。
titleA field-acquired RGB-Depth image dataset for computer vision-based baby broccoli detection and size estimation under varying illumination conditions.
Reproduction assets foundThe paper is a data article describing a public Mendeley Data repository containing the authors' field-acquired RGB-D baby broccoli image dataset (1759 image pairs, ground truth diameter annotations, camera intrinsics, and curation/annotation code), directly reproducing the paper's phenotyping measurements and analysisDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/px5p6zdk6k.3
Direct URL to data: https://data.mendeley.com/datasets/px5p6zdk6k/3Open asset ↗Mendeley Data · 10.17632/px5p6zdk6k.3html-lines:95-155Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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The authors declare that there are no conflicts of interest regarding the publication of this paper.
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code · public1003/1)
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Code / 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 · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedOpenAlex · arXiv · checked 13 Sept 2026
Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches based on manual scanning or fixed camera setups remain a major bottleneck, while active-mapping methods based solely on occupancy grids are too coarse for accurate trait estimation. To address this gap, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The system integrates OctoMap with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. A low-resolution OctoMap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, while 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize 3D Gaussians for high-fidelity scene reconstruction. We further introduce a robust mapping strategy that mitigates semantic segmentation and depth noise, together with a background pruning method that reduces memory and computational cost. We validate our framework across simulated, laboratory, and real greenhouse scenes, showing consistent improvements across three state-of-the-art Gaussian Splatting backbones. In simulation, where ground-truth geometry is available, our approach outperforms occupancy-based mapping in both reconstruction accuracy and runtime efficiency: compared with a 0.01m-resolution OctoMap, it doubles the fruit-level F1 score under noisy conditions while achieving up to a threefold reduction in runtime. Beyond simulation, novel-view synthesis quality also improves consistently in laboratory and real greenhouse environments, with PSNR and mIoU improving by up to 1.5 dB and 18%, respectively. Finally, the reconstructed semantic maps enable fruit counting and volume estimation with accuracies approaching 80%.
Why it matches plant phenotyping methods園芸ロボット向けの3D再構成・能動マッピング手法を開発し、果実の計数・体積推定という植物形質の取得に適用・検証しているため、フェノタイピング手法が中心的です。
titleOctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots
Reproduction assets foundThe paper's supplementary material is hosted on the authors' public project page (jrcuaranv.github.io/octosplat), and the authors state that all code and data are publicly available. The SimSense repository is a third-party depth-sensor simulator tool, not a paper-specific asset.Code · publicAll code and data are publicly available to facilitate reproducibility.Open asset ↗lines:59-163Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Code / dataset availability 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 confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / 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 confirmedarXiv · OpenAlex · checked 15 Sept 2026
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度も検証しているため、植物フェノタイピング手法が中心です。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are $\leq95\%$ occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methodsリンゴの姿勢推定アノテーションを大幅に効率化する3D再構成・自動ラベル投影パイプラインを開発し、姿勢推定性能も評価しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
Reproduction assets foundThe paper explicitly provides two paper-specific public assets: the authors' phenotyping/pose-estimation pipeline code on GitHub and the collected apple orchard image dataset on a 4TU DOI. Both are directly used for the paper's measurements and analysis.Dataset · publicIn total, 367 images were collected. The dataset is available at https://doi.org/10.4121/976c94f2-028f-4291-adfd-20eb82b0f647Open asset ↗10.4121/976c94f2-028f-4291-adfd-20eb82b0f647lines:92-108Code / 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 confirmedOpenAlex · Crossref · checked 6 Sept 2026
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments.
Why it matches plant phenotyping methods植物の3次元形態を取得・特徴づけるステレオビジョンと深度推定システムの開発が中心であり、明確な植物フェノタイピング手法です。
abstractThis paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants.
Reproduction assets foundThe paper's Data Availability Statement openly deposits the original study data (the 544 stereo RGB maize images and related phenotyping data) on OSF at a DOI, which is a paper-specific, publicly actionable asset. No author analysis code repository is explicitly stated.Dataset · publicData Availability Statement: The original data presented in the study are openly available in OSF at
https://doi.org/10.17605/OSF.IO/MN6P9.Open asset ↗OSF · 10.17605/OSF.IO/MN6P9pdf-page:18 lines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Code / dataset availability confirmedCrossref · OpenAlex · checked 6 Sept 2026
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Vegetation vertical structure refers to the 3D distribution of vegetation aboveground biomass. Vegetation vertical structure of tropical forests influences other ecological and environmental variables that are essential for the functioning of the ecosystems. Integrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020. We mapped canopy height, the height of half the cumulative returned energy from GEDI (RH50), total canopy cover, foliage height diversity, and total plant area index. The resulting maps tended to have the highest errors in the Amazon and Andean regions. Total cover had the highest relative error. Interrelationship curves between forest structural metrics of GEDI footprints are maintained across mapped metrics, indicating that the predictive models preserve structural relationships observed in GEDI data. Due to the medium-high spatial resolution and national coverage of the forest structural maps presented in this work, these maps will be useful for evaluating and mapping other ecological variables and conservation priorities in Colombia.
Why it matches plant phenotyping methodsGEDI LiDAR・マルチスペクトル・SARを統合し、森林キャノピー高、被覆率、葉群高多様性、植物面積指数などの植物構造形質を全国規模で推定・検証することが中心であり、単なる生態学的応用ではない。
abstractIntegrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020.
Reproduction assets foundThe paper's resulting forest vertical structure maps (CH, COVER, FHD, PAI, RH50 for Colombia, 2020) are publicly available on Zenodo and via Google Earth Engine assets, and the authors' analysis code is publicly available on GitHub. These are paper-specific, public, actionable assets.Code · publicCode availability
The code is publicly accessible on Github76: https://github.com/CamiloFaguaUNAL/Forest_Structure_Colombia.Open asset ↗GitHubhtml-lines:731-755Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Abstract High‐throughput and noninvasive phenotyping methods are promising technology for improving efficiency in plant research and breeding. In this study, we evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice ( Oryza sativa ). The DPS was used to estimate plant height, biomass, color, leaf morphology, and tiller angle in four rice varieties (Koshihikari, Nipponbare, PL9, and Tachiaoba). The results show high accuracy and correlation between manually measured and DPS‐derived traits. Notably, the 3D volume analysis can quantify biomass accumulation and growth dynamics and revealed distinct differences among varieties. The strong correlation between the green‐red normalized difference index (a red‐green‐blue‐based index) and soil plant analysis development also demonstrated the viability of the system in monitoring leaf color without using a multispectral instrument. The analysis also captured growth patterns over time, including canopy development and senescence, which are often challenging to quantify through manual measurements alone. Furthermore, the tiller angle estimation derived from DPS provided an alternative method to plant architecture evaluation, demonstrating its potential for use in breeding programs aimed to optimize canopy structure. These findings establish DPS as a reliable and scalable tool for a digital phenotyping platform that enables comprehensive trait analysis with reduced labor and increased precision and the capability to continuously monitor plant growth and biomass accumulation. This study shows the potential of this novel digital tool for automating manual measurements, which can increase efficiency and expedite research and breeding in rice and other crops.
Why it matches plant phenotyping methods3Dモデル再構築に基づくデジタル表現型解析システムを開発・評価し、イネの複数形質を手測定と比較検証しているため、方法が研究の中心です。
abstractwe evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice
Reproduction assets foundThe paper's data availability statement explicitly says the analysis code is openly available on GitHub at the authors' repository Rice_VTGa.O, which contains the digital phenotyping/leaf-tracing analysis code for this study. No phenotype dataset or image deposit is stated.Code · publicGrant Number 39 [2023] and 38 [2024]),
and Microbiome and Metabolome Control Project, University
of Miyazaki, Japan.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Rice_VTGa.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Kenji Aoki https://orcid.org/0000-0001-7003-1994
MasatsuguHashiguchi https://orcid.org/0000-0003-0637-2780
RyoAkashi https://orcid.org/0000-0002-5651-8285
Yuji Kishima https://orcid.org/0000-0002-0942-3371
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154Open asset ↗https://github.com/sandysan42/Rice_VTGa.O · Rice_VTGa.Opdf-raw-page:13 lines:1-84Code / dataset availability confirmedCrossref · checked 14 Sept 2026
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
With the continuous progress in micro-optical machine technology, miniature spectral imaging devices have been rapidly developed; however, three-dimensional (3D) imaging measurement technology has become increasingly mature and widely used. The evolution of these technologies has established a robust foundation for the integration of three-dimensional imaging and spectral information. To achieve accurate alignment between 3D data and spectral information to obtain a more comprehensive spectral representation of objects in 3D space, we developed a binocular multispectral stereo imaging (BMSI) system. This system acquires images in synchrony with a binocular multispectral imager, thereby ensuring accurate alignment between 3D data and spectral data at the pixel level and facilitating the construction of a four-dimensional (4D) dataset. The segmentation of leaf regions from shadow backgrounds in two distinct plant species was achieved through optimal band fusion and hue-saturation value (HSV) color space transformation, significantly improving the segmentation accuracy, processing efficiency, and robustness across different plant species. A systematic evaluation was conducted to quantify the reconstruction precision and system stability at different measurement distances. The designed system acquired 4D image spectral data with plants as the objects to be tested. The distribution characteristics of chlorophyll (Chl) on the 3D surface of plants were obtained by first-order derivatives of the spectral data and the normalized difference red edge (NDRE) index. This technique provides a new means for plant phenotyping research and a more effective technical approach for the digitalization and precision monitoring of the agricultural industry.
Why it matches plant phenotyping methods植物の3D・マルチスペクトル画像取得、葉領域分割、再構成精度・安定性評価を中核とする新規フェノタイピングシステムの開発研究である。
abstractwe developed a binocular multispectral stereo imaging (BMSI) system.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw data and essential source code for the BMSI plant phenotyping analysis on a public GitHub repository.Code · publicThe raw data and the essential parts of the source code have been uploaded to Github: https://github.com/wwxsoul1234/BMSI/tree/master.Open asset ↗wwxsoul1234/BMSI · BMSIhtml-lines:278-306Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Mass spectrometry imaging (MSI) is a vital tool in botanical research. Image fusion is introduced for resolution enhancement of MSI data from animal samples, but its application to plant MSI data resulted in unsatisfactory visualizations due to the distinct morphological characteristics of plant tissues. Herein, this study presents loss controlled residual network (LCRN), a workflow dedicated to the super‐resolution fusion of plant MSI data. The pipeline used a residual connection‐based neural network implemented with a novel loss metric called edge perceptual loss. Edge perceptual loss is developed for evaluating complex morphological information that can not be properly reflected by common image metrics, and its implementation in loss propagation is vital to the quality of the fusion result. Compared to existing deep learning‐based methods, LCRN is able to generate a high‐quality super‐resolution fusion image of extra high magnification (up to 20‐fold) that combined chemical and morphological information obtained from MSI and microscopy, respectively.
Why it matches plant phenotyping methods植物組織のMSIデータを対象に、化学情報と形態情報を統合して超解像画像を生成する画像融合ワークフローを開発しており、植物形態の取得・抽出手法が研究の中心である。
abstractHerein, this study presents loss controlled residual network (LCRN), a workflow dedicated to the super‐resolution fusion of plant MSI data.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe data that support the findings of this study are available in the supplementary material of this article. Codes are available at https://github.com/codexyster/LCRN‐pr .Open asset ↗codexyster/LCRN‐prlines:245-245Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published19 Nov 2025ISPRS Journal of Photogrammetry and Remote SensingCited by 3 · OpenAlex ↗
Accurate fruit shape reconstruction under real-world field conditions is essential for high-throughput phenotyping, sensor-based yield estimation, and orchard management. Existing approaches based on 2D imaging or explicit 3D reconstruction often suffer from occlusions, sparse views, and complex scene dynamics as a result of the plant geometries. This paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards. The proposed method integrates (1) Grounded-SAM2 for multi-object tracking and segmentation (MOTS), (2) photogrammetric structure-from-motion for 3D scene reconstruction, and (3) DeepSDF, an implicit neural representation, for completing occluded fruit geometries with a neural network. We furthermore propose a new MOTS evaluation protocol to assess tracking performance without requiring ground truth annotations. Experiments conducted in both controlled laboratory conditions and an operational apple orchard demonstrate the accuracy of our 3D fruit reconstruction at the centimeter level. The Chamfer distance error of the proposed shape completion method using the DeepSDF shape prior reduces this to the millimeter level, and outperforms the traditional method, while Grounded-SAM2 enables robust fruit tracking across challenging viewpoints. The approach is highly scalable and applicable to real-world agricultural scenarios, offering a promising solution to reconstruct complete fruits with visibility higher than 10% for precise 3D fruit phenotyping at a large scale under occluded conditions.
Why it matches plant phenotyping methods果実形状を対象とするUAV画像・3D再構成・形状補完法を開発し、実験で精度評価しており、植物表現型取得が研究の中心である。
abstractThis paper presents a novel UAV-based monocular 3D panoptic mapping framework for robust and scalable fruit shape completion in orchards.
Reproduction assets foundThe paper's authors publicly release their UAV orchard video data, lab 3D apple scans, and analysis code via a GitHub repository explicitly stated in the text. A Zenodo deposit (10.5281/zenodo.15635994) is also mentioned for the data, but its URL is not among the allowed URLs, so only the GitHub asset is reported.Code · publicing in orchard environments,(2) to propose a novel method to evaluate
MOTS without any annotations, and (3) to provide a highly accurate
3D apple dataset collected in a laboratory environment, along with
UAV-captured high-resolution videos in the field. The dataset and codes
for this research are publicly available at: https://github.com/Kaiwen-Robotics/Mono3DOrchard.2. Study area and materials
This study contains two data collection areas: field data collection
and laboratory data collection.
2.1. Field data collection
2.1.1. Study area
The field data collection was conducted within an apple orchard
located in Randwijk, Overbetuwe, the Netherlands (51.9376, 5.703057
in WGS84 UTM 31U), as shOpen asset ↗Kaiwen-Robotics/Mono3DOrchard.2pdf-raw-page:2 lines:75-128Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 32 000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image matching across different land cover types. Mean completeness ranged from 64 % for “glacial and perpetual snow” to 98 % for “sealed surfaces”, with a value of 93 % for the “closed forest” class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.08 to 0.16 m, with a normalized median absolute deviation (NMAD) of around 0.8 m and a maximum root mean square error (RMSE) of 1.20 m. Similar accuracies are obtained when comparing historical DSMs with measured geodetic points. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very-high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史的航空画像から植生高モデルを生成する自動写真測量法を開発・精度評価し、森林の高さ変化という植物キャノピー形質を抽出しているため、測定法が中心的である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's countrywide DSMs, VHMs, and auxiliary rasters (matching mask, vegetation mask, metadata shapefile) for four epochs are deposited publicly on EnviDat with an explicit DOI. These vegetation height models are the paper's plant/canopy phenotyping measurements. No author analysis code or trained models are namedDataset · publicDatasets can be accessed from EnviDat ( https://doi.org/10.16904/envidat.528 , Marty et al., 2024). The following files are available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models (VHMs).Open asset ↗Envidat · 10.16904/envidat.528lines:249-256Code / dataset availability confirmedarXiv · checked 13 Sept 2026
AI-driven crop health mapping systems offer substantial advantages over conventional monitoring approaches through accelerated data acquisition and cost reduction. However, widespread farmer adoption remains constrained by technical limitations in orthomosaic generation from sparse aerial imagery datasets. Traditional photogrammetric reconstruction requires 70-80\% inter-image overlap to establish sufficient feature correspondences for accurate geometric registration. AI-driven systems operating under resource-constrained conditions cannot consistently achieve these overlap thresholds, resulting in degraded reconstruction quality that undermines user confidence in autonomous monitoring technologies. In this paper, we present Ortho-Fuse, an optical flow-based framework that enables the generation of a reliable orthomosaic with reduced overlap requirements. Our approach employs intermediate flow estimation to synthesize transitional imagery between consecutive aerial frames, artificially augmenting feature correspondences for improved geometric reconstruction. Experimental validation demonstrates a 20\% reduction in minimum overlap requirements. We further analyze adoption barriers in precision agriculture to identify pathways for enhanced integration of AI-driven monitoring systems.
Why it matches plant phenotyping methods作物の健康状態マップ作成を目的とする航空画像のオルソモザイク生成法を開発し、重複率低減を実験検証しており、画像取得・再構成手法が中心である。
titleOrtho-Fuse: Orthomosaic Generation for Sparse High-Resolution Crop Health Maps Through Intermediate Optical Flow Estimation
Reproduction assets foundThe paper explicitly states that the authors' code and dataset (aerial imagery used for orthomosaic generation and crop health analysis) are publicly available at the project page https://rugvedkatole.github.io/OrthoFUSE/, which is an allowed URL. This qualifies as a paper-specific public asset containing the authors' Code · publicThe code and dataset are available at https://rugvedkatole.github.io/OrthoFUSE/Open asset ↗https://rugvedkatole.github.io/OrthoFUSE/lines:1-54Code / 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 confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Accurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies. While RGB-D cameras enable high-throughput scanning in operational settings like harvesting conveyors, they produce incomplete, low-quality 3D models. Conversely, close-range Structure-from-Motion (SfM) produces high-quality reconstructions but is not suitable for high-throughput field application. This study bridges this gap through 3DPotatoTwin , a paired dataset containing 339 tuber samples across three cultivars collected in Hokkaido, Japan. Our dataset uniquely combines: (1) conveyor-acquired RGB-D point clouds, (2) ground measurement, (3) SfM reconstructions under indoor controlled environment, and (4) aligned model pairs with transformation matrices. The multi-sensory alignment employs an semi-supervised pin-guided pipeline incorporating single-pin extraction and referencing, cross-strip matching, and binary-color-enhanced ICP, achieving 0.59 ± 0.11 mm registration accuracy. Beyond serving as a benchmark for 3D phenotyping algorithms, the dataset enables training of 3D completion networks to reconstruct high-quality 3D models from partial RGB-D point clouds. Meanwhile, the proposed semi-automated annotation pipeline has the potential to accelerate 3D dataset generation for similar studies. The presented methodology demonstrates broader applicability for multi-sensor data fusion across crop phenotyping applications. The dataset and pipeline source code are publicly available at HuggingFace and GitHub, respectively.
Why it matches plant phenotyping methodsジャガイモ塊茎の3D表現型計測を対象に、RGB-D・SfM・地上計測を統合したデータセット、位置合わせパイプライン、ベンチマークを開発しており、表現型取得手法が中心である。
abstractAccurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the batch processing scripts mentioned in this section were provided in the 3dscan folder at Github (https://github.com/UTokyo-FieldPhenomics-Lab/PotatoScan/).Open asset ↗UTokyo-FieldPhenomics-Lab/PotatoScanhtml-lines:119-131Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R 2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.
Why it matches plant phenotyping methodsマルチビュー画像によるイネ幼苗の3D再構成プラットフォームとデータセットを開発し、再構成精度および抽出形質を実測値と検証しており、表現型取得手法が研究の中心である。
abstractdeep learning-based feature extraction and matching methods are employed in this study
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub; the phenotype/image dataset is only available upon request, so it does not qualify as a public asset.Code · publicThe code used in this study is available at https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.git .Open asset ↗https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.gitlines:433-485Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method named closed-loop coarse-to-fine method with multi-masked matching (CLCFM3). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the closed-loop coarse-to-fine method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information.
Why it matches plant phenotyping methods植物フェノタイピングのためのフォトグラメトリ画像から高精度3D点群を再構成する手法を開発しており、表現型取得法が研究の中心である。
abstractThis research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology.
Reproduction assets foundThe authors explicitly deposit the MMM/CLCFM analysis code and scripts in a public GitHub repository, which also provides download links to the supporting image and 3D point cloud data used in this paper's soybean phenotyping reconstructions. The supplementary material contains only result figures, not datasets. Gene/NCode · publicThe computer codes and scripts of MMM and CLCFM are deposited in a GitHub repository at https://github.com/tanasoft/MMM-CLCFM (accessed on 3 September 2025). Download links to image and 3D point cloud data that support the findings of this study are also provided at this GitHub repository.Open asset ↗https://github.com/tanasoft/MMM-CLCFMlines:116-305Code / 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 · 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 confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 6 Sept 2026
Understanding the genetic basis of quantitative traits related to crop growth, yield, and stress response requires the acquisition of large-scale, high-quality phenotypic datasets. High-throughput phenotyping platforms have become effective tools for meeting this requirement. Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads, their operational flexibility, and their proximity to crops, which allows for higher imaging resolution. In this study, we introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions. The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field phenotyping, with integrated visual and satellite navigation systems enabling autonomous operation. We validated the performance of the robot through a series of experiments involving various crop canopies. By capturing RGB images of rice and wheat, we independently performed wheat ear detection and rice panicle segmentation. For wheat ear detection, we achieve a precision of 0.783, a recall of 0.822, and a mean average precision (mAP) of 0.853 when the YOLOv8m model is used. For rice panicle segmentation, the SegFormer_B0 model yielded a mean intersection over union (mIoU) of 0.949 and an accuracy of 0.987. Additionally, by capturing RGB-D data of maize canopies, we performed 3D reconstructions to calculate plant height, achieving an R 2 of 0.99 compared with manual measurements. Similar experiments with rapeseed yielded an R 2 of 0.97. Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories, with classification accuracies ranging from 0.977 to 0.996. Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
Why it matches plant phenotyping methods圃場用自律ロボットと複数の画像・分光センシング、形質抽出手法を開発し、作物キャノピーで性能検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce PhenoRob-F (a phenotyping robot for the field), a cross-row, wheeled robot designed for efficient and automated phenotyping under field conditions.
Reproduction assets foundThe paper's data availability statement explicitly links a public GitHub repository containing part of the data and code supporting this PhenoRob-F phenotyping study; remaining data are available on request.Code · publicPart of the data and code supporting this study are openly available with the following link: https://github.com/balloonhaha/PhenoRob-F. All other reasonable requests for data and research materials will be fulfilled upon contacting the corresponding authors.Open asset ↗balloonhaha/PhenoRob-Fhtml-lines:193-220Code / 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 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
As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5-70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R 2 ) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51-56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques' data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems.
Why it matches plant phenotyping methodsトマト果実の遮蔽点群を補完し、果実の長さ・幅・高さを推定する新規アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractThis study proposes an adaptive symmetry self-matching (ASSM) algorithm.
Reproduction assets foundThe paper's Data Availability Statement deposits the study's tomato/eggplant fruit point cloud data on ScienceDB, a public repository, making the paper-specific phenotyping data (3D point clouds of 150 tomato fruits used for completion and trait measurement) publicly actionable.Dataset · publicData Availability Statement
The data are available online at https://doi.org/10.57760/sciencedb.25084 .Open asset ↗sciencedb · 10.57760/sciencedb.25084lines:312-345Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
3D phenotyping of the external and internal structures is important to breed new fruit species. As manual phenotyping is error-prone and time-consuming, developing high-throughput solutions with enhanced precision and low costs is necessary. This study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images. The input X-rays are arranged in orthogonal pairs to provide additional information, and customized loss functions enable more effective learning of the mapping from 2D X-ray features to 3D CT volumes. Experimental results show that 6 views can generate high-quality citrus CT volumes, with a structural similarity index of 92.1 % and a peak signal-to-noise ratio of 26.374 dB compared with the real CT models. Moreover, the morphology of the generated model can be conveniently measured in the 3D space, facilitating the extraction of phenotypic traits including fruit length, width, height, volume, surface area, peel thickness, number of segments, and edible rate with high precision. As X-rays can be obtained using low-cost X-ray machines with high efficiency, the proposed method can be potentially developed into high-throughput equipment for fruit production lines or portable devices to realize in-field phenotyping.
Why it matches plant phenotyping methods柑橘の疎視野X線から3D CTモデルを再構成し、形態形質を抽出する手法を開発・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。
abstractThis study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and datasets (the citrus X-ray/CT phenotyping dataset and CitrusGAN analysis code) are publicly available at the authors' GitHub repository.Code · publicData availability
The code and datasets are available at https://github.com/Petrichoror/CitrusGAN . Other data will be made available on request.Open asset ↗Petrichoror/CitrusGANlines:244-347Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Quantitative descriptions of the complete canopy architecture are essential for accurately evaluating crop photosynthesis and yield performance to guide ideotype design. Although various sensing technologies have been developed for three-dimensional (3D) reconstruction of individual plants and canopies, they failed to obtain an accurate description of canopy architectures due to severe occlusion among complex canopy architectures. We proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model. A complete point cloud generation framework was developed for automated annotation of the training dataset by distinguishing surface points from occluded points within canopies. The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points. To further enhance feature extraction, a dynamic graph convolutional feature extractor (DGCFE) module was proposed to capture structural variations over the whole rapeseed growth period. The results demonstrated that CP-PCN achieved chamfer distance (CD) values of 3.35 cm -4.51 cm over four growth stages, outperforming the state-of-the-art transformer-based method (PoinTr). Ablation studies confirmed the effectiveness of the MRDG and DGCFE modules. Moreover, the validation experiment demonstrated that the silique efficiency index developed from CP-PCN improved the overall accuracy of rapeseed yield prediction by 11.2% compared to that of using incomplete point clouds. The CP-PCN pipeline has the potential to be extended to other crops, significantly advancing the quantitatively analysis of in-field population canopy architectures.
Why it matches plant phenotyping methods作物群落キャノピーの3D形態を復元する点群補完法を開発し、既存法との比較、アブレーション、収量予測への有効性検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractWe proposed an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops with a novel point cloud completion model.
Reproduction assets foundThe paper's availability statement explicitly deposits all source code and test data (rapeseed canopy point cloud completion, CP-PCN) on GitHub at the allowed URL.Code · publicn Wang, Yi Feng,
Mengjie Gong and Guangyu Wu, for their participation in the experiments, and
to the Jiaxing Academy of Agricultural Sciences for their assistance with the
experimental data acquisition.
Availability of supporting data and source code
All source codes and test data involved in this study are available on
GitHub (https://github.com/Ziyue-Guo/RP-PCN.git).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests
or personal relationships that could have appeared to influence the work
reported in this paper.
Contributions
Z. G. designed the study, conducted the experiments, and wrote the
manuscript. Y. S. contributed to the expeOpen asset ↗Ziyue-Guo/RP-PCNpdf-layout-page:42 lines:1-42Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Natural biocomposites such as wood and plant cell walls exhibit remarkable mechanical properties largely attributed to their nanoscale chiral organization of fibrous components, such as cellulose. However, resolving the three-dimensional (3D) arrangement of these structures at the nanoscale remains a significant challenge, particularly in beam-sensitive materials. This study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution. By acquiring low-dose SED data at multiple tilt angles and applying a symmetry-based reconstruction algorithm, we resolved the 3D orientation of cellulose fibrils in native oat husk and birch wood. Our results reveal a multilayered cell wall architecture with alternating helical handedness, providing precise measurements of 3D fibril orientation. This method reveals complex hierarchical structures at the nanoscale, enabling rapid data acquisition and analysis using widely available instrumentation. The ability to resolve such chiral organization opens new understanding of materials properties as well as opportunities for the design of bio-inspired materials with tunable mechanical and functional properties.
Why it matches plant phenotyping methods植物細胞壁中のセルロース fibril の3D配向を定量マッピングする画像計測・再構成法が研究の中心であり、植物構造形質の取得手法を開発している。
abstractThis study introduces a method for 3D reconstruction of orientation based on scanning electron diffraction (SED), enabling the quantitative mapping of chiral supramolecular organization with sub-100 nm spatial resolution.
Reproduction assets foundThe article's Data and Code Availability statement declares that the SED datasets (diffraction data from oat husk and birch wood) and the authors' custom Python analysis script are publicly available on Zenodo (DOI: 10.5281/zenodo.15647651). This is a paper-specific, public, actionable asset directly reproducing the 3DDataset · publicData and Code Availability
SED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code · publicSED data and Python script for SED data analysis used in this study are available from Zenodo
(DOI: 10.5281/zenodo.15647651).Open asset ↗Zenodo · 10.5281/zenodo.15647651pdf-page:19 lines:1-36Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Today, leaf trait estimation remains a labor-intensive process. The effort to obtain ground truth measurements limits how accurately this task can be performed automatically. Traditionally, plant scientists manually measure the traits of harvested leaves and associate them with sensor data, which is key for training machine learning approaches and to automate the processes. In this paper, we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping. We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton, which is automatically extracted. We use the generated leaves to fine-tune different leaf trait estimation methods. We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software. Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves. Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves' traits, making our data crucial for developing and testing data-driven trait estimation methods. Accurate trait estimation is key to understanding crop growth, productivity, and pest resistance, as leaf size directly influences photosynthesis, yield potential, and vulnerability to insects and fungal growth.
Why it matches plant phenotyping methods葉の形質推定を支援するため、形質付き合成3D点群を生成するニューラルネットワーク手法とデータセットを開発・評価しており、フェノタイピング手法が中心である。
abstractwe propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.
Reproduction assets foundThe paper uses two public 3D plant point-cloud datasets (Pheno4D and BonnBeetClouds3D) as real-world inputs for training/evaluating its leaf trait estimation and generation pipeline; both have explicit public URLs. The authors' code is only promised ('We plan to make our code publicly available'), so it is not yet an aDataset · publicWe use two publicly available datasets. Pheno4D [43] is available at the url: https://www.ipb.uni-bonn.de/data/pheno4d/index.html. It contains maize and tomato plants measured daily, over 12 and 20 days respectively.Open asset ↗Pheno4Dhtml-lines:401-424Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Forest phenotypic responses are significantly influenced by extreme climate conditions, particularly canopy structure and photosynthetic traits. However, the underlying mechanisms driving these responses, especially in conifer species, remain poorly understood. This study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis, quantifying changes in key physiological traits that light interception, gas exchange parameters stomatal conductance, and chlorophyll content. Developing 3D reconstruction algorithms tailored to conifer canopies is essential for simulating forest ecosystem responses under varying canopy densities. We investigate the following questions: (1) How does thinning affect canopy light penetration and photosynthetic efficiency? Thinning significantly increased light penetration from 15 % (CK) to 22 %, enhancing photosynthetic efficiency, resulting in an 18 % increase in carbon absorption under drought conditions. (2) How does reduced-rainfall affect photosynthetically active radiation (PAR) and stomatal conductance? Reduced-rainfall caused a 12 % decrease in PAR, a 20 % reduction in stomatal conductance, and an 8 % decrease in chlorophyll content. (3) What are the synergistic effects of thinning and reduced-rainfall in carbon absorption? Thinning under reduced-rainfall increased carbon absorption by 25 %. This study reveals a significant correlation between chlorophyll content, leaf nitrogen content, and canopy structural dynamics under drought and elevated temperature conditions, offering new insights into the adaptive mechanisms plants employ to adjust their photosynthetic processes. In conclusion, the development of 3D reconstruction algorithms tailored for conifer canopies, in regulating photosynthetic traits, is crucial for improving forest adaptation, contributing to functional trait-based forest management and ecosystem modeling.
Why it matches plant phenotyping methods針葉樹林冠の3D再構成アルゴリズム開発と生理形質推定が明示されており、植物表現型取得法が研究の中心的要素である。
abstractThis study employs advanced phenotyping technologies, combining three-dimensional (3D) canopy reconstruction with high-resolution physiological trait analysis
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets generated during the study (phenotype/physiological measurements and 3D canopy reconstruction outputs) in a public GitHub repository with an authors' URL, making it a paper-specific, publicly actionable asset.Dataset · publicThe datasets generated during this study are available in the GitHub repository: https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025 .Open asset ↗https://github.com/wuchunyanhehe/Plant-Phenomics-Wu-2025lines:270-306Code / dataset availability confirmedCrossref · checked 13 Sept 2026
To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep learning model size and the completion of the overall shape of the corn. In this work, the 3D corn models derived from segmentation are employed to systematically output the fragmented point cloud data in batches. The Shape Coding PointAttN (SCPAN) algorithm is also proposed, which is based on PointAttN. The model’s structure is simplified to output sparse point clouds and minimize computational complexity, and a gated multilayer perceptron (MLP) containing 3D position coding is introduced to enhance the model’s spatial awareness. In addition, the prior shape encoder module is initially trained and subsequently integrated into the model to enhance its focus on shape characteristics. Compared to the original model, PointAttN, SCPAN achieves a 34.2% reduction in the number of parameters, and the inference time is reduced by 30 ms while maintaining comparable accuracy. The experimental results show that the proposed method can complete the corn point cloud more effectively, using a small model to help estimate the pose and dimensions of corn accurately. This work supports the precise phenotypic analysis of corn and similar crops, such as citrus and tomatoes, and promotes the development of smart agricultural technology.
Why it matches plant phenotyping methodsトウモロコシの遮蔽点群を補完し、形状・姿勢・寸法を推定する計算手法の開発が中心であり、植物表現型取得への応用も明示されている。
abstractTo obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm
Reproduction assets foundThe paper's Data Availability Statement points to an authors' GitHub repository for the corn point cloud completion code (SCPAN). The phenotype dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publiccorresponding author upon reasonable request. The related code will be released at https://github.Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
The accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity and advancing precision forestry, yet it remains challenging in dense plantations due to canopy occlusion and LiDAR limitations. In this study, we extended the scope of conventional point cloud completion techniques to artificial planted forests by introducing a novel approach called Multi−feature Fusion Completion of Populus (MFCPopulus). Specifically designed for Populus Tomentosa plantations with uniform spacing, this method utilized a dataset of 1050 manually segmented trees with expert−validated trunk−canopy separation. Key innovations include the following: (1) a hierarchical adversarial framework that integrates multi−scale feature extraction (via Farthest Point Sampling at varying rates) and biologically informed normalization to address trunk−canopy density disparities; (2) a structural characteristics split−collocation (SCS−SCC) strategy that prioritizes crown reconstruction through adaptive sampling ratios, achieving a 94.5% canopy coverage in outputs; (3) a cross−layer feature integration enabling the simultaneous recovery of global contours and a fine−grained branch topology. Compared to state−of−the−art methods, MFCPopulus reduced the Chamfer distance variance by 23% and structural complexity discrepancies (ΔDb) by 33% (mean, 0.12), while preserving species−specific morphological patterns. Octree analysis demonstrated an 89−94% spatial alignment with ground truth across height ratios (HR = 1.25−5.0). Although initially developed for artificial planted forests, the framework generalizes well to diverse species, accurately reconstructing 3D crown structures for both broadleaf (Fagus sylvatica, Acer campestre) and coniferous species (Pinus sylvestris) across public datasets, providing a precise and generalizable solution for cross−species trees’ phenotypic studies.
Why it matches plant phenotyping methods個体樹冠の3D点群補完・再構成手法を開発し、樹冠構造や形態形質の定量化に有効性を検証しているため、植物フェノタイピング手法が中心である。
abstractThe accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo record (13255198) from which part of the tree point cloud data used in this study was sourced. This is a paper-specific, publicly accessible phenotyping input dataset (tree point clouds). No author analysis code, trained model checkpoints, or other paperDataset · publicData Availability Statement: The data presented in this study were partly sourced from the follow-
ing publicly available resource: https://zenodo.org/records/13255198 (accessed on 6 December
2024).Open asset ↗zenodo · 13255198pdf-page:23 lines:1-59Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 6 Sept 2026
With the rapid advancements in computer vision and deep learning, multi-view stereo (MVS) based on conventional RGB cameras has emerged as a promising and cost-effective tool for botanical research. However, existing methods often struggle to capture the intricate textures and fine edges of plants, resulting in suboptimal 3D reconstruction accuracy. To overcome this challenge, we proposed Edge_MVSFormer on the basis of TransMVSNet, which particularly focuses on enhancing the accuracy of plant leaf edge reconstruction. This model integrates an edge detection algorithm to augment edge information as input to the network and introduces an edge-aware loss function to focus the network’s attention on a more accurate reconstruction of edge regions, where depth estimation errors are obviously more significant. Edge_MVSFormer was pre-trained on two public MVS datasets and fine-tuned with our private data of 10 model plants collected for this study. Experimental results on 10 test model plants demonstrated that for depth images, the proposed algorithm reduces the edge error and overall reconstruction error by 2.20 ± 0.36 mm and 0.46 ± 0.07 mm, respectively. For point clouds, the edge and overall reconstruction errors were reduced by 0.13 ± 0.02 mm and 0.05 ± 0.02 mm, respectively. This study underscores the critical role of edge information in the precise reconstruction of plant MVS data.
Why it matches plant phenotyping methods植物の葉のエッジと3D形状を高精度に再構築するMVS手法を開発・評価しており、植物表現型取得の方法が中心である。
titleEdge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the dataset (private multi-view plant images with ground truth point clouds/depth maps) and the code used in this study are publicly available on Zenodo, with the URL matching an allowed URL.Code · publicThe dataset and code used in this study are publicly available at the webpage https://zenodo.org/records/15086606 with a DOI: 10.5281/zenodo.15086606, accessed on 19 March 2025.Open asset ↗zenodo · 10.5281/zenodo.15086606lines:95-266Code / dataset availability confirmedOpenAlex · arXiv · checked 14 Sept 2026
This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.
Why it matches plant phenotyping methods植物フェノタイピング施設向けに、固定カメラ画像からNeRFで植物の3D点群を再構成する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities.
Reproduction assets foundThe paper explicitly releases its full SC-NeRF dataset (raw 4K videos, frames, COLMAP poses, NeRF checkpoints, and final 10M-point clouds for six plant/produce objects) on Hugging Face, and states that all datasets and the authors' code are available at the project page. Both are paper-specific, public, and actionable.Code · publicd delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines. We provide all datasets and our code, available at https://baskargroup.github.io/SC-NeRF/
Figure 1 : Schematic of the stationary camera imaging system for NeRF-based point cloud reconstruction in high-throughput plant phenotyping. In this setup, each plant is conveyed to a rotating turntable marked against a matte black background. Over a full 30-second rotation, a tripod-mounted stationary camera captures high-resoOpen asset ↗lines:1-53Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
How robust three-dimension (3D) organ shape emerges during morphogenesis is a fundamental question in biology. Addressing this question requires a comprehensive quantification of organ geometry in 3D. To tackle these issues, we considered the sepal of Arabidopsis as a model. Using a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways. The results of a Principal Component Analysis reveal sepal curvature as an important parameter accounting for variations in sepal morphology within genotypes. Unexpectedly, despite genetic homogeneity of the wild-type plants and reproducible culture conditions, we found a significant level of variability in sepal morphology. Our data also show that sepal shape from wild-type plants is more robust (less variable) than sepal size, hinting to a possible selective pressure on shape parameters.
Why it matches plant phenotyping methods3D萼片形態を復元・定量する独自パイプラインが研究の中心であり、器官形状・曲率という植物表現型を解析している。
abstractUsing a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways.
Reproduction assets foundThe paper's data availability statement explicitly deposits the phenotyping assets (original sepal images, segmented images, and sepal measurements) on recherche.data.gouv.fr under DOI 10.57745/LTTTBK. No custom code was developed; analysis used standard Python libraries, so no code asset qualifies.Dataset · publicAll the data that were used for the statistical analysis in this study (original images, segmented images, as well as sepal measurements) are openly available as a published dataset from the French national data platform recherche.data.gouv.fr at https://doi.org/10.57745/LTTTBK . No custom code or scripts were developed for the analysisOpen asset ↗recherche.data.gouv.fr · 10.57745/LTTTBKlines:128-315Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 kg ha -1 ) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 kg ha -1 ) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.
Why it matches plant phenotyping methodsUAV・SfM-MVSによるダイズ群落の3D構造再構成と、収量推定・倒伏判別のための専用深層学習モデル開発が研究の中心であり、再利用可能な表現型取得・推定手法に該当する。
abstractIn this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies.
Reproduction assets foundThe article's Data availability statement explicitly says the code and data used in the study (soybean UAV point cloud phenotyping, SoyNet/SoyNet-Res models, yield/lodging analysis) are publicly downloadable from the authors' GitLab repository.Code · publicData availability
The code and data mentioned in the article can be downloaded from https://gitlab.com/zlyzly28/plant-phenomics .Open asset ↗gitlab.com/zlyzly28/plant-phenomicslines:588-659Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Published12 Mar 2025Investigaciones Geográficas Boletín del Instituto de GeografíaCited by 1 · OpenAlex ↗
This study employs photogrammetry to evaluate and monitor the recovery of the cypress forest on Guadalupe Island, Mexico, an ecosystem significantly impacted by fires and overgrazing. Two drone surveys were conducted over the forest area during the summers of 2016 and 2019 using natural color (RGB) and near-infrared (NIR) cameras. This work presents the first complete 3D reconstruction of the cypress forest on the island. The image processing products include the canopy height model (CHM), digital surface model (DSM), and digital terrain model (DTM), which were utilized to calculate the number, density, height and crown projected areas of trees. The CHM showed a high correlation with the forest's structure (R = 0.92), based on field measurements of tree heights. Our study accounted for approximately 67,340 trees taller than two meters in 2019. Over 90% of the cypress population consisted of young trees between 2 and 3 meters tall, which have recovered significantly following a fire in 2008 that burned 70% of its extent. A horizontal expansion of 134 hectares was observed from 2016 to 2019 in the regeneration process.
Why it matches plant phenotyping methodsドローン画像のフォトグラメトリによる3D再構成を用いて樹木の高さ・密度・樹冠面積を推定し、現地測定との相関で検証しているため、植物形質の取得手法が中心です。
abstractThis study employs photogrammetry to evaluate and monitor the recovery of the cypress forest on Guadalupe Island, Mexico
Reproduction assets foundThe paper's photogrammetric phenotyping products (2016/2019 point clouds, orthomosaics, DSMs, CHMs) are publicly downloadable via a DOI data repository, and supplemental crown/treetop features are in CICESE's institutional repository. Both URLs appear in allowed_urls.Dataset · publicees. This phenomenon can be seen
in the three years observation window (2016-2019)
using photogrammetry.
AVAILABILITY OF DATA
AND MATERIALS
Point clouds from the 2016 and 2019 photogram-
metric reconstructions, as well as orthomosaics,
digital surface models (DSMs), and canopy height
models (CHMs), are available for download in
https://doi.org/10.5069/G9668BDD and https://
doi.org/10.5069/G92J693D. Supplemental infor-
mation such as Features related to crown and tree-
tops are accessible through CICESE’s institutional
repository (https://repositoriobiblioteca.cicese.mx/jspui/handle/123456789/44)
REFERENCES
Aljos-Farjon. (2017). A handbook of the world’s conifers
(second ed., vol. 1).Open asset ↗10.5069/G9668BDD · 10.5069/G9668BDDpdf-raw-page:15 lines:1-89Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.
Why it matches plant phenotyping methods植物の3D構造と蛍光機能情報を取得・統合する画像計測手法の開発が中心であり、植物病害の非侵襲的フェノタイピングに該当します。
abstractWe show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the code and datasets for reproducing the SfM 3D plant imaging results in the 4TU repository, with a DOI matching an allowed URL.Code · publicThe code and data sets for reproducing the results are available in 4TU repository at https://doi.org/10.4121/e6db8707-10ee-4553-9a98-753f1b4c526a .Open asset ↗4TU repository · 10.4121/e6db8707-10ee-4553-9a98-753f1b4c526alines:52-127Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
Machine learning models for crop image analysis and phenomics are highly important for precision agriculture and breeding and have been the subject of intensive research. However, the lack of publicly available high-quality image datasets with detailed annotations has severely hindered the development of these models. In this work, we present a comprehensive multicultivar and multiview rice plant image dataset (CVRP) created from 231 landraces and 50 modern cultivars grown under dense planting in paddy fields. The dataset includes images capturing rice plants in their natural environment, as well as indoor images focusing specifically on panicles, allowing for a detailed investigation of cultivar-specific differences. A semiautomatic annotation process using deep learning models was designed for annotations, followed by rigorous manual curation. We demonstrated the utility of the CVRP by evaluating the performance of four state-of-the-art (SOTA) semantic segmentation models. We also conducted 3D plant reconstruction with organ segmentation via images and annotations. The database not only facilitates general-purpose image-based panicle identification and segmentation but also provides valuable resources for challenging tasks such as automatic rice cultivar identification, panicle and grain counting, and 3D plant reconstruction. The database and the model for image annotation are available at https://bic.njau.edu.cn/CVRP.html.
Why it matches plant phenotyping methodsイネ画像データセットとアノテーションモデルを開発・評価し、セグメンテーション、器官再構成、穂・粒数計測などの再利用可能な表現型解析を中心に扱っているため。
abstractwe present a comprehensive multicultivar and multiview rice plant image dataset (CVRP)
Reproduction assets foundThe paper's own CVRP rice image dataset (images + annotations), accompanying code, and trained Mask2Former annotation model are explicitly stated as publicly available on Hugging Face and the authors' NJAU site.Dataset · publicThe CVRP dataset is publicly available on Hugging Face at https://huggingface.co/datasets/CVRPDataset/CVRP for academic use under the specified license.Open asset ↗CVRPDataset/CVRPhtml-lines:236-252Code · publicThe accompanying code and trained models are available at https://huggingface.co/CVRPDataset/Model.Open asset ↗CVRPDataset/Modelhtml-lines:236-252Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
In quantitative genomic analysis of wheat plant height (PH), the average height of a few representative plants is typically used to represent the PH of the entire plot, which overlooks the variation in height among other plants. Extracting different height quantiles from canopy point clouds can address this limitation. For this purpose, low-cost UAV cross-circling oblique (CCO) imaging, combined with structure-from-motion (SfM) and multi-view stereopsis (MVS), was employed to generate precise canopy point clouds for 262 F5 recombinant inbred lines (Zhongmai 578 × Jimai 22) across seven environments. Multi-level 3D-PH measurements were extracted from six height quantiles, revealing a strong correlation (mean r = 0.95) between 3D-PH and field-measured PH (FM-PH) across environments. The 90 % and 92 % height quantiles showed the closest agreement with FM-PH compared to other quantiles. Eleven stable quantitative trait loci (QTLs) associated with multi-level 3D-PH were identified using a 50K single nucleotide polymorphism array. Among these, QPhzj.caas-3A.2 (detected by 3D-PH) and QPhzj.caas-7A.1 (detected by both FM-PH and 3D-PH) represented potential novel loci. KASP markers for these QTLs were developed and validated. Furthermore, within the intervals of QPhzj.caas-5A and QPhzj.caas-3B (both were detected by 3D-PH), two candidate genes associated with PH regulation were identified: TaGL3-5A and Rht5 , respectively. Corresponding KASP markers for these genes were also developed and validated. This study highlighted the advantages of 3D model and multi-level 3D-PH in elucidating the genetic basis of crop height, and provided a precise and objective basis for advancing wheat breeding programs.
Why it matches plant phenotyping methodsUAV画像からSfM/MVSで3Dキャノピーモデルを構築し、複数の高さ分位点として植物高を抽出・検証することが研究の中心であるため、画像ベースの植物フェノタイピング手法として適格。
abstractExtracting different height quantiles from canopy point clouds can address this limitation.
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 plant height data for various environments and the detailed information of the genetic map can be downloaded from https://github.com/ILIKEWIND123/Plant-Phenomics .Open asset ↗ILIKEWIND123/Plant-Phenomicslines:364-399Code / dataset availability confirmedCrossref · checked 6 Sept 2026
Abstract Monitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras.
Why it matches plant phenotyping methodsRGB-D画像からレタスの構造・体積・葉密度を抽出し、生体重推定を検証する手法開発が研究の中心であり、データセットも構築している。
abstractIn this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce.
Reproduction assets foundThe paper's own lettuce 3D point cloud dataset (Pii, 402 point clouds with fresh weight references) is deposited on Zenodo, and the vacuum-package surface reconstruction code plus data processing scripts are publicly available on the authors' GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicData used in this study and developed models are available on Zenodo storage service https://zenodo.org/records/8410252 .Open asset ↗Zenodo · 8410252lines:158-220Code · publicThe code used at this study is available at https://github.com/VicB18/LettuceFW (accessed on 1 November 2024).Open asset ↗GitHub · VicB18/LettuceFWlines:158-220Code · publicThe code for the vacuum package method, along with the data processing scripts used in this study, are available in the Supplementary Information.Open asset ↗lines:98-114Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth’s surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 40,000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image-matching across different land cover types. Mean completeness ranged from 64 % for ‘glacial and perpetual snow’ to 98 % for ‘sealed surfaces’, with a value of 93 % for the ‘closed forest’ class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.28 to 0.53 m, with a normalised median absolute deviation (NMAD) of around 1 m and a maximum root mean square error (RMSE) of 3.90 m. The same analysis between geodetic points and historical DSMs showed higher accuracies, with median values of ≤ 0.05 m and an NMAD < 1 m. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史航空画像から植生高モデルを自動生成するフォトグラメトリ手法を開発・精度評価し、森林の高さ変化という植物状態を測定するデータセットも提供しているため、植物フェノタイピング手法が中心である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's own countrywide DSM and vegetation height model (VHM) rasters, plus masks and metadata, are publicly deposited on EnviDat with an explicit DOI, directly reproducing the paper's vegetation height measurements.Dataset · public22
5 Data availability
434
Datasets can be accessed from EnviDat (https://doi.org/10.16904/envidat.528, Marty et al., 2024). The following files are
435
available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models
436
(VHMs). A metadata shapefile is provided with information about the acquisition year of the photographs used here; the
437
geometry corresponds to the 1:25,00Open asset ↗EnviDat · 10.16904/envidat.528pdf-raw-page:22 lines:1-63Code / 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 · Europe PMC · checked 15 Sept 2026
BACKGROUND: The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noisy representations of plants using software or are expensive to capture in hardware. Recently, view synthesis models have been developed that can generate detailed 3D scenes, and even 3D models, from only RGB images and camera poses. These models offer unparalleled accuracy but are currently data hungry, requiring large numbers of views with very accurate camera calibration. RESULTS: In this study, we present a view synthesis dataset comprising 20 individual wheat plants captured across 6 different time frames over a 15-week growth period. We develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework. We trained each plant instance using two recent view synthesis models: 3D Gaussian splatting (3DGS) and neural radiance fields (NeRF). Our results show that both 3DGS and NeRF produce high-fidelity reconstructed images of a plant subject from views not captured in the initial training sets. We also show that these approaches can be used to generate accurate 3D representations of these plants as point clouds, with 0.74-mm and 1.43-mm average accuracy compared with a handheld scanner for 3DGS and NeRF, respectively. CONCLUSION: We believe that these new methods will be transformative in the field of 3D plant phenotyping, plant reconstruction, and active vision. To further this cause, we release all robot configuration and control software, alongside our extensive multiview dataset. We also release all scripts necessary to train both 3DGS and NeRF, all trained models data, and final 3D point cloud representations. Our dataset can be accessed via https://plantimages.nottingham.ac.uk/ or https://https://doi.org/10.5524/102661. Our software can be accessed via https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis.
Why it matches plant phenotyping methods3D植物表現型取得のための撮影システム、再構成手法、データセットを開発し、スキャナとの精度比較で検証しているため、方法が中心的である。
abstractWe develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework.
Reproduction assets foundThe paper releases its wheat plant multiview image dataset (via plantimages.nottingham.ac.uk and GigaDB DOI 10.5524/102661), its authors' analysis/capture codebase on GitHub (3D-Plant-View-Synthesis), a Software Heritage archive of that code, and a DOME-ML registry annotation. All are paper-specific, public, and have作者Code · publicruction output across all plants. We hope that our study will provide opportunities for researchers exploring new and improved 3D phenotyping algorithms, 3D reconstruction and view synthesis research, and active vision systems.
Availability of Source Code and Requirements
Project name: 3D Plant View Synthesis:
Project homepage: https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis [ 13 ]
Operating system(s): Windows, Ubuntu
Programming language: Python (>=3.8)
License: Apache 2.0
Any restrictions to use by nonacademics: None
Our code has also been archived in Software Heritage [ 66 ]. Functionality, such as Robotic View Capturing, 3DGS to Point Cloud, and our UR5 Configs files, are storOpen asset ↗GitHub · Lewis-Stuart-11/3D-Plant-View-Synthesislines:663-695Code / 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 confirmedEurope PMC · checked 13 Sept 2026
A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato ( Solanum lycopersicum var. cerasiforme ) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.
Why it matches plant phenotyping methods植物の生育・ストレス・健全性評価を目的とした、放射補正済みマルチスペクトル画像とオルソモザイクを含む再利用可能なデータセットであり、植物表現型取得基盤が中心です。
abstractThe dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels.
Reproduction assets foundThe paper is itself a data descriptor for a public UAV multispectral cherry tomato phenotyping dataset (calibrated aerial images, manual plant images, orthomosaics, binary masks) deposited in Dryad, with an explicit DOI and direct URL matching an allowed URL.Dataset · publicRepository name: tomatodb
Data identification number: 10.5061/dryad.63xsj3vbd
Direct URL to data: https://datadryad.org/stash/share/Wq_X7QUyGryJ-ZnmgfwRn4MtOCr4VBm_MSnhF40sv_8#readmeOpen asset ↗Dryad · 10.5061/dryad.63xsj3vbdlines:1-42Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Hyperspectral imaging provides high-dimensional spatial-temporal-spectral information showing intrinsic matter characteristics 1-5 . Here we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution. By integrating different broadband modulation materials on the image sensor chip, the target spectral information is non-uniformly and intrinsically coupled to each pixel with high light throughput. Using intelligent reconstruction algorithms, multi-channel images can be recovered from each frame, realizing real-time hyperspectral imaging. Following this framework, we fabricated a broadband visible-near-infrared (400-1,700 nm) hyperspectral image sensor using photolithography, with an average light throughput of 74.8% and 96 wavelength channels. The demonstrated resolution is 1,024 × 1,024 pixels at 124 fps. We demonstrated its wide applications, including chlorophyll and sugar quantification for intelligent agriculture, blood oxygen and water quality monitoring for human health, textile classification and apple bruise detection for industrial automation, and remote lunar detection for astronomy. The integrated hyperspectral image sensor weighs only tens of grams and can be assembled on various resource-limited platforms or equipped with off-the-shelf optical systems. The technique transforms the challenge of high-dimensional imaging from a high-cost manufacturing and cumbersome system to one that is solvable through on-chip compression and agile computation.
Why it matches plant phenotyping methods植物のクロロフィルおよび糖含量を定量可能なオンチップ・ハイパースペクトル画像センサーを開発しており、センサー技術と植物形質取得への応用が中心的である。
abstractHere we report an on-chip computational hyperspectral imaging framework with high spatial and temporal resolution.
Reproduction assets foundThe paper explicitly states that all data generated or analysed are available in a public GitHub repository (hyperspectral image/video dataset collected with the HyperspecI sensors) and that demo code is available in another public GitHub repository. Both are paper-specific, public, and actionable.Dataset · publiced the project.
Peer review
Peer review information
Nature thanks Yidong Huang, Yunfeng Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, Open asset ↗bianlab/Hyperspectral-imaging-datasetlines:148-189Code · publiche peer review of this work.
Data availability
All data generated or analysed during this study are included in this published article and the public repository at GitHub ( https://github.com/bianlab/Hyperspectral-imaging-dataset ).
Code availability
The demo code of this work is available from the public repository at GitHub ( https://github.com/bianlab/HyperspecI ).
Competing interests
L.B., Z.W., Yuzhe Zhang and J. Zhang hold patents on technologies related to the devices developed in this work (China patent nos. ZL 2022 1 0764166.5, ZL 2022 1 0764143.4, ZL 2022 1 0764141.5, ZL 2019 1 0441784.4, ZL 2019 1 0482098.1 and ZL 2019 1 1234638.0) and submitted the related patent applications.Open asset ↗bianlab/HyperspecIlines:148-189Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability 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 confirmedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングによりソルガムの葉序を自動抽出し、手動測定との比較および反復性を評価しており、表現型取得手法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's Data Availability section publicly deposits the raw sorghum images on Zenodo and the phenotypic data, GWAS result files, and analysis/figure code on GitHub (jdavis-132/phyllotaxy). The reconstruction/skeletonization code (cropsinsilico/SorghumVoxelCarving) is also mentioned but its URL has no exact match inDataset · publicuction and skeletonization is available at GitHub: https://github.com/
401 cropsinsilico/SorghumVoxelCarving
402 The raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstructiOpen asset ↗Zenodo · 10.5281/zenodo.4426620pdf-layout-page:14 lines:1-50Code · publicare available at Zenodo: Mathieu Gaillard, Chenyong
403 Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of
404 Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620.
405 The phenotypic data, GWAS result files and code for main figures and analysis are available at
406 Github: https://github.com/jdavis-132/phyllotaxy.git
407 Author Contributions
408 JMD and NS collected measurements and ground truth data. MG IO and BL designed methods
409 for and performed image analysis, plant reconstruction and trait value extraction. JMD NS and
410 RJG annotated image data and employed domain expertise to reconcile extracted trait values and
411 true plaOpen asset ↗GitHub · jdavis-132/phyllotaxypdf-layout-page:14 lines:1-50Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract Microscopic imaging for studying plant-pathogen interactions is limited by its reliance on invasive histological techniques, like clearing and staining, or, for in vivo imaging, on complicated generation of transgenic pathogens. We present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography. Based on intrinsic signal fluctuations as tissue contrast we image filamentous pathogens and a nematode in vivo in 3D in plant tissue. We analyze 3D images of lettuce downy mildew infection ( Bremia lactucae ) to obtain hyphal volume and length in three different lettuce genotypes with different resistance levels showing the ability for precise (micro) phenotyping and quantification of the infection level. In addition, we demonstrate in vivo longitudinal imaging of the growth of individual pathogen (sub)structures with functional contrast on the pathogen micro-activity revealing pathogen vitality thereby opening a window on the underlying molecular processes.
Why it matches plant phenotyping methods植物病原体を対象としたラベルフリーOCTによるリアルタイム3D画像化を開発し、感染植物の病原体量・感染レベル・活性を定量する手法として実証しているため、植物フェノタイピング手法が中心である。
abstractWe present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography.
Reproduction assets foundThe authors explicitly deposited supporting code for dynamic OCT processing, segmentation, and data analysis, together with a representative selection of the dynamic OCT volumes (the paper's plant-pathogen phenotyping data), in a freely-accessible Zenodo repository (10.5281/zenodo.11428245). This is a paper-specific,公开Dataset · publicA representative selection of the data, all the dynamic OCT volumes, and supporting code for data processing and plotting have been uploaded to a freely-accessible Zenodo repository 33 . [10.5281/zenodo.11428245].Zenodo · 10.5281/zenodo.11428245lines:133-155Code / dataset availability confirmedOpenAlex · arXiv · checked 7 Sept 2026
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge of reconstructing accurate 3D shapes of fruits from partial views, which is common in agricultural settings. We introduce CF-PRNet, a coarse-to-fine prototype refining network, leverages high-resolution 3D data during the training phase but requires only a single RGB-D image for real-time inference. Our approach begins by extracting the incomplete point cloud data that constructed from a partial view of a fruit with a series of convolutional blocks. The extracted features inform the generation of scaling vectors that refine two sequentially constructed 3D mesh prototypes - one coarse and one fine-grained. This progressive refinement facilitates the detailed completion of the final point clouds, achieving detailed and accurate reconstructions. CF-PRNet demonstrates excellent performance metrics with a Chamfer Distance of 3.78, an F1 Score of 66.76%, a Precision of 56.56%, and a Recall of 85.31%, and win the first place in the Shape Completion and Reconstruction of Sweet Peppers Challenge.
Why it matches plant phenotyping methods果実の部分RGB-D画像から3D形状を再構成する手法を開発・評価しており、植物器官の形態形質取得が研究の中心である。
abstractThis paper addresses the challenge of reconstructing accurate 3D shapes of fruits from partial views
Reproduction assets foundThe paper's authors publicly release their CF-PRNet source code for sweet pepper point cloud completion. The sweet pepper benchmark dataset is cited prior work (ref [2]), not a paper-specific asset, and the challenge website is a generic event page.Code · publicOur source code is
available at https://github.com/uqzhichen/CF-PRNet/.Open asset ↗uqzhichen/CF-PRNetpdf-page:1 lines:1-50Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.
Why it matches plant phenotyping methods葉傾斜角という植物形質を自動取得する画像ベース手法を開発し、LiDARと精度比較で検証しており、フェノタイピング手法が中心である。
abstractwe design an image-phenotype-based noninvasive and efficient optical sensor measurement system
Reproduction assets foundThe authors explicitly state that their code and data (the Auto-LIA LIA measurement system, including RGB image datasets and processing pipeline) are publicly available at their project site, which is among the allowed URLs.Code · publicWe make our code and data publicly available at http://autolia.samlab.cn .Open asset ↗autolia.samlab.cnlines:334-498Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underestimating the actual volume. To address this issue, we developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning network that consists of a convolutional encoder and a decoder. The encoder compresses RGB-D images into latent vectors that are used by the decoder to complete the 3D shape using the deep signed distance field network (DeepSDF). To evaluate our CoRe++ network, we collected partial and complete 3D point clouds of 339 potato tubers on an operational harvester in Japan. On the 1425 RGB-D images in the test set (representing 51 unique potato tubers), our network achieved a completion accuracy of 2.8 mm on average. For volumetric estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml). We found that the RMSE can be further reduced to 18.2 ml when performing the 3D shape completion in the center of the RGB-D image. With an average 3D shape completion time of 10 milliseconds per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation. CoRe++'s high-throughput and accurate processing allows it to be applied to other tuber, fruit and vegetable crops, thereby enabling versatile, accurate and real-time yield monitoring in precision agriculture. Our code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git.
Why it matches plant phenotyping methodsRGB-D画像からジャガイモ塊茎の3D形状を補完し、体積・収量を推定する手法を開発・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 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images.
Reproduction assets foundThe paper's abstract explicitly states that the authors' code, network weights, and the potato tuber RGB-D/3D point cloud dataset are publicly available at the authors' GitHub repository (UTokyo-FieldPhenomics-Lab/corepp), which is a paper-specific, public, actionable asset for the CoRe++ phenotyping analysis.Code · publicOur code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git .Open asset ↗UTokyo-FieldPhenomics-Lab/corepplines:1-93Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
Why it matches plant phenotyping methodsCT画像から花序内の小花と花托の接触点を自動検出し、3D位置を推定する手法の開発・精度評価が研究の中心であるため、植物フェノタイピング手法に該当する。
abstractTherefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques.
Reproduction assets foundThe authors publicly deposited the labeled CT slice-image dataset (contact point annotations and receptacle segmentation labels) on Figshare, and a 3D visualization video of the contact point estimation results is available on YouTube. Raw CT volumes are only available on request. No author analysis code repository is.Dataset · publicre task is to automate the clustering parameters, which are currently determined manually. We also plan to develop a mathematical model of the position of the contact point between the receptacle and florets based on the estimation results.
Data availability statement
The labeled data for this study can be found in the Figshare https://doi.org/10.6084/m9.figshare.25388434 . The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributionsOpen asset ↗Figshare · 10.6084/m9.figshare.25388434lines:513-540Code / dataset availability confirmedarXiv · checked 14 Sept 2026
As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server.
Why it matches plant phenotyping methods果実の3D形状という植物器官の形態形質を対象に、RGB-D画像・高精度点群・評価用ベンチマークを構築しており、形状取得と推定の方法論が中心である。
abstractWe provide an RGB-D dataset for estimating the 3D shape of fruits.
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 · publicOur development toolkit including a data loader is available at:
https://github.com/PRBonn/shape_completion_toolkit for handling the dataset and computing metrics.Open asset ↗PRBonn/shape_completion_toolkitlines:55-81Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Manual segmentation of the petals of flower computed tomography (CT) images is time-consuming and labor-intensive because the flower has many petals. In this study, we aim to obtain a three-dimensional (3D) structure of Camellia japonica flowers and propose a petal segmentation method using computer vision techniques. Petal segmentation on the slice images fails by simply applying the segmentation methods because the shape of the petals in CT images differs from that of the objects targeted by the latest instance segmentation methods. To overcome these challenges, we crop two-dimensional (2D) long rectangles from each slice image and apply the segmentation method to segment the petals on the images. Thanks to cropping, it is easier to segment the shape of the petals in the cropped images using the segmentation methods. We can also use the latest segmentation method for the task because the number of images used for training is augmented by cropping. Subsequently, the results are integrated into 3D to obtain 3D segmentation volume data. The experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping. The 3D segmentation results were also obtained and visualized successfully.
Why it matches plant phenotyping methods花弁のCT画像から3D構造を抽出する画像セグメンテーション手法の開発と精度比較が中心であり、植物形態フェノタイピングに該当する。
abstractThe experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping.
Reproduction assets foundThe paper's CT volume data of Camellia japonica flowers (with ground-truth annotations) is publicly deposited on Figshare, and the authors' segmentation/integration code is publicly available on GitHub, both with explicit availability statements.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.25264774.v1Open asset ↗figshare · 10.6084/m9.figshare.25264774.v1lines:447-494Code · publicThe code implementing the proposed method is available at https://github.com/yu-NK/petal_ct_crop_seg.gitOpen asset ↗github · yu-NK/petal_ct_crop_seglines:447-494Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Feeding the increasing global population and reducing the carbon footprint of agricultural activities are two critical challenges of our century. Growing crops under protected horticulture and precise crop monitoring have emerged to address these challenges. Crop monitoring in commercial protected facilities remains mostly manual and labour intensive. Using computer vision to solve specific problems in image-based crop monitoring in these compact and complex growth environments is currently hindered by the scarcity of available data. We collected an RGBD dataset for vertically supported, hydroponically-grown capsicum plants in a commercial-scale glasshouse facility to fill this gap. Data were collected weekly using a single top-angled stereo camera mounted on a mobile platform running between the hydroponic gutters. The RGBD streams covered 80 % of the crop growing season in three different light conditions. The metadata include camera configurations and light condition information. Manually measured plant heights of ten selected plants per gutter are provided as ground truth. The images covered the whole plants and focused on the top third. This dataset will support research on plant height estimation, plant organ identification, object segmentation, organ measurements, 3D reconstruction, 3D data processing, and depth noise reduction. The usability of the dataset has been successfully demonstrated in a previously published study on plant height estimation using machine learning and 3D point cloud.
Why it matches plant phenotyping methods植物の草丈推定や器官計測を目的としたRGBD画像データセットを構築し、地上真値も提供しているため、植物フェノタイピング手法・データセットが中心です。
abstractWe collected an RGBD dataset for vertically supported, hydroponically-grown capsicum plants in a commercial-scale glasshouse facility to fill this gap.
Reproduction assets foundThis data article directly deposits its paper-specific phenotyping assets: the PC4C_CAPSI RGBD image dataset (Rosbag streams, JSON metadata, manual plant-height ground truth) on the Western Sydney University ResearchDirect repository, and the authors' RGBD processing code (image extraction, depth correction, 3D reconssDataset · publicData accessibility
Repository name: Image Data of Capsicum Plant Growth in Protected Horticulture: PC4C_CAPSI. [ 1 ]
Data identification number: 10.26183/1A0R-E318
Direct URL to data: https://rds.westernsydney.edu.au/Institutes/HIE/2024/Jayasuriya_N/Open asset ↗rds.westernsydney.edu.aulines:1-40Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
In recent years, illegal felling of and damage to the incense tree Aquilaria sinensis (Lour.) Spreng. have been reported in Hong Kong. Their native populations are under increasingly severe threat. Therefore, the development of a standard and efficient method to classify and document wounds on vulnerable trees is urgently needed for conservation purposes. In this study, photogrammetry was used to document wounds in A. sinensis through 3D modeling. A total of 752 wound records from 484 individual A. sinensis trees from Hong Kong were included to establish a new wound classification system. Our major findings include a novel standardized procedure for photogrammetric documentation and a wound classification system. The results of this study will facilitate A. sinensis conservation, by enhancing wound documentation and information transfer to law enforcement and education.
Why it matches plant phenotyping methods樹木の損傷状態を対象に、写真測量による3D記録と分類手順を開発しており、植物状態の取得・抽出が研究の中心である。
abstractphotogrammetry was used to document wounds in A. sinensis through 3D modeling.
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 · publicAll the 3D models can be accessed on the “Virtual Carpological Herbarium” in the Shiu‐Ying Hu Herbarium ( https://syhuherbarium.sls.cuhk.edu.hk/collections/3d‐digitized‐tag/wound/ ; Username: syhuherbarium; Password: @CUHK).Open asset ↗lines:403-434Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.
Why it matches plant phenotyping methods3Dデジタルアトラスと神経ベースのトポロジー解析、形態計測を用いて植物器官の細胞構造と形状を定量化しており、表現型取得・解析手法が研究の中心である。
abstractHere, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana.
Reproduction assets foundThe paper's topological analysis and statistical evaluation code is publicly available on GitHub (NADO repository), with explicit availability language. The paper-specific 3D digital ovule dataset (S-BIAD957) is deposited in BioStudies, but no matching allowed URL exists for it, so it cannot be listed as an actionable,Code · publicThe source code and the Dockerfiles can be obtained from the Github repository at https://github.com/fabian-roll/NADO .Open asset ↗https://github.com/fabian-roll/NADO · NADOlines:109-124Code / 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 · OpenAlex · checked 15 Sept 2026
Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Why it matches plant phenotyping methods植物の葉縁形態を3D再構築して定量化する手法を開発し、仮想葉と実葉で性能評価・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets and analysis code for this 3D leaf edge reconstruction study in a public GitHub repository (MorphometricsGroup/Murata-2024), and the virtual-leaf simulation inputs (Sketchfab 3D leaf models) are publicly available. Generic libraries (Detectron2, 3Dataset · publicto S4
Data Availability Statement
The datasets used and/or analyzed during the current study are available in the repositories on Zenodo (10.5281/zenodo.10836254, 10.5281/zenodo.10836258, 10.5281/zenodo.10836260, 10.5281/zenodo.10065546, 10.5281/zenodo.10828962, 10.5281/zenodo.10121073, and 10.5281/zenodo.10829007) and GitHub ( https://github.com/MorphometricsGroup/Murata-2024 ).Open asset ↗MorphometricsGroup/Murata-2024lines:311-320Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023. The UAV flight took place at 30 m from the canopy level with the multispectral camera titled at an angle of 900. The SPAD Chlorophyll Meter was used to collect ground truth data, which is proportional to the nitrogen level of the leaf. There were 50 randomly selected readings throughout the paddy field. Relevant climate data for five days was provided by the Rice Research and Development Institute, Bathalagoda, which belongs to the paddy field. The purpose of this data creation was to aid researchers who are generally interested in disease diagnosis. Moreover, this dataset allows for studying the effect of using different tilt angles on the 3D reconstruction of the paddy fields and the generation of orthomosaics.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSPADによる地上真値を含む、植物キャノピーの状態推定に再利用可能なデータセットであり、画像取得・オルソモザイク生成・3D再構成が中心的な方法的貢献です。
abstractIn this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening)
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the authors' own UAV multispectral images, SPAD ground-truth readings, GPS shapefile, and climate data for paddy nitrogen phenotyping. This is a paper-specific, publicly available dataset with an explicit direct URL and DOI.Dataset · publicructions, the flight path was configured to fly on its own (DJI). The dataset includes a shapefile containing the GPS positions of the BG300 rice clusters. The same dates were used to gather SPAD meter values.
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/h8s5mn52j6.1
Direct URL to data: https://data.mendeley.com/datasets/h8s5mn52j6/1
Data source location
Institution: Rice Research and Development Institute
City/Town/Region: Batalagoda, Ibbagamuwa, Kurunegala
Country: Sri Lanaka
Latitude and longitude (and GPS coordinates) for collected samples/data: 7.53240 N, 80.43400E
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Value of the Data
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Data is useful for researchers interested in UAV (unmannedOpen asset ↗Mendeley Data · 10.17632/h8s5mn52j6.1lines:1-80Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
Why it matches plant phenotyping methods根系のランドマーク検出・形状復元・形質抽出を行う深層学習ベースの植物フェノタイピング手法を開発・検証し、専用ライブラリも提供しているため。
abstractHere, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly make all paper-specific assets public: the sleap-roots trait-extraction codebase on GitHub, a separate repository with figure-replication code, and an OSF deposit containing labeled training data, trained pose-estimation models, and analysis files for the root phenotyping measurements.Code · publicthe specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-rootsOpen asset ↗talmolab/Berrigan_et_al_sleap-roots · Berrigan_et_al_sleap-rootslines:485-526Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files, which can be accessed via the following link: https://osf.io/k7j9g/Open asset ↗osf.io/k7j9g · k7j9glines:485-526Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
MaizeGrowth chamberStereoRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Background The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を取得・解析する画像計測システムを開発し、特徴量の信頼性と精度を検証しており、植物表現型取得が中心である。
abstractThis paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe paper's 3D root tip trajectory data (phenotyping measurements from maize root imaging) are publicly deposited on Zenodo. The analysis software and scripts are only available upon request, so they qualify as request_only.Dataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242 . Software and scripts are available for research purposes upon request through the email address: mindtheplantlab@gmail.com.Open asset ↗Zenodo · 8422242lines:141-172Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.
Why it matches plant phenotyping methods小麦の3D形態情報をMVS-Phenoと点群・深層学習で取得し、器官分割、形態パラメータ抽出、精度評価を行う手法研究であり、フェノタイピング手法が中心です。
abstractwe propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale.
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code used in the article are publicly available on GitHub at the authors' repository, which matches an allowed URL. This qualifies as a paper-specific public asset covering the wheat 3D reconstruction/phenotyping analysis.Code · publicThe data and code used in this article are available on GitHub, at https://github.com/lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatOpen asset ↗lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatlines:280-436Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework’s performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework’s performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework’s applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.
Why it matches plant phenotyping methods植物葉画像から病変画素率を算出して病害重症度を推定する画像解析手法を開発し、データセット上で比較・統計検証しており、植物表現型取得が中心である。
abstracta lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances.
Reproduction assets foundThe paper's plant-phenotyping measurements (apple leaf disease detection and severity estimation) were performed on the publicly available Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset, which the authors link via a Science Data Bank deposit. No authors' analysis code or trained model checkpoints are explicitlydeDataset · publicrk of this research also includes the deployment of the PDSE-Lite framework on different IoT devices, such as Unmanned Aerial Vehicles (UAVs), to enable real-time monitoring of plant diseases in agricultural fields.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.scidb.cn/en/detail?dataSetId=0e1f57004db842f99668d82183afd578 .
Author contributions
PB: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing. PG: Conceptualization, Methodology, Software, Visualization, Writing – original draft. SM: Formal analysis, Resources, Writing – review & editing.
Funding Open asset ↗lines:501-513Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published1 Jan 2024IEEE Transactions on Geoscience and Remote SensingCited by 1 · OpenAlex ↗
Full-spectrum Sun-induced chlorophyll fluorescence (SIF) offers profound physiological insights into plant functional status compared to single-band SIF. We propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF, aiming to address the limitations of existing methods, such as reliance on reflectance training datasets and the limited spectral range of retrieved SIF spectrum. The core principle of the FSM involves modeling reflectance as a wavelength-dependent function, which can be approximated by successive summations using high-order expansions of the Fourier series. The performance of the FSM was thoroughly evaluated through a combination of simulations and field measurements. The findings illustrate FSM’s capability to achieve high-precision full-spectrum SIF retrieval, with an average relative root-mean-square error (RRMSE) of 2.468% based on synthetic data. Moreover, the corresponding RRMSE values in the O2-A and O2-B bands, at 1.1% and 3.724%, respectively, indicate accuracy comparable to the spectral fitting method (SFM) and advanced FSR (aFSR) methods and superior to the SpecFit method. In the field full-spectrum SIF retrieval, FSM exhibited improved reflectance reconstruction and produced more reasonable results for the diurnal variation of full-spectrum SIF. The diurnal comparison of single-band SIF at both Italian and German sites further highlights the close alignment between FSM-retrieved SIF and the SFM SIF, with$R^{2}$values exceeding 0.96 and a maximum RMSE of 0.118 mW/m2/sr/nm. Conversely, the aFSR method encountered challenges stemming from an under-representation of the training dataset, resulting in the maximum RMSE at the Italian site reaching 0.506 mW/m2/sr/nm, along with a minimum$R^{2}$of 0.809. The FSM demonstrates the promising potential for full-spectrum SIF retrieval, accompanied by fewer limitations.
Why it matches plant phenotyping methods植物キャノピー計測から葉緑素蛍光を抽出する新規手法を開発し、シミュレーションと圃場計測で精度検証・既存法比較を行っており、植物生理状態のフェノタイピング手法が中心である。
abstractWe propose a Fourier series-based method (FSM) for retrieving full-spectrum SIF
Reproduction assets foundThe paper's FloX field spectral measurements (canopy upwelling radiance and apparent reflectance from Grosseto, Italy and Julich, Germany) are explicitly stated to be publicly available on Zenodo, matching the allowed URL. No author analysis code or trained model deposit is mentioned.Dataset · publicp (sparse vegetation), with observation heights of 1.5
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m and 3 m, respectively. For this study, we used six sets of clear-sky observations
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conducted on April 7, 16, and 25, 2018, in Italy, and on November 5, 7, and 18, 2020,
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in Germany. These spectral datasets are already available on the shared online platform
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(https://zenodo.org/records/7040578). For a more detailed description, please refer to
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the work by Naethe, Julitta [17].
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Figure 1 illustrates the diurnal measurements of upwelling radiance and apparent
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reflectance recorded over the course of six days. The Italian measurements (upper two
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rows) depict the vigorous growth period of the target vegetatOpen asset ↗zenodo · 7040578pdf-raw-page:6 lines:1-53Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
PoplarLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection ( P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.
Why it matches plant phenotyping methods時系列画像フェノタイピングとFSPMを統合し、根系形態パラメータを自動抽出・評価する方法が中心である。
abstractwe applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM.
Reproduction assets foundThe paper's Data Availability statement deposits all data and R scripts (the paper's phenotyping measurements and analysis) on Zenodo, and the adapted CropRootBox.jl model code on GitHub. Only the Zenodo URL matches an allowed URL, so the Zenodo asset is reported; the GitHub repository is noted but its URL is not in anDataset · publicview and editing: S.P., D.B., K.Y., S.D., and S.-H.K.
Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The model is housed in Github at github.com/uwkimlab/CropRootBox.jl_propagation.jl . All data and and R scripts are housed in Zenodo at https://doi.org/10.5281/zenodo.8083525 .
Supplementary Materials
Supplementary 1
Figs. S1 to S7
Tables S1 to S2
Click here for additional data file.
References
1.
Seethepalli
A , Dhakal
K , Griffiths
M , Guo
H , Freschet
GT , York
LM
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RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Code / dataset availability confirmedEurope PMC · Crossref · checked 7 Sept 2026
Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusions The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Why it matches plant phenotyping methods植物の3D顕微鏡画像から細胞形態を抽出するセグメンテーション手法を開発し、植物データセットで性能評価・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractWe propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data.
Reproduction assets foundThe paper provides public author code (CellStitch implementation and experiment-reproducing notebooks on GitHub) and the plant image datasets analyzed (Ovules, ATAS, Arabidopsis 3D Digital Tissue Atlas), all with explicit availability statements and URLs.Code · publicopen source code implementing the stitching algorithm from the top to the bottom layer is available at https://github.com/imyiningliu/cellstitchOpen asset ↗imyiningliu/cellstitchlines:116-127Dataset · publicAll the datasets analyzed in this paper are publicly available online. Ovules: https://osf.io/uzq3w/ ; ATAS: https://www.repository.cam.ac.uk/handle/1810/262530 ; Arabidopsis 3D Digital Tissue Atlas: https://osf.io/fzr56Open asset ↗lines:175-223Code / dataset availability confirmedCrossref · checked 14 Sept 2026
A sound understanding of plant growth is critical to maintaining future crop productivity under ongoing climate change. Remotely sensed time series of crop functional traits from optical satellite imagery are an invaluable tool for deriving appropriate management practices that facilitate risk mitigation and increase the resilience of agroecosystems. However, the availability of imagery is limited by atmospheric disturbances that cause large temporal gaps and noise in the trait time series. Therefore, time series reconstruction methods are required for accurate crop growth modelling. Physiological priors, such as the fact that plant growth is mainly controlled by a few environmental covariates, among which air temperature plays a prominent role, represent a promising approach to improve the representation of crop growth. Here, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat. A probabilistic ensemble Kalman filtering data assimilation scheme allows the combination of high temporal resolution air temperature data and satellite imagery, which also allows quantification of uncertainties. The proposed approach requires a smaller number of satellite observations compared to conventional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover, and is considerably less complex than a mechanistic crop growth model. Validation was carried out using in-situ data collected on winter wheat plots in Switzerland in two consecutive years. The validation results suggest that the proposed assimilation of Sentinel-2 GLAI and temperature-response-based growth rates allows the reconstruction of physiologically meaningful GLAI time series. In particular, the systematic underestimation of high in-situ GLAI values (> 5 m^2 m^-2) often prevalent in purely remote sensing driven GLAI time series reconstruction was reduced. Thus, the proposed approach is advantageous compared to state-of-the-art remote sensing approach based on wide-spread logistic functions by means of physiological plausibility, fitting requirements and representation of high in-situ GLAI values. This has great potential to increase the reliability of remotely sensed crop productivity assessment.
Why it matches plant phenotyping methods衛星光学データと生理モデルを統合して作物GLAI時系列を再構築する手法を提案し、圃場データで検証しており、植物形質推定が研究の中心である。
abstractHere, a novel approach is proposed that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three dose response curve approaches describing the a priori physiological relationship between growth and temperature in winter wheat.
Reproduction assets foundThe authors explicitly state that code and data to reproduce the entire workflow (DRC fitting, Sentinel-2 GLAI assimilation, and validation) are publicly available on GitHub under GNU GPL v3.0. This is a paper-specific, public, actionable asset. Other URLs in the text are cited references or generic libraries (e.g., NLCode · publicCode and Data Availability
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Code to reproduce the entire workflow including calibration and validation data is
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available at https://github.com/EOA-team/sentinel2_crop_trait_timeseries
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under GNU General Public License v3.0.
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Credit Authorship Contribution Statement
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Lukas Valentin Graf: Conceptualization, Methodology, Formal analysis, Vali-
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dation, Visualization, Software, Writing - original draft. Flavian Tschurr: Formal
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Analysis, Methodology, Software, Methodology, Writing - original draftOpen asset ↗EOA-team/sentinel2_crop_trait_timeseriespdf-raw-page:55 lines:1-41Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
Field / plotLeafStomata / guard-cell complexPhysiological trait estimation2D/3D reconstructionPhotosynthesis / fluorescenceStomatal traitsWater status / transpiration
ABSTRACT Premise of the study The adaptive significance of stomata on both upper and lower leaf surfaces, called amphistomy, is unresolved. A widespread association between amphistomy and open, sunny habitats suggests the adaptive benefit of amphistomy may be greatest in these contexts, but this hypothesis has not been tested experimentally. Understanding why amphistomy evolves can inform its potential as a target for crop improvement and paleoenvironment reconstruction. Methods We developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”. We used humidity to modulate stomatal conductance and thus compare photosynthetic rates at the same total stomatal conductance. We estimated AA and related physiological and anatomical traits in 12 populations, six coastal (open, sunny) and six montane (closed, shaded), of the indigenous Hawaiian species ‘ilima ( Sida fallax ). Key results Coastal ‘ilima leaves benefit 4.04 times more from amphistomy compared to their montane counterparts. Our evidence was equivocal with respect to two hypotheses – that coastal leaves benefit more because 1) they are thicker and therefore have lower CO 2 conductance through the internal airspace, and 2) that they benefit more because they have similar conductance on each surface, as opposed to most of the conductance being on the lower (abaxial) surface. Conclusions This is the first direct experimental evidence that amphistomy per se increases photosynthesis, consistent with the hypothesis that parallel pathways through upper and lower mesophyll increase the supply of CO 2 to chloroplasts. The prevalence of amphistomatous leaves in open, sunny habitats can partially be explained the increased benefit of amphistomy in ‘sun’ leaves, but the mechanistic basis of this observation is an area for future research.
Why it matches plant phenotyping methods葉の両面気孔性が光合成に与える効果を定量化する新しい生理的測定法を開発し、複数集団で比較検証しており、表現型取得が研究の中心である。
abstractWe developed a new method to quantify “amphistomy advantage”, AA, as the log-ratio of photosynthesis in an amphistomatous leaf to that of the same leaf but with gas exchange blocked through the upper (adaxial) surface, which we term “pseudohypostomy”.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides a public GitHub repository containing the custom analysis scripts for this study's amphistomy advantage measurements. Raw data are only promised for future Dryad deposit (not yet available), so only the code asset qualifies.Code · publicCustom scripts are available on a GitHub repository (https://github.com/cdmuir/stomata-ilima) and will
be archived on Zenodo with a DOI and stable URL upon publication.Open asset ↗cdmuir/stomata-ilimapdf-page:16 lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs. Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images, consistent with biological interpretation. While using substantial SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and dataset balance in terms of phenotypes for more precise outcomes.
Why it matches plant phenotyping methodsSNPからリンゴ画像を予測・再構成するGenoDrawingフレームワークを提案しており、果樹の視覚形質を推定する計算手法が研究の中心である。
abstractThis study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array
Reproduction assets foundThe authors publicly release their analysis code, notebooks, and trained model weights (autoencoder and embedding predictor) for the GenoDrawing framework in a GitHub repository. The apple images used for phenotyping are only available upon request from a prior study, so they do not qualify as public assets.Code · publicThe code repository including notebooks and models with their trained weights can be found in the following GitHub repository: https://github.com/Fedjurrui/GenoDrawingOpen asset ↗Fedjurrui/GenoDrawinglines:80-113Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.
Why it matches plant phenotyping methodsドローン画像のSfM-MVS処理を改良・精度検証し、植生の高さ・体積・バイオマス監視に用いる手法が研究の中心である。
abstractwe refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions.
Reproduction assets foundThe paper's SfM sparse-cloud optimization analysis was implemented as a Python module in the authors' public MetashapeTools repository (co-author Marvin Ludwig), explicitly linked in the text. The bl_gimbal repository is only a gimbal hardware controller, not phenotyping analysis, and the Data Availability Statement isCode · publicencing process of the sparse cloud [50]. This approach focuses on minimizing the error of
georeferencing check points within the sparse cloud by identifying the optimal filter pa-
rameters. Consequently, only tie points with low reprojection errors are used. This appli-
cation is available as a Python module for MetashapeTools (https://github.com/en-vima/MetashapeTools/, accessed on 30 August 2023).
An orthomosaic is a detailed and geometrically accurate image of an area, composed
of multiple photos that have been orthorectified. Within this framework, once the
Figure 4. (a) Illustrates the optimized workflow for the Metashape Structure from Motion (SfM)
(Ludwig et al, 2020 [50]). (b) RepresOpen asset ↗en-vima/MetashapeToolspdf-raw-page:7 lines:1-31Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the authors' repository, which is an allowed URL.Code · publicAll code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 7 Sept 2026
MaizeLaboratory / benchtopStereoRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Background: The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking of temporal and three-dimensional (3D) spatial information. This paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics. Results The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length. Conclusions The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analyzing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.
Why it matches plant phenotyping methods根の3D動態を画像から再構成・解析するシステムを開発し、特徴量の信頼性と測定精度を検証しており、植物表現型取得法が中心である。
abstractThis paper describes a new system based on timelapse for the 3D reconstruction and analysis of roots growing in hydroponics.
Reproduction assets foundThe preprint explicitly states that the 3D root tip trajectory data underlying its phenotyping analysis are publicly deposited on Zenodo (record 8422242), making this a paper-specific, publicly accessible phenotype dataset. No author analysis code with a public URL is stated (SPROUTS is proprietary third-party softwareDataset · publicData describing 3D trajectories used in this paper are available here: https://zenodo.org/record/8422242Open asset ↗Zenodo · 8422242lines:119-156Code / dataset availability confirmedEurope PMC · OpenAlex · checked 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 confirmedOpenAlex · Crossref · checked 15 Sept 2026
The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops.
Why it matches plant phenotyping methods大豆植物の3D形態取得を目的に、画像セグメンテーション前処理を開発・比較し、再構成精度と処理効率を検証しているため、植物フェノタイピング手法が中心である。
abstractThe investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition.
Reproduction assets foundThe paper publicly releases its semantic segmentation dataset of 500 annotated soybean plant images, the 3D reconstruction model data from both preprocessing methods, and the authors' four segmentation network implementations (DeepLabv3+, Unet, PSPnet, HRnet) via Baidu pan links and GitHub repositories.Dataset · publicent research, LabelMe was used to annotate 500 images of soybean plants during
the vegetative period. The soybean plants and the calibration pad were labeled as a whole
and marked as “soybean”. The training set and testing set were divided in an 8:2 ratio. A
dataset was created for semantic segmentation, and the dataset link is
https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ (accessed on 4 August 2023). Ex-
tract code: dr2v.
Four deep-learning-based semantic segmentation models were selected as follows:
DeepLabv3+ [19], Unet [20], PSPnet [21] and HRnet [22]. These models were used to sep-
arate the soybean plants and the calibration pad from the background. Figure 3 shows the
network architeOpen asset ↗pdf-raw-page:5 lines:1-36Dataset · publichis study is pub-
licly available. These data can be found at: https://pan.baidu.com/s/13qpZsOl3bgmAgua2D441UQ
(accessed on 4 August 2023). Extract code: dr2v. Meanwhile, 3D reconstruction of soybean plant
images obtained using two image preprocessing methods was conducted, and the constructed
model data were linked as follows: https://pan.baidu.com/s/1UIBAts1dbjIiLvBv6YVpPA (accessed
on 4 August 2023). Extract code: 65xf.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
(a) (b)
(c) (d)
Figure A1. The confusion matrix diagram of the true value and the predicted value of the training
set. (a) DeepLabv3+; (b) Unet; (c) PSPnet; (d) HRNet.
(a)Open asset ↗pdf-layout-page:25 lines:1-32Code / 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 confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Premise We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting. Methods and results We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional. Conclusions The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.
Why it matches plant phenotyping methods植物標本の3D形状を取得するための野外撮像およびフォトグラメトリ手順を開発した研究であり、画像取得・形状抽出手法が中心です。
abstractWe developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.
Reproduction assets foundThe paper's field-captured plant specimen image sets and resulting 3D meshes are publicly deposited on MorphoSource (project 000494239), and the final 3D models for the five viable subject types are also publicly viewable on Sketchfab. These directly reproduce the paper's photogrammetry-based plant digitization outputsDataset · publicThe meshes and image sets relevant to this project are available at Morphosource ( https://www.morphosource.org/projects/000494239?locale=en ). The data were uploaded and managed by Alex Adkinson.Open asset ↗MorphoSource · 000494239lines:228-292Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.
Why it matches plant phenotyping methodsスマートフォン画像・深度情報と3D再構成を用いて樹高という植物構造形質を自動測定する手法を開発し、誤差評価とAndroidアプリ化まで行っているため、植物フェノタイピング手法が中心である。
abstractWe propose a tree height measurement method based on three-dimensional (3D) reconstruction.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides two paper-specific public assets: the source code of the TreeHeight prototype app on GitHub and the authors' annotated tree image dataset (300 annotated images augmented to 1000 pairs of color images, relative depth maps, and tree masks) on Google Drive. Both,Code · publicThe source code of the prototype app is publicly available on GitHub at https://github.com/LisaShen0509/Tree_Height_Measurement (accessed on 27 July 2023).Open asset ↗LisaShen0509/Tree_Height_Measurementlines:432-615Dataset · publicTree image dataset is available at https://drive.google.com/file/d/1kG6LWMOAiA2KvGF_suZ5cG_4C-udUV0m/view?usp=sharing (accessed on 27 July 2023).Open asset ↗lines:432-615Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Premise: Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible. Methods: plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs. Results: Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates. Discussion: In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.
Why it matches plant phenotyping methods植物群落の種存在と被覆率を、写真測量・SfMによるDSMから推定する手法を開発し、現地推定値と比較検証しているため、植物フェノタイピング手法が中心です。
abstractwe propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities
Reproduction assets foundThe authors publicly deposited the 3D image models (DSMs/virtual permanent plots) created in this study on FigShare, as stated in the Data Availability Statement. Other URLs (Agisoft, QGIS, R Metrics, GLORIA) are generic tools or cited prior work, not paper-specific assets.Dataset · publiccreate virtual permanent plots in rare and threatened plant communities. Applications in Plant Sciences
11(5): e11534. 10.1002/aps3.11534
This article is part of the special issue “Advances in Plant Imaging across Scales.”
DATA AVAILABILITY STATEMENT
3D image models created during this project can be found online on FigShare ( https://figshare.com/s/03a500bf7717afe3a9a6 ).
REFERENCES
Agisoft Helpdesk Portal
. 2022. 3D Model Reconstruction. Website: https://agisoft.freshdesk.com/support/solutions/articles/31000152092 [accessed 12 June 2023].
Barros, A.
, Aschero V., Mazzolari A., Cavieres L. A., and Pickering C. M.. 2020. Going off trails: How dispersed visitor use affects alpine vegetation. Open asset ↗FigSharelines:99-133Code / 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 · OpenAlex · checked 7 Sept 2026
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate ( A c ) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in A c between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in A c (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with A c and plant architecture traits were non-linearly related to A c . This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Why it matches plant phenotyping methods64台カメラのマルチビュー・ステレオ計測によるフェノミクスデータと、キャノピー光合成モデル・因子分析を統合した新規パイプラインの開発が中心であり、植物形態形質とキャノピー光合成の関係を定量化している。
titleDevelopment of a Novel 3D Canopy Modeling Pipeline
Reproduction assets foundThe paper's Data Availability section explicitly deposits the authors' 3D canopy modeling pipeline source code and the FastTracer ray tracing software used for the canopy photosynthesis simulations, both on public GitHub repositories. No phenotype/image datasets are explicitly deposited.Code · publicThe source code used in this study is available for non-commercial use and the code can be downloaded from https://github.com/PlantSystemsBiology/3DCanopyModelOpen asset ↗PlantSystemsBiology/3DCanopyModellines:224-402Code · publicThe FastTracer software is available from https://github.com/PlantSystemsBiology/fastTracerPublicOpen asset ↗PlantSystemsBiology/fastTracerPubliclines:224-402Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Aims Our understanding of the rhizosphere is limited by the lack of techniques for in situ live microscopy. Current techniques are either destructive or unsuitable for observing chemical changes within the pore space. To address this limitation, we have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles. Methods The transparency of smart soils was achieved using polymer particles with refractive index matching that of water. The surface of the particles was modified both to retain water and act as a local sensor to report on pore space pH via fluorescence emissions. Multispectral signals were acquired from the particles using a light sheet microscope, and machine learning algorithms predicted the changes and spatial distribution in pH at the surface of the smart soil particles. Results The technique was able to predict pH live and in situ within ± 0.5 units of the true pH value. pH distribution could be reconstructed across a volume of several cubic centimetres around plant roots at 10 μm resolution. Using smart soils of different composition, we revealed how root exudation and pore structure create variability in chemical properties. Conclusion Smart soils captured the pH gradients forming around a growing plant root. Future developments of the technology could include the fine tuning of soil physicochemical properties, the addition of chemical sensors and improved data processing. Hence, this technology could play a critical role in advancing our understanding of complex rhizosphere processes.
Why it matches plant phenotyping methods植物根圏のpHを生体根周辺で測定・3D再構成するセンサー基盤を開発し、精度検証まで行っており、植物状態の取得方法が研究の中心である。
abstractwe have developed artificial substrates, termed smart soils, that enable the acquisition and 3D reconstruction of chemical sensors attached to soil particles.
Reproduction assets foundThe paper's Data availability statement explicitly releases the authors' software for predicting pH from light-sheet image data (the machine-learning phenotyping analysis) on the authors' public GitHub repository SENSOIL. No separate phenotype/trait dataset or image deposit is stated; supplementary material is only a 'Code · public102 Plant Soil (2024) 500:91–104
1 3
Vol:. (1234567890)
Data availability Software developped for predicting pH
from image data is available at https://github.com/LionelDu-puy/SENSOIL/tree/main/pH_Release.Declarations
Competing interest There is no competing interest.
Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License, which per-
mits use, sharing, adaptation, distribution and reproduction in
any medium or format, as long as you give appropriate credit
to the original author(s) and the source,Open asset ↗SENSOILpdf-raw-page:12 lines:1-92Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
MicroscopyCell / cellular structureFlowerTissue2D/3D reconstructionGrowth / development / phenology
Floral spurs are invaginations borne by perianth organs (petals and/or sepals) that have evolved repeatedly in various angiosperm clades. They typically store nectar and can limit the access of pollinators to this reward, resulting in pollination specialization that can lead to speciation in both pollinator and plant lineages. Despite the ecological and evolutionary importance of nectar spurs, the cellular mechanisms involved during spur development have only been described in detail in a handful of species, primarily with respect to epidermal cells. These studies show that the mechanisms involved are taxon-specific. Using confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae) and showed that the process is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic (directional) cell expansion. The comparison with Aquilegia , another taxon of Ranunculaceae with spurred petals, revealed that the convergence in form between the spurs of both taxa is obtained by partially similar developmental processes. The analytical pipeline designed here is an efficient method to visualize in 3D each cell of a developing organ, paving the way for future comparative studies of organ morphogenesis in multicellular eukaryotes. Highlight A new method of 3D analysis of plant tissues at the cellular level revealed that spur morphogenesis in Staphisagria picta is marked by an early phase of dominant cell proliferation, followed by a phase of anisotropic cell expansion. Floral spur development is analysed for the first time quantitatively, taking into account all tissues composing the organ, namely epidermis and parenchyma.
Why it matches plant phenotyping methods共焦点顕微鏡と自動3D画像解析による発生器官の細胞形態・増殖・異方的伸長の定量化手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractUsing confocal microscopy and automated 3D image analysis, we studied spur morphogenesis in Staphisagria picta (Ranunculaceae)
Reproduction assets foundThe paper's 3D segmentation/visualization pipeline (PlantSeg + MorphoLibJ + homemade Python scripts) is the paper-specific computational analysis, and the authors explicitly state the automation and visualization code is publicly available on GitHub. No separate public phenotype dataset or image deposit is stated; dataCode · public”.
235 Cell outliers, i.e. the 5% largest and smallest cells in terms of volume, were filtered out. To
236 visualize the interior of the petals, we relied on the opacity of the dots or on virtual sections.
237 The code that allowed the automation of the segmentations and the visualization of the data is
238 available on github [https://github.com/paulinedlpch/morphogenesis].Open asset ↗paulinedlpch/morphogenesispdf-layout-page:6 lines:1-57Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.
Why it matches plant phenotyping methodsリンゴ樹の開花強度という植物形質をUAV RGB画像から推定するための3年間の画像・地上真値データセットであり、再利用可能なフェノタイピング基盤として中心的です。
abstractThe present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020.
Reproduction assets foundThis Data in Brief article describes the authors' own public Zenodo deposit containing the paper-specific UAV RGB images, flower cluster/floridity ground truth, and GCP files for the apple orchard flowering monitoring study, with direct download URL provided.Dataset · publicRepository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.6802308
Direct URL to data: https://zenodo.org/record/6802308#.YvvMFuxBz0pOpen asset ↗Zenodo · 10.5281/zenodo.6802308lines:1-51Code / dataset availability confirmedEurope PMC · bioRxiv · checked 7 Sept 2026
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotMicroscopyCell / cellular structureMorphology / geometry measurement2D/3D reconstruction
Abstract The streptophyte green algal class Zygnematophyceae is the immediate sister lineage to land plants. Their special form of sexual reproduction via conjugation might have played a key role during terrestrialization. Thus, studying Zygnematophyceae and conjugation is crucial for understanding the conquest of land. Moreover, sexual reproduction features are important for species determination. We present a phylogenetic analysis of a field‐sampled Zygnema strain and analyze its conjugation process and zygospore morphology, both at the micro‐ and nanoscale, including 3D‐reconstructions of the zygospore architecture. Vegetative filament size (26.18 ± 1.07 μm) and reproductive features allowed morphological determination of Zygnema vaginatum, which was combined with molecular analyses based on rbcL sequencing. Transmission electron microscopy (TEM) depicted a thin cell wall in young zygospores, while mature cells exhibited a tripartite wall, including a massive and sculptured mesospore. During development, cytological reorganizations were visualized by focused ion beam scanning electron microscopy (FIB‐SEM). Pyrenoids were reorganized, and the gyroid cubic central thylakoid membranes, as well as the surrounding starch granules, degraded (starch granule volume: 3.58 ± 2.35 μm3 in young cells; 0.68 ± 0.74 μm3 at an intermediate stage of zygospore maturation). Additionally, lipid droplets (LDs) changed drastically in shape and abundance during zygospore maturation (LD/cell volume: 11.77% in young cells; 8.79% in intermediate cells, 19.45% in old cells). In summary, we provide the first TEM images and 3D‐reconstructions of Zygnema zygospores, giving insights into the physiological processes involved in their maturation. These observations help to understand mechanisms that facilitated the transition from water to land in Zygnematophyceae.
Why it matches plant phenotyping methodsFIB-SEM/TEMによる3D画像取得と再構成が研究の中心で、接合胞子の形態・細胞内構造・体積変化を定量的に評価しているため、画像ベースの植物表現型計測として含める。
abstractincluding 3D‐reconstructions of the zygospore architecture
Reproduction assets foundThe paper's FIB-SEM image stacks of Zygnema vaginatum zygospores (the raw imaging data underlying the 3D reconstructions) are publicly deposited as supplementary Movies S2–S4 on figshare. No author analysis code or trained models are explicitly deposited; other supporting data are available only upon request.Dataset · publicof a young Zygnema vaginatum zygospore. https://figshare.com/s/637190524e99459e5b4e .
MOVIE S3. FIB SEM generated stack of frames of an intermediate stage of Zygnema vaginatum zygospore maturation. https://figshare.com/s/30519eddaac8274263aa .
MOVIE S4. FIB SEM generated stack of frames of a mature Zygnema vaginatum zygospore. https://figshare.com/s/11bdf9f4e7ee785e7f9f .
ACKNOWLEDGMENTS
We thank Sabrina Obwegeser, MSc (University of Innsbruck, Austria) for expert technical help in TEM sectioning and image generation. We also thank Jiří Neustupa for his company and assistance during the fieldwork.
DATA AVAILABILITY STATEMENT
The supporting data of the present study are available upon requestOpen asset ↗figsharelines:180-264Code / 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 · Crossref · bioRxiv · checked 15 Sept 2026
Background Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ . Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity among different dimensions; as a result, they have not been widely used in practice. Methods To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. Results We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. Conclusion The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
Why it matches plant phenotyping methods植物の3D細胞画像から形態を抽出するセグメンテーション手法を開発し、複数の植物データセットで性能をベンチマークしているため、植物フェノタイピング手法が中心である。
abstractWe propose CellStitch, a flexible pipeline that segments cells from 3D images
Reproduction assets foundThe paper provides explicit public availability for its CellStitch implementation and experiment-reproduction notebooks on GitHub, and evaluates on publicly available Arabidopsis thaliana image datasets, including the Arabidopsis 3D Digital Tissue Atlas hosted on OSF. These are paper-specific, public, actionable assetsCode · publicA python implementation of this method, CellStitch, is available at https://github.com/imyiningliu/300
cellstitch. The reproduction of all experiments presented herein can be accessed via https:
301
//github.com/imyiningliu/cellstitch/tree/main/notebooks.Open asset ↗imyiningliu/cellstitchpdf-raw-page:14 lines:1-70Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Digital phenotyping, particularly the use of plant 3D models, is a promising method for high‐throughput plant evaluation. Although many recent studies on the topic have been published, further research is needed to apply it to breeding research and other related fields. In this study, using a 3D model phenotyping system we developed, we reconstructed and analyzed 20 accessions of zoysiagrass (Zoysia spp.), including three species and their hybrid, over a period of 1 year. Artificial neural network with three hidden layers was able to effectively remove nonplant parts while retaining plant parts that were incorrectly removed using the cropping method, offering a robust and flexible approach for post‐processing of 3D models. The system also demonstrated its ability to accurately evaluate a range of traits, including height, area, and color using red green blue (RGB)‐based vegetation indices. The results showed a high correlation between the estimated volume obtained from voxel 3D model and dry weight, enabling its use as a non‐destructive method for measuring plant volume. In addition, we found that the green red normalized difference index from RGB‐based indices was similar to the commonly used normalized difference vegetation index in controlled illumination conditions. These results demonstrate the potential for three‐dimensional model phenotyping to facilitate plant breeding, particularly in the field of turfgrass and feed crops.
Why it matches plant phenotyping methods3Dモデル再構築、ニューラルネットワークによる後処理、RGB形質推定を中核とする植物フェノタイピングシステムの開発・検証研究である。
titleDevelopment of a digital phenotyping system using 3D model reconstruction for zoysiagrass
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code on GitHub (sandysan42/Zoysia3DModel), which reproduces the 3D-model-based phenotyping analysis (height, area, color, volume). No public phenotype dataset or trained model checkpoint is stated; supporting information is generic.Code · publicYAPAIBOON ET AL.
AC K N OW L E D G M E N T S
A part of this study is supported by JST CREST grant number
JPMJCR16O1.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Zoysia3DModel/.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154
RyoAkashi https://orcid.org/0000-0002-5651-8285
R E F E R E N C E S
Bienert, A., Hess, C., Maas, H.-G., & Von Oheimb, G. (2014). A voxel-
based technique to estimate the volume of trees from terrestriOpen asset ↗sandysan42/Zoysia3DModelpdf-raw-page:10 lines:1-78Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Despite its widespread employment as a highly efficient dimensionality reduction technique, limited research has been carried out on the advantage of Principal Component Analysis (PCA)–based compression/reconstruction of image data to machine learning-based image classification performance and storage space optimization. To address this limitation, we designed a study in which we compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models developed using training data from a number of original guava leaf images contained in a predefined amount of storage space (on the one hand), and a number of PCA compressed/reconstructed guava leaf images contained in the same amount of storage space (on the other hand), on the basis of four criteria – Accuracy, F1-Score, Phi Coefficient and the Fowlkes–Mallows index. Our approach achieved a 1:100 image compression ratio (99.00% image compression) which was comparatively much better than previous results achieved using other algorithms like arithmetic coding (1:1.50), wavelet transform (90.00% image compression), and a combination of three transform-based techniques – Discrete Fourier (DFT), Discrete Wavelet (DWT) and Discrete Cosine (DCT) (1:22.50). From a subjective visual quality perspective, the PCA compressed/reconstructed guava leaf images presented almost no loss of image detail. Finally, the CNN-RF model developed using PCA compressed/reconstructed guava leaf images outperformed the CNN-RF model developed using original guava leaf images by 0.10% accuracy increase, 0.10 F1-Score increase, 0.18 Phi Coefficient increase and 0.09 Fowlkes–Mallows increase.
Why it matches plant phenotyping methods植物病害の葉画像分類におけるPCA圧縮・再構成とCNN-RF解析を技術的に比較・評価しており、病害状態の画像ベース表現型取得・判定手法が中心です。
abstractwe compared the performances of two Convolutional Neural Network-Random Forest Algorithm (CNN-RF) guava leaf image classification models
Reproduction assets foundThe authors explicitly state that the Python scripts used for their PCA compression/reconstruction and CNN-RF classification analysis were pushed to a public GitHub repository (Enowtakang/PCA-Reconstruct). The guava leaf image dataset itself is from Chouhan et al. (2019), i.e., cited prior work, not a paper-specific deCode · publicoding process is required.
MATERIALS AND METHODS
System Specification
The scripts used for this research were written in Python 3
with system specifications as follows: 64-bit operating system,
x64-based processor, 8GB RAM, intel CORE i7 processor. After
the results were obtained, the scripts were pushed to a GitHub
repository (https://github.com/Enowtakang/PCA-Reconstruct).Open asset ↗Enowtakang/PCA-Reconstructpdf-raw-page:3 lines:85-110Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Understanding three‐dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field‐grown roots remains a challenge due to the unknown tradeoff between 3D root‐model quality and 3D root‐trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state‐of‐the‐art open‐source 3D model reconstruction pipelines on 12 contrasting genotypes of field‐grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch‐based Multi‐View Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi‐View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. In the second test, we compared the accuracy of 3D root‐trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP‐based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM‐based 3D reconstruction on the same dataset of 12 genotypes, with 5–10 replicates per genotype. The results revealed that (1) the average number of images needed to build a denser 3D model was reduced from 3000 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (DIRT/3D [COLMAP‐based 3D reconstruction]); (2) denser 3D models helped improve the accuracy of the 3D root‐trait measurement; (3) reducing the number of images can help resolve data storage problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・検証し、更新版DIRT/3Dによる根形質推定の精度と効率を評価しており、植物フェノタイピング手法が中心である。
titleComparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping
Reproduction assets foundThe paper publicly releases its analysis scripts on GitHub, demo workflows for reconstruction and trait computation, Docker/Singularity containers for DIRT/3D reconstruction and trait extraction, and manuscript data on CyVerse Data Commons via a permanent DOI.Code · publice computation of the software-
supported GPUs. The GPU model with the DELL workstation
was a GeForce RTX 2070 SUPER, NVIDIA Corporation
TU104, nvcc: NVIDIA (R) Cuda compiler driver. All the
pipelines were tested under the command-line interface to
generate related 3D root models in point cloud format. The
scripts are on GitHub (https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master, folder Compu-
tational_test_1).
25782703,
2023,
1,
Downloaded
from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.20068,
Wiley
Online
Library
on
[28/06/2023].
See
the
Terms
and
Conditions
(https://onlinelibrary.wiley.com/terms-and-conditions)
on
Wiley
Online
Library
for
rules
of
useOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:3 lines:1-114Code · publicm the University of Georgia to the University
of Arizona.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
PeOpen asset ↗Computational-Plant-Science/3D_model_reconstruction_demopdf-raw-page:12 lines:1-88Code · publicF I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
Peter Pietrzyk https://orcid.org/0000-0002-6794-8133
Alexander Bucksch https:/Open asset ↗Computational-Plant-Science/3D_model_traits_demopdf-raw-page:12 lines:1-88Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 7 Sept 2026
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
Why it matches plant phenotyping methods植物群集の3次元構造とマルチスペクトル情報を取得する自動フェノタイピングシステムをフィールド用に改変・実証しており、植物表現型取得法が研究の中心である。
abstractWe introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies
Reproduction assets foundThe paper's Data availability statement points to a public repository DOI (10.17616/R32P9Q, a re3data registry DOI) for the datasets presented in this study, which include the DWCP scan-derived morphological/physiological parameters, manual trait measurements, and vegetation data. No author analysis code or trained模型的公Dataset · publicThe datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found below: http://doi.org/10.17616/R32P9Q.Open asset ↗10.17616/R32P9Qpdf-page:11 lines:1-61Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 7 Sept 2026
Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.
Why it matches plant phenotyping methodsドローン画像とRTK-GNSSを用いた植物高のリアルタイム測定法を開発し、既存法との精度・処理時間比較で検証しており、植物表現型取得が研究の中心である。
abstractA new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed.
Reproduction assets foundThe paper's Data availability statement points to a public supplementary materials zip on the authors' site (nobuharaken.com) containing the datasets (drone images with GNSS data) generated and analysed in this plant-height measurement study. No separate analysis code repository is stated.Dataset · publicThe datasets generated and/or analysed during the current study are available in the https://nobuharaken.com/Open asset ↗nobuharaken.compdf-page:14 lines:1-60Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Expanded use of 3D imaging in organismal biology and paleontology has substantially enhanced the ability to visualize and analyze specimens. These techniques have improved our understanding of the anatomy of many taxa, and the integration of downstream computational tools applied to 3D datasets have broadened the range of analyses that can be performed (e.g., finite element analyses, geometric morphometrics, biomechanical modeling, physical modeling using 3D printing). However, morphological analyses inevitably present challenges, particularly in fossil taxa where taphonomic or preservational artifacts distort and reduce the fidelity of the original morphology through shearing, compression, and disarticulation, for example. Here, we present a compilation of techniques to build high-quality 3D digital models of extant and fossil taxa from 3D imaging data using freely available software for students and educators. Our case studies and associated step-by-step supplementary tutorials present instructions for working with reconstructions of plants and animals to directly address and resolve common issues with 3D imaging data. The strategies demonstrated here optimize scientific accuracy and computational efficiency and can be applied to a broad range of taxa.
Why it matches plant phenotyping methods植物を含む標本の3D画像データから高品質な形態モデルを構築する技術と手順が中心で、植物形態の取得・解析に再利用可能な方法論を提供している。
abstractHere, we present a compilation of techniques to build high-quality 3D digital models of extant and fossil taxa from 3D imaging data using freely available software for students and educators.
Reproduction assets foundThe paper's Data Availability statement points to a public Figshare deposit containing the authors' 3D object files and tutorial materials used in the case studies (including plant specimen reconstructions), making it a paper-specific, publicly actionable asset.Dataset · publicData is available as part of the Supplemental Materials and from Figshare: https://doi.org/10.6084/m9.figshare.21266568 .Open asset ↗Figshare · 10.6084/m9.figshare.21266568lines:72-101Code / dataset availability confirmedOpenAlex · bioRxiv · Europe PMC · checked 7 Sept 2026
Abstract Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation. Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and improved dataset balance in terms of shape for more precise outcomes.
Why it matches plant phenotyping methodsSNPからリンゴ画像を予測・再構成するオートエンコーダ手法自体が中心であり、果実形状などの植物表現型推定に直接関係する。
abstractThis study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array
Reproduction assets foundThe paper's Data availability section states that the code repository including notebooks and trained models is publicly available on GitHub at https://github.com/Fedjurrui/GenoDrawing, which is a paper-specific asset containing the authors' analysis code and trained phenotyping model weights. The image and SNP phenotvCode · publicThe code repository including notebooks, and models with their trained weights can be found in the
following github repository:
https://github.com/Fedjurrui/GenoDrawingOpen asset ↗Fedjurrui/GenoDrawingpdf-page:13 lines:1-54Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / field2D/3D reconstructionArchitecture / morphology / geometry
LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests.
Why it matches plant phenotyping methods森林植生の3D構造を対象に、疎なLiDAR観測から高忠実度の3D点群を再構成する手法を開発・評価しており、植物群落の幾何学的状態の取得が中心である。
abstractwe present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements.
Reproduction assets foundThe paper's 3D LiDAR forest point cloud inputs are publicly available OpenTopography datasets (Andrews Experimental Forest/Willamette NF 2008 and USFS Tahoe NF 2014), explicitly cited with DOIs in the Data Availability Statement. No author analysis code, trained models, or checkpoints are stated as available.Dataset · publicview & editing, S.D.; visualization, R.S.; super-
vision, S.D.; project administration, S.D.; All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data presented in this study are openly available in OpenTopog-
raphy at https://doi.org/10.5069/G92N506P and https://doi.org/10.5069/G9V122Q1.Acknowledgments: The authors, express their gratitude towards the OpenTopography Facility
with support from the National Science Foundation for publishing the open LiDAR data. The NSF
OpenTopography Facility provides the 2014 USFS Tahoe National Forest LiDAR and Andrews Ex-
perimental ForestOpen asset ↗OpenTopography · 10.5069/G92N506Ppdf-raw-page:24 lines:1-52Dataset · publicR.S.; super-
vision, S.D.; project administration, S.D.; All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data presented in this study are openly available in OpenTopog-
raphy at https://doi.org/10.5069/G92N506P and https://doi.org/10.5069/G9V122Q1.Acknowledgments: The authors, express their gratitude towards the OpenTopography Facility
with support from the National Science Foundation for publishing the open LiDAR data. The NSF
OpenTopography Facility provides the 2014 USFS Tahoe National Forest LiDAR and Andrews Ex-
perimental Forest and Willamette National Forest LiDAR (Aug 20Open asset ↗OpenTopography · 10.5069/G9V122Q1pdf-raw-page:24 lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
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 development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
ArabidopsisLeaf2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.
Why it matches plant phenotyping methods静的データから器官の発生時系列と成長軌跡を再構成するモデルベース手法が研究の中心であり、葉の形態形成・成長という植物表現型を推定している。
abstractWe introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data.
Reproduction assets foundThe paper's data availability statement explicitly provides three paper-specific public assets: a new version of the MorphoLeaf phenotyping application, COPASI and R scripts for temporal calibration parameter estimation, and the leaf datasets used in the analysis, each with a public URL.Dataset · publicLeaf datasets are available at https://doi.org/10.15454/BMELNY .Open asset ↗10.15454/BMELNYlines:210-440Code · publicCOPASI and R scripts used to estimate temporal calibration parameters are available at https://doi.org/10.15454/DPFU1T .Open asset ↗10.15454/DPFU1Tlines:210-440Code / dataset availability 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 confirmedarXiv · OpenAlex · checked 15 Sept 2026
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
Why it matches plant phenotyping methodsSentinel/GEDI等のリモートセンシング画像から樹冠高を推定する深層学習手法を開発し、外部データで検証しているため、植物形質取得法が中心である。
abstractwe developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map
Reproduction assets foundThe paper's primary phenotyping-relevant input is the GEDI L2A canopy height dataset (526,449 footprints over the Landes forest, 2020), explicitly downloaded from NASA's EarthDataSearch. This is a public, paper-specific sensor dataset directly used for the study's canopy height measurements and model training. No code,Dataset · publicwater bodies (Beck et al., 2020). Indeed, these
surfaces mirror the transmitted waveforms that have a pulse width of ~ 15 ns which
corresponds to a ~ 2.25 m wide waveform (Dubayah et al., 2020).
In total, 526,449 footprints from the GEDIv002 L2A product (Dubayah et al., 2021) were
downloaded from NASA’s EarthDataSearch website
(https://search.earthdata.nasa.gov/search) for this study, covering the entire area of interest
for 2020. Due to atmospheric perturbations, some waveforms could not be used to give
information on the vertical forest structure. Therefore, several filtering criteria were applied to
remove unusable waveforms: (1) When the quality_flag provided in the GEDI data was set toOpen asset ↗GEDIv002 L2Apdf-raw-page:6 lines:1-45Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証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。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, 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.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS: We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
Why it matches plant phenotyping methodsトウモロコシ器官の3D時系列形態を抽出・追跡する新規パイプラインを開発し、大規模データセットで精度検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize.
Reproduction assets foundThe paper's own PhenoTrack3D pipeline (source code and examples) is publicly available on GitHub under an open-source licence. Phenomenal (GitHub/Zenodo) is cited prior work used as an input pipeline, not a paper-specific asset; no public phenotype dataset or trained model deposit is stated.Code · publicThe source code and examples are available on Github ( https://github.com/openalea/phenotrack3d ) under an Open Source licence (Cecill-C).Open asset ↗openalea/phenotrack3dlines:189-246Code / dataset availability confirmedEurope PMC · Crossref · checked 13 Sept 2026
Background and aims The surface area of mesophyll cells (Smes) and chloroplasts (Sc) facing the intercellular airspace (IAS) are important parameters for estimating photosynthetic activity from leaf anatomy. Although Smes and Sc are estimated based on the shape assumption of mesophyll cells (MCs), it is questionable if the assumption is correct for rice MCs with concave-convex surfaces. Therefore, in this study, we establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images and to determine the correct shape assumption for the estimation of Smes and Sc based on 2-D section images. Methods We used serial section light microscopy to reconstruct 3-D representations of the IAS, MCs and chloroplasts in rice leaf tissue. Actual Smes and Sc values obtained from the 3-D representation were compared with those estimated from the 2-D images to find the correct shape-specific assumption (oblate or prolate spheroid) in different orientations (longitudinal and transverse sections) using the same leaf sample. Key results The 3-D representation method revealed that volumes of the IAS and MCs accounted for 30 and 70 % of rice leaf tissue excluding epidermis, respectively, and the volume of chloroplasts accounted for 44 % of MCs. The shape-specific assumption on the sectioning orientation affected the estimation of Smes and Sc using 2-D section images with discrepancies of 10-38 %. Conclusions The 3-D representation of rice leaf tissue was successfully reconstructed using serial section light microscopy and suggested that estimation of Smes and Sc of the rice leaf is more accurate using longitudinal sections with MCs assumed as oblate spheroids than using transverse sections with MCs as prolate spheroids.
Why it matches plant phenotyping methodsイネ葉組織の3D再構成法を開発し、2D画像による葉肉細胞・葉緑体の表面積推定と比較検証しており、植物形質の取得・推定法が中心である。
abstractwe establish a reconstruction method for the 3-D representation of the IAS in rice leaf tissue to calculate the actual Smes and Sc with 3-D images
Reproduction assets foundThe paper's supplementary materials include 3-D reconstruction videos of the IAS and chloroplast regions facing the IAS (Videos S1 and S2) and a table of shape assumptions used for curvature correction factors (Table S1), all directly reproducing this paper's phenotyping measurements. These are hosted online via the OyDataset · publicSupplementary data are available online at https://academic.oup.com/aob and consist of the following:Open asset ↗lines:139-146Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
Conventional crop height measurements performed using aerial drone images require the 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation times and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths during flight by linking the RTK-GNSS and aerial image capture points. A new calibration method is proposed to further improve the accuracy and stereo matching speed. Throught the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%, at flight altitudes of 10 and 20 m. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the errors were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.
Why it matches plant phenotyping methodsドローン画像とRTK-GNSSを用いた植物高のリアルタイム測定法を開発し、既存法との精度・速度比較で検証しており、表現型取得手法が中心である。
abstractThrought the comparison between the proposed method and conventional methods in natural world environments, wherein it reduced the error rates by 62.2% and 69.4%
Reproduction assets foundThe preprint declares that the datasets generated/analysed (drone images, GNSS data, and phenotyping measurements) are publicly available in the authors' supplementary materials zip archive. The Middlebury stereo dataset is a generic external resource, not paper-specific.Dataset · publicAvailability of data and material : The datasets generated and/or analysed during the current study are available in the https://nobuharaken.com/NatSciRep/supplementary_materials.zip repository.Open asset ↗nobuharaken.com/NatSciRep/supplementary_materials.ziplines:266-284Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without having a ground-truth point cloud. Finally, a counting method is presented where the density of seed centers in the 3D model allows 2D counts from multiple views to be effectively combined into a whole-panicle count. We demonstrate that using this method to estimate seed count and weight for sorghum outperforms count extrapolation from 2D images, an approach used in most state of the art methods for seeds and grains of comparable size.
Why it matches plant phenotyping methodsソルガム穂の3D再構成と種子計数という植物形質取得手法を開発し、再構成品質評価指標と種子数・重量推定を検証しており、フェノタイピング手法が中心である。
abstractwe present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments
Reproduction assets foundThe paper's authors publicly release their sorghum panicle stereo-image dataset (camera poses, human-labeled seed segmentations, panicle weights, seed counts) via the CMU AIIRA resources page, which is an allowed URL.Dataset · publicection, some unremoved husks were counted as seeds by the counting machine despite manual efforts to separate seeds from husks. We expect the effect on the ground truth to be small. The stereo images, camera poses, human-labeled seed segmentations, panicle weights, and human-counted seed counts can be found in our dataset 3 3
3
https://labs.ri.cmu.edu/aiira/resources/ .
Figure 8: (a) 100 sorghum panicles from 10 different sorghum species. (b) Our data collection system, a stereo camera attached to the UR5 robot arm. (c) Seeds were manually stripped and (d) counted using a seed counting machine.
IV-B 3D Reconstruction Quality
We assess the effectiveness of our approach with ablation tests usiOpen asset ↗lines:141-165Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Flower morphologies shape the accessibility to nectar and pollen, two major traits that determine plant-pollinator interactions and reproductive success. Melon is an economically important crop whose reproduction is completely pollinator-dependent and, as such, is a valuable model for studying crop-ecological functions. High-resolution imaging techniques, such as micro-computed tomography (micro-CT), have recently become popular for phenotyping in plant science. Here, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality. We generated high-quality 3D models of melon male and female flowers and compared the geometric measures. Micro-CT allowed for a relatively easy and rapid generation of 3D volumetric data on nectar, nectary, flower, and honey bee body sizes. A comparative analysis of male and female flowers showed a strong positive correlation between the nectar gland volume and the volume of the secreted nectar. We modeled the nectar level inside the flower and reconstructed a 3D model of the accessibility by honey bees. By combining data on flower morphology, the honey bee size and nectar volume, this protocol can be used to assess the flower accessibility to pollinators in a high resolution, and can readily carry out genotypes comparative analysis to identify nectar-pollination-related traits.
Why it matches plant phenotyping methodsマイクロCTを用いて花、蜜腺、蜜、ハナバチの3D形態・体積を取得し、花粉媒介関連形質を高精度に評価するプロトコルを実装・提示しており、表現型取得法が中心である。
abstractHere, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality.
Reproduction assets foundThe paper deposits its Python image-processing/phenotyping pipeline on GitHub and provides a supplement containing raw nectar/nectary measurement data (Tables S1–S4). Both are paper-specific, public, and actionable.Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cells11213452/s1 . Figure S1: pollen on Stamens in ♂ and ⚥ flower types at different magnifications; Table S1: nectar-related traits in male and female flowers; Table S2: correlation analysis between nectary volume, nectary cross-section area, nectary surface, flower width and nectar volume in the respective male, female and pooled melon flowerOpen asset ↗lines:83-224Code / dataset availability confirmedCrossref · checked 8 Sept 2026
Abstract Mitigating the effects of global change on biodiversity requires its understanding in the past. The main proxy of plant diversity, fossil pollen record, has a complex relationship to surrounding vegetation and unknown spatial scale. We explored both using modern pollen spectra in species-rich and species-poor regions in temperate Central Europe. We also considered the biasing effects of the trees by using sites in forests and open habitats in each region. Pollen samples were collected from moss polsters at 60 sites and plant species were recorded along two 1 km-transects at each site. We found a significant positive correlation between pollen and plant richness (alpha diversity) in both complete datasets and for both subsets from open habitats. Pollen richness in forest datasets is not significantly related to floristic data due to canopy interception of pollen rather than to pollen productivity. Variances (beta diversity) of the six pollen and floristic datasets are strongly correlated. The source area of pollen richness is determined by the number of species appearing with increasing distance, which aggregates information on diversity of individual patches within the landscape mosaic and on their compositional similarity. Our results validate pollen as a reconstruction tool for plant diversity in the past.
Why it matches plant phenotyping methods現代の花粉データと植物種多様性を比較し、花粉を過去の植物多様性再構築に用いる測定・推定手法を明示的に検証しているため、単なる生態学的なルーチン測定ではない。
abstractOur results validate pollen as a reconstruction tool for plant diversity in the past.
Reproduction assets foundThe paper's pollen and vegetation datasets are deposited on Zenodo and the analysis code is on GitHub, both with explicit availability statements.Dataset · publicPollen data are available in the Neotoma Palaeoecological database. The list of the Neotoma datasets, vegetation data and further data at https://doi.org/10.5281/zenodo.7233824 .Open asset ↗zenodo · 10.5281/zenodo.7233824lines:133-178Code · publicCode to reproduce the numerical analysis is available at https://github.com/vojtechabraham/SpatialScalingPollenDiversity/ .Open asset ↗github · vojtechabraham/SpatialScalingPollenDiversitylines:133-178Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
The generation of realistic plant and animal images from marker information could be a main contribution of artificial intelligence to genetics and breeding. Since morphological traits are highly variable and highly heritable, this must be possible. However, a suitable algorithm has not been proposed yet. This paper is a proof of concept demonstrating the feasibility of this proposal using ‘decoders’, a class of deep learning architecture. We apply it to Cucurbitaceae, perhaps the family harboring the largest variability in fruit shape in the plant kingdom, and to tomato, a species with high morphological diversity also. We generate Cucurbitaceae shapes assuming a hypothetical, but plausible, evolutive path along observed fruit shapes of C. melo . In tomato, we used 353 images from 129 crosses between 25 maternal and 7 paternal lines for which genotype data were available. In both instances, a simple decoder was able to recover expected shapes with large accuracy. For the tomato pedigree, we also show that the algorithm can be trained to generate offspring images from their parents’ shapes, bypassing genotype information. Data and code are available at https://github.com/miguelperezenciso/dna2image .
Why it matches plant phenotyping methodsDNA配列や親の形状から植物果実形状画像を生成する深層学習手法の概念実証であり、植物形態の取得・推定が研究の中心です。
titleComputer generation of fruit shapes from DNA sequence
Reproduction assets foundThe paper's cucurbit shape phenotyping inputs and analysis code are publicly available in the authors' dna2image GitHub repository, explicitly cited in the methods and data availability statement.Dataset · publichways. One pathway would be wild gourd (akin to pumpkin shape) scallop acorn; a
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second pathway would be wild gourd marrow straightneck zucchini cocozelle
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(Figure 1B). See also Figure 17 in (Paris 1989). We extracted contours from the
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‘contours.png’ file, based in (Paris 1989) and available in GitHub
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(https://github.com/miguelperezenciso/dna2image/blob/main/images/contours.png), using
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OpenCV library (Bradski 2000). Contours were centered and 500 pseudo-landmarks were
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obtained with the algorithm in Zingaretti et al. (2021). Next, contours were aligned with a
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generalized procrustes algorithm implemented in python package ‘procrustes’ (Meng et al.
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2022Open asset ↗https://github.com/miguelperezenciso/dna2image · contours.pngpdf-raw-page:5 lines:1-76Code · publicy, we have shown that very simple networks can be successfully trained in small
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datasets to accurately predict fruit images. Although much work remains to be done, this
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research opens new possibilities in the area of prediction of complex traits.
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Data availability statement
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All data and code are available at https://github.com/miguelperezenciso/dna2image.327
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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 September 22, 2022.
;
https://doi.org/10.1101/2022.09.19.Open asset ↗https://github.com/miguelperezenciso/dna2image.327 · dna2image.327pdf-raw-page:10 lines:1-73Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
Why it matches plant phenotyping methods植物の多視点画像取得、SfM再構成、質量推定を中核とするロボット型フェノタイピング手法の開発・実証であり、単なる生物学的測定ではない。
abstractWe describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each.
This study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method, in which only about 100 to 150 images are required for each model reconstruction. The 3D models reflect the realistic morphology and genuine color of the carpological materials. The 3D models are scaled to represent the true size of the materials even as small as 3 mm in diameter. The interfaces are interactive, in which the 3D models can be rotated in 360° to observe the structures and be zoomed to inspect the macroscopic details. This new platform is beneficial for developing a virtual herbarium of carpological collection which is thus the most important to botanical authentication and education.
Why it matches plant phenotyping methods植物の果実・種子等の形態を高解像度3D再構成するフォトグラメトリ手法とインタラクティブ基盤が中心であり、観察可能な植物形態を取得する方法論研究である。
abstractThis study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method
Reproduction assets foundThe authors publicly host the 3D models of carpological materials reconstructed in this study (100 models from the paper, over 250 released) in the 'Virtual Carpological Herbarium of Fruits and Seeds' online database. This is a paper-specific public asset directly reproducing the paper's phenotyping outputs. Software (Dataset · publicAll the 3D models were uploaded to an open online database ( https://syhuherbarium.sls.cuhk.edu.hk/collections/3d-specimen/ ; Username: syhuherbarium; Password: @CUHK). Currently, over 250 3D models were uploaded to the online database and more will be released in the future.Open asset ↗lines:94-123Code / 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 confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Confocal imaging is a well-established method for investigating plant phenotypes on the tissue and organ level. However, many differences are difficult to assess by visual inspection and researchers rely extensively on ad hoc manual quantification techniques and qualitative assessment. Here we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces. We successfully demonstrate the applicability of the approach using confocal imaging of aerial organs in Arabidopsis thaliana. Automatic identification of flower primordia using the surface curvature as an indication of outgrowth allows for high-throughput quantification of divergence angles and further analysis of individual flowers. We demonstrate the throughput of our method by quantifying geometric features of 1065 flower primordia from 172 plants, comparing auxin transport mutants to wild type. Additionally, we find that a paraboloid provides a simple geometric parameterisation of the shoot inflorescence domain with few parameters. We utilise parameterisation methods to provide a computational comparison of the shoot apex defined by a fluorescent reporter of the central zone marker gene CLAVATA3 with the apex defined by the paraboloid. Finally, we analyse the impact of mutations which alter mechanical properties on inflorescence dome curvature and compare the results with auxin transport mutants. Our results suggest that region-specific expression domains of genes regulating cell wall biosynthesis and local auxin transport can be important in maintaining the wildtype tissue shape. Altogether, our results indicate a general approach to parameterise and quantify plant development in 3D, which is applicable also in cases where data resolution is limited, and cell segmentation not possible. This enables researchers to address fundamental questions of plant development by quantitative phenotyping with high throughput, consistency and reproducibility.
Why it matches plant phenotyping methods植物組織の3D画像から形態形質を自動抽出・定量する手法を開発し、高スループット性と再現性を実証しているため、フェノタイピング手法が中心である。
abstractHere we present a method for quantitatively phenotyping large samples of plant tissue morphologies using triangulated isosurfaces.
Reproduction assets foundThe paper's data availability statement explicitly deposits all original source data (confocal phenotyping data of Arabidopsis shoot apical meristems) in the Cambridge Apollo repository and all analysis/segmentation/quantification scripts in a public Sainsbury Laboratory GitLab repository. Both are paper-specific,公开,直接Dataset · publicAll original source data files used in this study are available via the Cambridge University Apollo Repository ( https://doi.org/10.17863/CAM.82442 ).Open asset ↗Cambridge University Apollo Repository · 10.17863/CAM.82442lines:369-397Code · publicAll scripts and software for segmentation, quantification, analysis and visualisation are available via the Sainsbury Laboratory GitLab repository ( https://gitlab.com/slcu/teamHJ/publications/aahl_etal_2022 ).Open asset ↗Sainsbury Laboratory GitLab repositorylines:369-397Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
O_LICitrus come in diverse sizes and shapes, and play a key role in world culture and economy. Citrus oil glands in particular contain essential oils which include plant secondary metabolites associated with flavor and aroma. Capturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions. C_LIO_LIWe investigated the shape of citrus fruit of 51 accessions based on 3D X-ray CT scan reconstructions. Accessions include all three ancestral citrus species, accessions from related genera, and several interspecific hybrids. We digitally separate and compare the size of fruit endocarp, mesocarp, exocarp, and oil gland tissue. Based on the centers of the oil glands, overall fruit shape is approximated with an ellipsoid. Possible oil gland distributions on this ellipsoid surface are explored using directional statistics. C_LIO_LIThere is a strong allometry along fruit tissues; that is, we observe a strong linear relationship between the volume of any pair of major tissues. This suggests that the relative growth of fruit tissues with respect to each other follows a power law. We also observe that on average, glands distance themselves from their nearest neighbor following a square root relationship, which suggests normal diffusion dynamics at play. C_LIO_LIThe observed allometry and square root models point to the existence of biophysical developmental constraints that govern novel relationships between fruit dimensions from both evolutionary and breeding perspectives. Understanding these biophysical interactions prompt an exciting research path on fruit development and breeding. C_LI Societal Impact StatementCitrus are intrinsically connected to human health and culture, including preventing human diseases like scurvy, and inspiring sacred rituals. Citrus fruits come in a stunning number of different sizes and shapes, ranging from small clementines to oversized pummelos, and fruits display a vast diversity of flavors and aromas. These qualities are key in both traditional and modern medicine and the production of cleaning and perfume products. By quantifying and modeling overall fruit shape and oil gland distribution, we can gain further insight into citrus development and the impacts of domestication and improvement on multiple characteristics of the fruit.
Why it matches plant phenotyping methods3D X線CT再構成とデジタル分離により、柑橘果実の形状・組織体積・油腺分布を定量化しモデル化しており、植物表現型の取得・解析が研究の中心です。
abstractCapturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions.
Reproduction assets foundThe paper explicitly deposits its processed citrus X-ray CT 3D reconstructions, segmented tissues, oil gland point clouds, and ellipsoidal approximations in Dryad, and its full image-processing and analysis code on GitHub. Both are paper-specific, public, and actionable.Code · public357 All our code is available at the https://github.com/amezqui3/vitaminC_morphology repos-Open asset ↗GitHub · amezqui3/vitaminC_morphologypdf-page:19 lines:1-48Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
MaizeField / plotMesh / voxelLiDAR / point cloudRootWhole plant / canopy / plot / field2D/3D reconstructionRoot system architecture
Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・評価し、圃場トウモロコシ根の表現型取得性能を検証する研究であり、フェノタイピング手法が中心です。
titleComparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize
Reproduction assets foundThe paper's data availability statement provides two public, paper-specific assets: a GitHub repository with the scripts used to run the 3D reconstruction pipeline comparison, and a Cyverse archive containing all 60 resulting 3D root point cloud models from the twelve field-grown maize genotypes.Code · publicDATA AVAILABILITY STATEMENT
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
Cyverse link to all the 3D model results:
https://data.cyverse.org/dav-anon/iplant/home/lsx1980/3D_model_compare.zip
ACKNOWLEDGMENTS
The research was supported by the NSF CAREER Award No. 1845760 and USDOE ARPA-E ROOTS
Award Number DE-AR0000821 to A.B. Any Opinions, findings, and conclusions or recommendations
expressed in thisOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:6 lines:1-40Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物の細胞から個体レベルの3D形態を取得するX線顕微鏡法と試料調製・計算セグメンテーションを中心に開発・提示しており、植物表現型取得手法が明確に主題である。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level.
Reproduction assets foundThe authors deposited fly-through animations of 2D image stacks and 3D volume rendering animations of the XRM scans shown in the paper's figures on figshare, directly reproducing this paper's plant phenotyping imaging data. No author analysis code or trained model checkpoints were explicitly deposited.Dataset · publicCanada) was used for data integration, visualization, and animation of the scan data, and to export image data as 2D 16-bit Tag Image File Format (TIFF) stacks. Fly-through animations of 2D image stacks for scans shown in all Figures, as well as 3D volume rendering animations of selected scans, are available for download from ( https://figshare.com/s/944efc8832e47fd4f203 ).
Image analysis and segmentation
Data from XRM scans were segmented using Amira software and with the assistance of a Wacom tablet for manual segmentation, in addition to ORS Dragonfly Deep Learning Module 2021.1.0.977 which is free for noncommercial use. Segmentation for Figure 1D combined automated and manual methods in Open asset ↗figsharelines:87-114Code / 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 confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
The three-dimensional (3D) arrangement of cells in tissues provides an anatomical basis for analyzing physiological and biochemical aspects of plant and animal cellular development and function. In this study, we established a protocol for tissue clearing and 3D imaging in rice. Our protocol is based on three improvements: clearing with iTOMEI (clearing solution suitable for plants), developing microscopic conditions in which the Z step is optimized for 3D reconstruction, and optimizing cell-wall staining. Our protocol successfully 3D imaged rice shoot apical meristems, florets, and root apical meristems at cellular resolution throughout whole tissues. Using fluorescent reporters of auxin signaling in rice root tips, we also revealed the 3D distribution of auxin signaling events that are activated in the columella, quiescent center, and multiple rows of cells in the stele of the root apical meristem. Examination of cells with higher levels of auxin signaling revealed that only the central row of cells was connected to the quiescent center. Our method provides opportunities to observe the 3D arrangement of cells in rice tissues.
Why it matches plant phenotyping methodsイネ組織を対象に、組織透明化・最適化した3D顕微鏡撮像・細胞壁染色による細胞配置の取得法を開発しており、植物表現型の画像取得が中心的な技術貢献である。
abstractIn this study, we established a protocol for tissue clearing and 3D imaging in rice.
Reproduction assets foundThe paper's 3D imaging datasets (supplementary videos S1–S6 of rice SAMs, florets, anthers, and root tips, plus figure data) are publicly available via the MDPI supplementary materials link. No separate analysis code repository is mentioned.Dataset · publiccquisition, which took approximately 2 h for 150 μm in depth, the images were processed using LASX software (Leica Microsystems, Tokyo, Japan).
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Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/ijms23010040/s1 .
Click here for additional data file.
Author Contributions
M.S. and H.T. designed the research; M.S., H.A., Y.S. and S.M. performed the research; M.S. and H.T. analyzed the data; M.S. and H.T. wrote the paper. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supporOpen asset ↗lines:70-202Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・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。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 9 Sept 2026
A plant's architecture contributes to its ability to acquire resources and reduce mechanical load. Arabidopsis thaliana is the most common model plant in molecular biology, and there are several mutants and transgenic lines with modified plant architecture regulation, such as lazy1 mutants, which have reversed angles of lateral branches. Although some phenotyping methods have been used in larger agricultural plants, limited suitable methods are available for three-dimensional reconstruction of Arabidopsis, which is smaller and has more uniform surface textures and structures. An inexpensive, easily adopted three-dimensional reconstruction system that can be used for Arabidopsis is needed so that researchers can view and quantify morphological changes over time. We developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method, which uses a fixed camera to capture plant images from multiple directions while the plant slowly rotates. We then developed a script to autogenerate stack images from the obtained input movie and visualized the plant architecture by rendering the output stack image using the general bioimage analysis software. We successfully three-dimensionally and time-sequentially scanned wild-type and lazy1 mutant A. thaliana plants and measured the angles of the lateral branches. This non-contact, non-destructive method requires no specialized equipment and is space efficient, inexpensive and easily adopted by Arabidopsis researchers. Consequently, this system will promote three- and four-dimensional phenotyping of this model plant, and it can be used in combination with molecular genetics to further elucidate the molecular mechanisms that regulate Arabidopsis architecture.
Why it matches plant phenotyping methodsシロイヌナズナの3次元画像再構成と枝角度測定を目的とする、植物表現型取得システムの開発が中心である。
abstractWe developed a three-dimensional reconstruction system for A. thaliana using the visual volume intersection method
Reproduction assets foundThe authors publicly release their Python scripts for three-dimensional reconstruction, skeletonization, and curvature measurement (the paper's core phenotyping analysis) on their University of the Ryukyus website. No phenotype datasets or raw plant images are stated as deposited.Code · publicThis script was implemented in the Python language ( https://www.python.org/ ) and is executable in Windows and macOS. The script is only for academic purposes and freely available at our website ( https://ie.u-ryukyu.ac.jp/∼kunita/download_plant3d.html ).Open asset ↗lines:64-69Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSfMにより、植物体ごとの病害重症度と生体植生量を定量推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractestablish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a publicly accessible dataset at the author's website (zixuanqiu.com), matching an allowed URL. The study's UAV multispectral imagery, vegetation index data, and 11,400 sample-point annotations for arecanut yellow leaf disease are the paper-specific phenotypcDataset · publicData Availability Statement: Data available in a publicly accessible repository that does not issue
DOIs Publicly available datasets were analyzed in this study. This data can be found here:
http://www.zixuanqiu.com/nd.jsp?id=39#_np=110_649 (accessed on 20 October 2021).Open asset ↗zixuanqiu.compdf-page:19 lines:1-58Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
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Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
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thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
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experimental resources.
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Data Availability
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All the phenotypic data and images can be viewed and downloaded via the link
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(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
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References
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Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
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Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
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of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
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An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
MaizeX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系のX線CT画像から3Dモデルを構築し、71形質を抽出する計算パイプラインを開発・適用しており、表現型取得手法が研究の中心です。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper's custom image-processing and feature-extraction scripts are publicly available in the Topp-Roots-Lab GitHub repository, explicitly linked by the authors for reproducing the work. The phenotype data (Data File S1) is in supplements without a direct URL, and image volumes are only available upon request.Code · publicA more extensive description of trait implementations, all scripts used for image processing and feature extraction, and links to repositories required to reproduce the work are available at https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/Open asset ↗Topp-Roots-Lab/3d-root-crown-analysis-pipelinelines:42-50Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.
Why it matches plant phenotyping methodsリンゴ果実の数とサイズを動画から自動抽出するフォトグラメトリ手法を開発し、実験で精度を検証しており、植物フェノタイピング手法が中心である。
abstractThis work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number.
Reproduction assets foundThe authors explicitly state that acquired data, labelled images, code, and network weights for the apple phenotyping pipeline are publicly available on the 3DOM-FBK GitHub account, with a concrete URL given in reference [56]. This is a paper-specific, public, actionable asset covering the Mask R-CNN retraining code/权重Code · publicData Availability Statement: Data acquired and used in the presented experiments, labelled im-
ages, code, and network weights, are available to the scientific community at 3DOM-FBK-GitHub
[56].Open asset ↗pdf-page:16 lines:1-58Dataset · publicAcquired
data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.Open asset ↗pdf-page:1 lines:1-67Code / 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 confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Purpose Root growth, respiration, water uptake as well as root exudation induce biogeochemical patterns in the rhizosphere that can change dynamically over time. Our aim is to develop a method that provides complementary information on 3D root system architecture and biogeochemical gradients around the roots needed for the quantitative description of rhizosphere processes. Methods We captured for the first time the root system architecture of maize plants grown in rectangular rhizotrons in 3D using neutron computed laminography (NCL). Simultaneously, we measured pH and oxygen concentration using fluorescent optodes and the 2D soil water distribution by means of neutron radiography. We co-registered the 3D laminography data with the 2D oxygen and pH maps to analyze the sensor signal as a function of the distance between the roots and the optode. Results The 3D root system architecture was successfully segmented from the laminographic data. We found that exudation of roots in up to 2 mm distance to the pH optode induced patterns of local acidification or alkalization. Over time, oxygen gradients in the rhizosphere emerged for roots up to a distance of 7.5 mm. Conclusion Neutron computed laminography allows for a three-dimensional investigation of root systems grown in laterally extended rhizotrons as the ones designed for 2D optode imaging studies. The 3D information on root position within the rhizotrons derived by NCL explained measured 2D oxygen and pH distribution. The presented new combination of 3D and 2D imaging methods facilitates systematical investigations of a wide range of dynamic processes in the rhizosphere.
Why it matches plant phenotyping methodsNCLを用いた3D根系構造の取得・セグメンテーションが研究の中心であり、根系アーキテクチャという植物表現型を抽出する新しい画像計測法を開発・適用している。
abstractOur aim is to develop a method that provides complementary information on 3D root system architecture
Reproduction assets foundThe paper's Data availability statement deposits the raw and reconstructed 3D neutron computed laminography dataset of one maize root sample in the datacite repository at Helmholtz-Zentrum Berlin (DOI 10.5442/ND000004), a public, paper-specific phenotyping asset. No author analysis code or trained models are disclosed;Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG) under Grant Numbers OS 351/8-1 and TO 949/2-1.
Data availability The raw data and reconstructed 3D dataset
from neutron computed laminography of one maize sample is
available at the datacite repository from Helmholtz Centre Ber-
lin under http://doi.org/10.5442/ND000004.Declarations
Conflicts of interest The authors have no conflicts of interest
to declare that are relevant to the content of this article.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, asOpen asset ↗datacite · 10.5442/ND000004pdf-raw-page:11 lines:1-94Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
The Early Devonian Rhynie chert preserves the earliest terrestrial ecosystem and informs our understanding of early life on land. However, our knowledge of the 3D structure, and development of these plants is still rudimentary. Here we used digital 3D reconstruction techniques to produce the first well-evidenced reconstruction of the structure and development of the rooting system of the lycopsid Asteroxylon mackiei , the most complex plant in the Rhynie chert. The reconstruction reveals the organisation of the three distinct axis types – leafy shoot axes, root-bearing axes, and rooting axes – in the body plan. Combining this reconstruction with developmental data from fossilised meristems, we demonstrate that the A. mackiei rooting axis – a transitional lycophyte organ between the rootless ancestral state and true roots – developed from root-bearing axes by anisotomous dichotomy. Our discovery demonstrates how this unique organ developed and highlights the value of evidence-based reconstructions for understanding the development and evolution of the first complex vascular plants on Earth.
Why it matches plant phenotyping methods化石植物の根系構造と発生をデジタル3D再構成で推定する手法が研究の中心であり、植物形態の取得・再構成に該当する。
abstractHere we used digital 3D reconstruction techniques to produce the first well-evidenced reconstruction of the structure and development of the rooting system of the lycopsid Asteroxylon mackiei
Reproduction assets foundThe authors deposited photographs of the serial thick sections and peels used for phenotyping-style 3D reconstruction, plus the 3D reconstructions themselves, on Zenodo (DOI 10.5281/zenodo.4287297), which is an allowed URL and is explicitly cited as the generated dataset.Dataset · publicImages of the full series of thick sections were deposited on Zenodo ( http://doi.org/10.5281/zenodo.4287297 ).Open asset ↗Zenodo · 10.5281/zenodo.4287297lines:155-186Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Code / dataset availability confirmedarXiv · checked 13 Sept 2026
Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.
Why it matches plant phenotyping methods果実の位置・サイズ推定に必要な3Dセンサデータ取得を対象に、局所遮蔽回避と大域的視点計画を組み合わせる視点計画法を開発・評価しており、植物表現型取得が中心である。
abstractespecially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts
Reproduction assets foundThe paper's authors explicitly state that the source code of their combined local/global viewpoint planning system (used for fruit ROI coverage experiments) is publicly available on GitHub. OctoMap is a generic third-party library and is excluded.Code · publicThe source code of our system is available on GitHub 1 1
1
https://github.com/Eruvae/roi_viewpoint_planner .Open asset ↗Eruvae/roi_viewpoint_plannerlines:1-105Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
Why it matches plant phenotyping methods植物フェノタイピング用の時系列3D点群データセットを構築し、手動アノテーションと複数の解析ベースラインを提供することが中心であり、再利用可能なデータ基盤として適格です。
abstractwe present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks
Reproduction assets foundThe paper's core contribution is the Pheno4D dataset of labeled maize and tomato plant point clouds, explicitly stated to be freely and publicly available at the authors' Bonn repository. A companion public data-loader API (Python/C++) is also provided on GitHub. Both are paper-specific, public, and directly actionableDataset · publicThe dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.Open asset ↗https://www.ipb.uni-bonn.de/data/pheno4d/pdf-page:1 lines:1-65Code · publicWe provide the
code and the examples for loading the data at https://github.com/AIS-Bonn/data_loaders.Open asset ↗https://github.com/AIS-Bonn/data_loaderspdf-page:10 lines:1-57Code / dataset availability 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
5/25
.
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 confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha -1 . Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.
Why it matches plant phenotyping methodsUAV画像からキャノピー高を推定し、地上部バイオマスを予測するフォトグラメトリ手法の開発・検証・標準化が研究の中心であるため。
abstractHere we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's UAV aerial images, marker/plot coordinates, and harvested dry biomass weights at the NERC Environmental Information Data Centre, and the photogrammetric processing and statistical analysis code at Zenodo. Both are paper-specific, public, and have作者Dataset · publicThe data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre < https://doi.org/10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424 >.Open asset ↗NERC Environmental Information Data Centre · 10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424lines:222-246Code · publicCode for photogrammetric processing and statistical analysis is available at Zenodo < https://doi.org/10.5281/zenodo.4783021 >Open asset ↗Zenodo · 10.5281/zenodo.4783021lines:222-246Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
Why it matches plant phenotyping methods画像ベースの3D根形態フェノタイピング基盤を開発し、18形質を算出して手動測定と検証しているため、フェノタイピング手法が研究の中心である。
abstractWe address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique.
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 · publicOur open-source software is available to the whole plant science community on GitHub and can be deployed within a platform-agnostic Singularity/Docker container to be executed independently of the operating system ( Supplemental Data S D3 ; https://github.com/Computational-Plant-Science )Open asset ↗Computational-Plant-Sciencelines:184-191Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Laboratory / benchtopRoot2D/3D reconstructionSegmentationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. RESULTS: We developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. CONCLUSIONS: Our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
Why it matches plant phenotyping methods根系画像の深層セグメンテーションと3D装置を開発し、画像から根系形態・成長動態を自動抽出する方法が研究の中心である。
abstractHere we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.
Reproduction assets foundThe paper publicly releases its root segmentation image/annotation datasets, hardware files, and analysis code via GitHub repositories, plus supporting data in GigaDB. Three qualifying paper-specific assets with allowed URLs are listed; the GigaDB deposit (10.5524/100911) is paper-specific but its URL is not in the允许edCode · publicThe source code corresponding to ChronoRoot imaging controller, namely, the web interface to check and set up the image acquisition parameters:
Project name: ChronoRoot: Module Controller
Project home page: https://github.com/ThomasBlein/ChronoRootControlOpen asset ↗https://github.com/ThomasBlein/ChronoRootControllines:185-222Dataset · publicThe 2 datasets of images and annotations described in the Datasets section, as well as the 3D printing and laser cutting files, are publicly available at https://github.com/ThomasBlein/ChronoRootModuleHardware under the CERN Open Hardware License Version 2—Strongly Reciprocal licence.Open asset ↗https://github.com/ThomasBlein/ChronoRootModuleHardwarelines:223-262Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high‐throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do‐it‐yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high‐end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r 2 = 0.96, Laser r 2 = 0.86; projected leaf area: EasyDCP r 2 = 0.96, Laser r 2 = 0.96). EasyDCP is an open‐source software tool to measure phenotypic traits of container plants with high‐throughput and low labour and financial costs.
Why it matches plant phenotyping methodsEasyDCPは、フォトグラメトリによる3D植物表現型取得と自動形質抽出のためのソフトウェア・撮像ワークフローを開発し、レーザースキャナおよび実測値と比較検証しており、方法が研究の中心です。
abstractWe introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data.
Reproduction assets foundThe paper's EasyDCP source code is publicly available on GitHub, and the performance-test data (source images, point clouds, trait data, R files) plus code and documentation are archived on Zenodo.Code · public| 1681
Methods in Ecology and Evolu on
FELDMAN et al.
EasyDCP_Creation (Section 2.2), which creates a 3D point
cloud from 2D images; and EasyDCP_Analysis (Section 2.3),
which analyses that point cloud and performs trait calcula-
tion. EasyDCP source code and documentation are available
on GitHub (https://github.com/UTokyo-FieldPhenomics-Lab/EasyDCP).2.1 | Image acquisition
Plants must be imaged prior to EasyDCP measurement, and the
image acquisition area can be set up according to the user's needs
(Figure 2a,b). The image acquisition area should have as little in-
clination as possible. One printed target page (.pdf provided with
the software) must be placed in a corner oOpen asset ↗UTokyo-FieldPhenomics-Lab/EasyDCPpdf-raw-page:3 lines:1-111Dataset · public.
PEER REVIEW
The peer review history for this article is available at https://publo
ns.
com/publon/10.1111/2041-210X.13645.
DATA AVAILABILITY STATEMENT
Data from the performance test (source images, point clouds, trait
data and R files), EasyDCP source code, example scripts and detailed
documentation are archived using Zenodo https://doi.org/10.5281/zenodo.4756537 (Feldman et al., 2021).
ORCID
Alexander Feldman https://orcid.org/0000-0002-1162-5917
Haozhou Wang https://orcid.org/0000-0001-6135-402X
Yuya Fukano https://orcid.org/0000-0001-9057-4742
Yoichiro Kato https://orcid.org/0000-0002-7131-0220
Seishi Ninomiya https://orcid.org/0000-0002-2123-4354
Wei Guo https://orcid.org/0000-0002-Open asset ↗Zenodo · 10.5281/zenodo.4756537pdf-raw-page:6 lines:1-102Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.
Why it matches plant phenotyping methods三次元キャノピー再構築から葉角度分布を抽出し、光遮断モデルを比較・検証することが研究の中心であり、植物体の構造形質を定量化している。
abstractanalyzed θ distribution in the canopies from three-dimensional canopy reconstructions
Reproduction assets foundThe paper's K and FIPAR light interception models were coded in Matlab and implemented as a BioMA component; the authors state the source code and standalone executable are freely available on Zenodo (record 3820386). The SiriusQuality GitHub link is a general model repository, not paper-specific analysis code.Code · publicAll K and FIPAR models presented here were coded in Matlab and we also developed an independent executable component in the BioMA software framework ( http://www.biomamodelling.org ), which can easily be extended and coupled with CGMs. The source code and the standalone executable of the BioMA component are freely available at https://zenodo.org/record/3820386 .Open asset ↗Zenodo · 3820386lines:906-951Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%.
Why it matches plant phenotyping methodsUAVフォトグラメトリとLiDARによる樹冠パラメータ推定を比較・精度評価しており、植物形態形質の取得手法が研究の中心である。
abstracta detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary
Reproduction assets foundThe authors state that the UAV-LiDAR and photogrammetric point clouds used for tree canopy parameter estimation are freely available as Supplementary Materials via an MDPI link, making the paper's core phenotyping sensor data (3D point clouds) publicly accessible.Dataset · publicThe LiDAR and photogrammetric point clouds used in this research
are freely available (https://susy.mdpi.com/user/manuscripts/displayFile/d71a32682356d1cece4c0Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be used for phenotypic analysis. In this paper, a method is proposed to reconstruct wheat 3D model based on semantic information. The method can generate the corresponding 3D point cloud model of wheat according to the semantic description. First, an object detection algorithm is used to detect the characteristics of some wheat phenotypes during the growth process. Second, the growth environment information and some phenotypic features of wheat are combined into semantic information. Third, text-to-image algorithm is used to generate the 2D image of wheat. Finally, the wheat in the 2D image is transformed into an abstract 3D point cloud and obtained a higher precision point cloud model using a deep learning algorithm. Extensive experiments indicate that the method reconstructs 3D models and has a heuristic effect on phenotypic analysis and breeding research by deep learning.
Why it matches plant phenotyping methods小麦の表現型解析を目的とした3D点群再構成手法の開発であり、表現型情報を用いた画像・深層学習ベースの形状復元が中心的な技術貢献である。
abstracta method is proposed to reconstruct wheat 3D model based on semantic information
Reproduction assets foundThe paper's Data Availability Statement links a public Google Drive folder containing the authors' wheat dataset (RGB images, object-detection labels, textual annotations, and point cloud markers) used for the phenotyping pipeline. No code or trained model deposit is stated.Dataset · publicData Availability Statement: The data are available online at https://drive.google.com/drive/
folders/1ko6rlE1LThkNG_fcm5C12LcBaUWwdsPc?usp=sharing.Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedCrossref · checked 13 Sept 2026
The ability to quantify changes in the structural complexity of reefs and individual coral colonies that build them is vital to understanding, managing, and restoring the function of these ecosystems. However, traditional methods for quantifying coral growth in situ fail to accurately quantify the diversity of morphologies observed both among and within species that contribute to topographical complexity. Three-dimensional (3D) photogrammetry has emerged as a powerful tool for the quantification of reefscape complexity but has yet to be broadly adopted for quantifying the growth and morphology of individual coral colonies. Here we debut a high-throughput method for colony-level 3D photogrammetry and apply this technique to explore the relationship between linear extension and other growth metrics in Acropora cervicornis . We fate-tracked 156 individual coral transplants to test whether initial growth can be used to predict subsequent patterns of growth. We generated photographic series of fragments in a restoration nursery immediately before transplanting to natural reef sites and re-photographed coral at 6 months and 1 year post-transplantation. Photosets were used to build 3D models with Agisoft Metashape, which was automated to run on a high-performance computing system using a custom script to serially process models without the need for additional user input. Coral models were phenotyped in MeshLab to obtain measures of total linear extension (TLE), surface area, volume, and volume of interstitial space (i.e., the space between branches). 3D-model based measures of TLE were highly similar to by-hand measurements made in the field ( r = 0.98), demonstrating that this method is compatible with established techniques without additional in water effort. However, we identified an allometric relationship between the change in TLE and the volume of interstitial space, indicating that growth in higher order traits is not necessarily a linear function of growth in branch length. Additionally, relationships among growth measures weakened when comparisons were made across time points, implying that the use of early growth to predict future performance is limited. Taken together, results show that 3D photogrammetry is an information rich method for quantifying colony-level growth and its application can help address contemporary questions in coral biology.
Why it matches plant phenotyping methodsサンゴ個体群の3Dフォトグラメトリによる形態・成長形質取得法を開発し、自動処理と既存手法との比較検証を行っているため、植物体（サンゴ）の表現型計測法が中心である。
abstractHere we debut a high-throughput method for colony-level 3D photogrammetry
Reproduction assets foundThe paper's phenotype dataset (TLE, SA, V, Vinter for 156 A. cervicornis colonies) is publicly deposited in the authors' GitHub repository Frontiers3Dmorphology, and the custom Metashape automation scripts are in Coral3DPhotogram; both are paper-specific, public, and actionable.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/wyattmillion/Frontiers3Dmorphology .Open asset ↗Frontiers3Dmorphologylines:321-372Code · publicAll bioinformatic scripts used to run Metashape on the command line can be found at https://github.com/wyattmillion/Coral3DPhotogram .Open asset ↗Coral3DPhotogramlines:271-276Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Monitoring of environmental restoration is essential to communicate progress and improve outcomes of current and future projects, but is typically done in a very limited capacity due to budget and personnel constraints. Unoccupied aerial vehicles (UAVs) have been used in a variety of natural and human-influenced environments and have been found to be time- and cost-efficient, but have not yet been widely applied to restoration contexts. In this study, we evaluated the utility of UAVs as an innovative tool for monitoring tidal marsh restoration. We first optimized methods for creating high-resolution orthomosaics and Structure from Motion digital elevation models from UAV imagery by conducting experiments to determine an optimal density of ground control points (GCPs) and flight altitude for UAV monitoring of topography and new vegetation. We used elevation models and raw and classified orthomosaics before, during, and after construction of the restoration site to communicate with various audiences and inform adaptive management. We found that we could achieve 1.1 cm vertical accuracy in our elevation models using 2.1 GCPs per hectare at a flight altitude of 50 m. A lower flight altitude of 30 m was more ideal for capturing patchy early plant cover while still being efficient enough to cover the entire 25-hectare site. UAV products were valuable for several monitoring applications, including calculating the volume of soil moved during construction, tracking whether elevation targets were achieved, quantifying and examining the patterns of vegetation development, and monitoring topographic change including subsidence, erosion, and creek development. We found UAV monitoring advantageous for the ability to survey areas difficult to access on foot, capture spatial variation, tailor timing of data collection to research needs, and collect a large amount of accurate data rapidly at relatively low cost, though with some compromise in detail compared with field monitoring. In summary, we found that UAV data informed the planning, implementation and monitoring phases of a major landscape restoration project and could be valuable for restoration in many habitats.
Why it matches plant phenotyping methodsUAV画像から植生被覆・発達を定量化するための撮影高度、GCP密度、オルソモザイクおよびSfM手法を最適化・精度評価しており、植物状態の取得技術が実質的に中心である。
abstractWe first optimized methods for creating high-resolution orthomosaics and Structure from Motion digital elevation models from UAV imagery by conducting experiments to determine an optimal density of ground control points (GCPs) and flight altitude for UAV monitoring of topography and new vegetation.
Reproduction assets foundThe paper's data availability statement points to a public Figshare collection (DOI 10.6084/m9.figshare.c.5226785.v1) containing the study's UAV-derived datasets (orthomosaics, elevation models, vegetation analyses) for the Hester Marsh restoration monitoring. This is a paper-specific, publicly accessible asset. No作者分析Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.c.5226785.v1 (Figshare).Open asset ↗Figshare · 10.6084/m9.figshare.c.5226785.v1lines:628-638Code / dataset availability confirmedEurope PMC · Crossref · OpenAlex · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types, each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice (Oryza sativa) and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis (Arabidopsis thaliana). In addition, field-emission scanning electron microscopy observation revealed the presence of various cell types in haustoria. Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides cellular and structural insights into haustoria of S. hermonthica and P. japonicum and lays the foundation for understanding haustorium function.
Why it matches plant phenotyping methods植物器官の3次元画像再構成を中心に、ハウストリアの内部構造と細胞配置を可視化しており、形態状態の取得・抽出が研究の主要部分である。
titleThree-dimensional reconstructions of haustoria in two parasitic plant species in the Orobanchaceae
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 · publicAfter automated alignment adjustment, section alignment was manually checked and misaligned sections were re-registered by changing the registration parameters. The tools are available at https://github.com/yk-szk/ssrvtools .Open asset ↗yk-szk/ssrvtoolslines:86-98Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系を対象に、X線CTによる3Dモデル化と計算パイプラインで71形質を抽出し、根引抜き力との較正・解釈まで行う、中心的な表現型計測手法研究である。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper states that the authors' scripts for X-ray CT image processing and root feature extraction (batch-segmentation, batch-skeleton) are publicly available in the Topp-Roots-Lab GitHub repository. Raw phenotype data is said to be in Supplemental File 1, but no public URL for it is provided in the supplied blocks.Code · publicestimated by taking the 2D projection of the 3D volume, then
185 calculated using a similar approach to that described in Grift et al., 2011. DensityS features are
186 computationally similar to plant compactness traits described in Yang et al., 2014. Scripts used
187 for image processing and feature extraction are available at https://github.com/Topp-Roots-Lab/
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189 Statistical Analysis
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191 All downstream (i.e. post feature extraction) analysis was performed in the R statistical
192 computing environment. Initially, principal component analysis using all 71 3D roots traits was
193 used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3
19Open asset ↗Topp-Roots-Labpdf-layout-page:5 lines:1-56Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.
Why it matches plant phenotyping methodsUAV斜め写真測量と3D点群・ボクセル解析を用いて森林キャノピーのLAIを推定する手法を開発・比較検証しており、植物形態形質の取得方法が研究の中心である。
abstractTo explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes
Reproduction assets foundThe paper's authors publicly released the voxelization/LAI extraction code on GitHub; phenotype data (UAV images, point clouds, LAI-2200 measurements) are only available upon request.Code · publicData Availability Statement: The source codes developed in this study were donated to GitHub
(https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐
grammetry (accessed on 7 January 2021)). And the data used to support the findings of this study
are available from the corresponding author upon request.Open asset ↗https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐pdf-page:15 lines:1-59Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Abstract We propose a novel, canopy density estimation solution using a three‐dimensional (3D) ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous‐time 3D SLAM (simultaneous localization and mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data are processed through a Continuous‐Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analyzed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 km, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (relative root mean square error) per vineyard panel, for acquisition speeds of 5–6 km/h, and under half the standard deviation in estimated densities when compared with an industry standard gap‐fraction based solution. The code and field data sets are available at https://github.com/csiro-robotics/agscan3d .
Why it matches plant phenotyping methods3D LiDARとSLAMを用いてブドウ樹冠密度を推定する取得・解析手法を開発し、シミュレーションおよび大規模圃場実験で反復性と既存法を検証しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel, canopy density estimation solution using a three‐dimensional (3D) ray cloud representation for perennial horticultural crops at the field scale.
Reproduction assets foundThe paper's abstract explicitly states that the authors' code and field datasets (AgScan3D lidar data used for canopy density estimation) are publicly available at the CSIRO Robotics GitHub repository, which matches the allowed URL.Code · publicThe code and field datasets are available at
https://github.com/csiro-robotics/agscan3d .Open asset ↗csiro-robotics/agscan3dlines:1-63Code / dataset availability confirmedCrossref · checked 13 Sept 2026
A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana , enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.
Why it matches plant phenotyping methods機械学習による3Dデジタル画像解析と細胞・組織レベルの定量的アトラス構築が研究の中心であり、胚珠の形態・成長パターンを抽出する植物フェノタイピング手法に該当する。
abstractUsing machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana
Reproduction assets foundThe paper's 3D digital ovule datasets (raw images, PlantSeg predictions, segmented cells, annotated 3D cell meshes, and csv attribute files) are publicly deposited in EMBL-EBI BioStudies under accessions S-BSST475, S-BSST498, S-BSST497, and S-BSST513. Additionally, the PlantSeg 'generic_confocal_3D_unet' model was re/Dataset · publicAccession S-BSST475: the wild-type high-quality dataset and the additional dataset with more segmentation errors.Open asset ↗S-BSST475lines:655-758Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs.In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain.We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender).Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images.Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images.We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.
Why it matches plant phenotyping methods果実の3D再構成と体積回帰を対象とする教師なしドメイン適応手法を開発しており、植物器官の形態・サイズ推定に用いるフェノタイピング手法が研究の中心である。
abstractIn this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction
Reproduction assets foundThe paper's synthetic banana image-volume dataset (25,000 image-volume pairs) is publicly deposited at the authors' project site, and the pipeline/training code is deposited on the authors' GitHub. Both are paper-specific, public, and actionable.Code · publicThe code used to create the dataset for this study has been deposited on github at https://github.com/zanehartley . The code for the neural networks used for this study has been deposited on github at https://github.com/zanehartley .Open asset ↗github.com/zanehartleylines:158-160Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
SoybeanSugar beetLiDAR / point cloudLeafMorphology / geometry measurement2D/3D reconstructionLeaf traits
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Why it matches plant phenotyping methods3D点群から植物葉面を再構成し、ノイズ耐性と葉面積推定の安定性を従来法と比較検証する手法開発研究であり、植物フェノタイピング手法が中心です。
abstractwe developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy
Reproduction assets foundThe paper's authors explicitly state that the Python implementation of their proposed leaf surface reconstruction method is publicly available on GitHub. No public deposit of the 3D point cloud phenotype data (soybean/sugar beet scans) is mentioned, so only the code qualifies as a paper-specific public asset.Code · publicWe implemented the algorithm for the proposed method in Python ( http://www.python.org/ ). The source code is at https://github.com/oceam/LeafSurfaceReconstruction .Open asset ↗oceam/LeafSurfaceReconstructionlines:46-55Code / dataset availability confirmedEurope PMC · Crossref · bioRxiv · checked 15 Sept 2026
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High quality 3D volume data from our enhanced methods facilitate more sophisticated and effective computational segmentation and analyses than have previously been employed for X-ray based imaging. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物試料の細胞から個体までを対象に、X線顕微鏡によるマルチスケール3D画像取得、試料調製、計算セグメンテーション、相関イメージングの方法論を中心に提示しており、植物形態の取得・解析法が明確に中心です。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level.
Reproduction assets foundThe preprint points to a public figshare collection containing the paper's high-resolution XRM image stacks ('flythroughs') and videos of the 3D plant datasets, which directly reproduce the paper's phenotyping imaging measurements. No author analysis code or trained model checkpoint is explicitly deposited; the deep-seDataset · publicof these improved techniques will
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high-resolution image stacks (“flythroughs”) and videos portraying the 3D data sets can be found here:
https://figshare.com/s/944efc8832e47fd4f203
.
CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted December 22, 2020.
;
https://doi.org/10.1101/2020.12.18.423480
doiOpen asset ↗figsharepdf-raw-page:4 lines:1-64Code / 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 confirmedCrossref · checked 9 Sept 2026
Autonomous analysis of plants, such as for phenotyping and health monitoring etc., often requires the reliable identification and localization of single leaves, a task complicated by their complex and variable shape. Robotic sensor platforms commonly use depth sensors that rely on either infrared light or ultrasound, in addition to imaging. However, infrared methods have the disadvantage of being affected by the presence of ambient light, and ultrasound methods generally have too wide a field of view, making them ineffective for measuring complex and intricate structures. Alternatives may include stereoscopic or structured light scanners, but these can be costly and overly complex to implement. This article presents a fully computer-vision based solution capable of estimating the three-dimensional location of all leaves of a subject plant with the use of a single digital camera autonomously positioned by a three-axis linear robot. A custom trained neural network was used to classify leaves captured in multiple images taken of a subject plant. Parallax calculations were applied to predict leaf depth, and from this, the three-dimensional position. This article demonstrates proof of concept of the method, and initial tests with positioned leaves suggest an expected error of 20 mm. Future modifications are identified to further improve accuracy and utility across different plant canopies.
Why it matches plant phenotyping methods単一カメラとロボットを用いて植物葉の三次元位置を推定するコンピュータビジョン手法の開発・概念実証であり、葉の形態・構造 phenotype の取得が中心。
abstractThis article presents a fully computer-vision based solution capable of estimating the three-dimensional location of all leaves of a subject plant with the use of a single digital camera autonomously positioned by a three-axis linear robot.
Reproduction assets foundThe paper's Supplementary Materials state that all code, collected data, and the trained neural network are publicly available in the authors' GitHub repository, directly reproducing this paper's leaf position estimation phenotyping analysis. The other GitHub URLs are third-party tutorial/model-zoo resources, not paperCode · publicCode written for this article, all data collected, and the trained neural network can be accessed in this GitHub Repository to enable our experiments to be reproduced: https://github.com/JamAJB/Plant-Leaf-Position-Estimation-with-Computer-Vision .Open asset ↗JamAJB/Plant-Leaf-Position-Estimation-with-Computer-Visionlines:188-206Code / dataset availability confirmedOpenAlex · Europe PMC · checked 9 Sept 2026
Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.
Why it matches plant phenotyping methodsソルガムのRGB画像から3D植物体を自動再構成し、光 interception 特性を推定する高スループット手法の開発が中心であるため。
abstractWe develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos.
Reproduction assets foundThe paper's voxel carving analysis code is explicitly stated as publicly available on GitHub. The raw images, 3D reconstructions, and trait values were only promised for future DataDryad deposit with no URL, so they are not actionable. The FigShare deposit contains genetic marker data (molecular omics), not phenotypingCode · publicThe code is available at https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗https://github.com/cropsinsilico/SorghumVoxelCarvinglines:289-474Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Optical Projection Tomography (OPT) is a powerful three-dimensional imaging technique used for the observation of millimeter-scaled biological samples, compatible with bright-field and fluorescence contrast. OPT is affected by spatially variant artifacts caused by the fact that light diffraction is not taken into account by the straight-light propagation models used for reconstruction. These artifacts hinder high-resolution imaging with OPT. In this work we show that, by using a multiview imaging approach, a 3D reconstruction of the bright-field contrast can be obtained without the diffraction artifacts typical of OPT, drastically reducing the amount of acquired data, compared to previously reported approaches. The method, purely based on bright-field contrast of the unstained sample, provides a comprehensive picture of the sample anatomy, as demonstrated in vivo on Arabidopsis thaliana and zebrafish embryos. Furthermore, this bright-field reconstruction can be implemented on practically any multi-view light-sheet fluorescence microscope without complex hardware modifications or calibrations, complementing the fluorescence information with tissue anatomy.
Why it matches plant phenotyping methods多視点取得と画像融合による3D明視野再構成法を開発し、Arabidopsisの解剖学的形態を実証しているため、植物形態の取得手法が中心です。
abstractIn this work we show that, by using a multiview imaging approach, a 3D reconstruction of the bright-field contrast can be obtained without the diffraction artifacts typical of OPT
Reproduction assets foundThe paper's authors state that the Python sample code implementing their bright-field multi-view reconstruction (used for the Arabidopsis thaliana and zebrafish phenotyping/imaging analysis) is publicly available on GitHub under the authors' account. The GitHub URL in the text contains formatting artifacts and does notCode · publicData processing was performed in Python; a sample code is available on GitHubOpen asset ↗pdf-page:7 lines:1-55Code / dataset availability confirmedEurope PMC · OpenAlex · bioRxiv · checked 14 Sept 2026
Parasitic plants infect other plants by forming haustoria, specialized multicellular organs consisting of several cell types each of which has unique morphological features and physiological roles associated with parasitism. Understanding the spatial organization of cell types is, therefore, of great importance in elucidating the functions of haustoria. Here, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species, the obligate parasite Striga hermonthica infecting rice and the facultative parasite Phtheirospermum japonicum infecting Arabidopsis . Our images reveal the spatial arrangements of multiple cell types inside haustoria and their interaction with host roots. The 3-D internal structures of haustoria highlight differences between the two parasites, particularly at the xylem connection site with the host. Our study provides structural insights into how organs interact between hosts and parasitic plants. One-sentence summary Three-dimensional image reconstruction was used to visualize the spatial organization of cell types in the haustoria of parasitic plants with special reference to their interaction with host roots.
Why it matches plant phenotyping methods寄生植物のハウストリア内部構造を3次元画像再構成で可視化することが研究の中心であり、植物器官の空間形態を抽出・比較している。
abstractHere, we report a three-dimensional (3-D) reconstruction of haustoria from two Orobanchaceae species
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 tools are available at https://github.com/yk-szk/ssrvtools.Open asset ↗yk-szk/ssrvtoolspdf-page:12 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 15 Sept 2026
RiceRootMorphology / geometry measurement2D/3D reconstructionRoot system architectureWater status / transpiration
Background and aims Upland rice is often grown where water and phosphorus (P) are limited and these two factors interact on P bioavailability. To better understand this interaction, mechanistic models representing small-scale nutrient gradients and water dynamics in the rhizosphere of full-grown root systems are needed. Methods Rice was grown in large columns using a P-deficient soil at three different P supplies in the topsoil (deficient, suboptimal, non-limiting) in combination with two water regimes (field capacity versus drying periods). Root architectural parameters and P uptake were determined. Using a multiscale model of water and nutrient uptake, in-silico experiments were conducted by mimicking similar P and water treatments. First, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data, such as nodal root number, lateral types, interbranch distance, root diameters, and root biomass allocation along depth. Secondly, the multiscale model was informed with these 3D root architectures and the actual transpiration rates. Finally, water and P uptake were simulated. Key results The plant P uptake increased over threefold by increasing P and water supply, and drying periods reduced P uptake at high but not at low P supply. Root architecture was significantly affected by the treatments. Without calibration, simulation results adequately predicted P uptake, including the different effects of drying periods on P uptake at different P levels. However, P uptake was underestimated under P deficiency, a process likely related to an underestimated affinity of P uptake transporters in the roots. Both types of laterals (i.e. S- and L-type) are shown to be highly important for both water and P uptake, and the relative contribution of each type depend on both soil P availability and water dynamics. Key drivers in P uptake are growing root tips and the distribution of laterals. Conclusions This model-data integration demonstrates how multiple co-occurring single root phene responses to environmental stressors contribute to the development of a more efficient root system. Further model improvements such as the use of Michaelis constants from buffered systems and the inclusion of mycorrhizal infections and exudates are proposed.
Why it matches plant phenotyping methods3D根系アーキテクチャを観測データで再構成・較正し、根形態と吸水・リン吸収を統合モデルで推定する手法適用が研究の中心である。
abstractFirst, 3D root systems were reconstructed by calibrating an architecure model with observed phenological root data
Reproduction assets foundThe paper explicitly states that the multiscale soil-root model code used for the water and phosphorus uptake simulations is publicly shared on GitHub at the Plant-Root-Soil-Interactions-Modelling/dumux-rosi repository (pub/Mai2019 branch), which is the authors' computational analysis code for this study. No public rawCode · publictrient transport models, the
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implementation of the dynamic root growth in the flow and transport model, the root growth model, the
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mathematical equations, and the multiscale coupling method are presented in Supplementary Information
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(Text S1) and can be found in Mai et al. (2018). The model code is shared on GitHub
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(https://github.com/Plant-Root-Soil-Interactions-Modelling/dumux-rosi/tree/pub/Mai2019).24
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Virtual experiment setup
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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 January 27, 2020.
;
https://doi.org/10.1101/2020.01.27.921247
doi:
biOpen asset ↗Plant-Root-Soil-Interactions-Modelling/dumux-rosi · pub/Mai2019pdf-raw-page:10 lines:1-58Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the P anicle I maging Plat form (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.
Why it matches plant phenotyping methodsイネ穂の非破壊3D画像再構成とデジタル形質抽出を行うPI-Platを開発・比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractThese images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity.
Reproduction assets foundThe paper publicly shares (1) a partial raw image dataset on a UNL Box repository and (2) the authors' PI-Plat Panicle-3D-Reconstruction workflow scripts at wrchr.org. Full raw images and the manual phenotyping dataset are only available on request, so those portions would be request_only, but the two public assets areDataset · publicRaw image data is large and hence only part of them is shared for user testing on a UNL Box repository ( https://unl.box.com/s/g0bof1mpfp33hn66b2qabrk9kiwmhbzv ).Open asset ↗unl.box.comlines:117-127Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Inflorescence architecture provides the scaffold on which flowers and fruits develop, and consequently is a primary trait under investigation in many crop systems. Yet the challenge remains to analyse these complex 3D branching structures with appropriate tools. High information content datasets are required to represent the actual structure and facilitate full analysis of both the geometric and the topological features relevant to phenotypic variation in order to clarify evolutionary and developmental inflorescence patterns. We combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture (the rachis and all branches without berries) among 10 wild Vitis species. Clustering and correlation analyses revealed unexpected relationships, for example pedicel branch angles were largely independent of other traits. We identified multivariate traits that typified species, which allowed us to classify species with 78.3% accuracy, versus 10% by chance. Twelve traits had strong signals across phylogenetic clades, providing insight into the evolution of inflorescence architecture. We provide an advanced framework to quantify 3D inflorescence and other branched plant structures that can be used to tease apart subtle, heritable features for a better understanding of genetic and environmental effects on plant phenotypes.
Why it matches plant phenotyping methodsX線CT画像と計算解析を組み合わせ、ブドウの3D花序構造を定量化する再利用可能な表現型解析フレームワークを開発・適用しており、手法が研究の中心である。
abstractWe combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: the full X-ray tomography PLY dataset (7.85 GB) of 392 scanned grapevine inflorescences hosted on the Danforth Center Topp lab resources page, and the authors' Matlab analysis code (persistence barcodes, bottleneck distances, berry potential simulation,几何/Dataset · publicThe full PLY dataset for this work is 7.85 GB, and can be downloaded from: https://www.danforthcenter.org/scientists-research/principal-investigators/chris-topp/resources .Open asset ↗lines:39-133Code · publicAll Matlab functions used to calculate persistence barcodes, bottleneck distances, simulation for berry potential, other geometric features used in this study, and the script for extracting phylogenetic information can be found at the following GitHub repository: https://github.com/Topp-Roots-Lab/Grapevine-inflorescence-architecture .Open asset ↗Topp-Roots-Lab/Grapevine-inflorescence-architecturelines:174-184Code / dataset availability confirmedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements.
Why it matches plant phenotyping methods植物フェノタイピングのための3D再構成・葉セグメンテーション・深層学習による形質推定法の開発と手測定による検証が研究の中心である。
abstractwe present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting
Reproduction assets foundThe paper's leaf segmentation analysis code (a Tensorflow Mask R-CNN implementation used for the plant phenotyping measurements) is explicitly stated to be freely available on the authors' GitHub organization. The collected image/phenotype dataset (1728 images, 2592 manual data points) is described but no public data-Code · publicWe used our
implementation in Tensorflow, which is freely available on GitHub (See https://github.com/alcor-lab).Open asset ↗alcor-labpdf-page:10 lines:1-109Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Soil biota have important effects on crop productivity, but can be difficult to study in situ. Laser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy, providing new opportunities to investigate interactions between roots and edaphic organisms. LAT was used for analysis of maize roots colonized by arbuscular mycorrhizal fungi, maize roots herbivorized by western corn rootworm, barley roots parasitized by cereal cyst nematode, and common bean roots damaged by Fusarium. UV excitation of root tissues affected by edaphic organisms resulted in differential autofluorescence emission, facilitating the classification of tissues and anatomical features. Samples were spatially resolved in three dimensions, enabling quantification of the volume and distribution of fungal colonization, western corn rootworm damage, nematode feeding sites, tissue compromised by Fusarium, and as well as root anatomical phenotypes. Owing to its capability for high-throughput sample imaging, LAT serves as an excellent tool to conduct large, quantitative screens to characterize genetic control of root anatomy and interactions with edaphic organisms. Additionally, this technology improves interpretation of root-organism interactions in relatively large, opaque root segments, providing opportunities for novel research investigating the effects of root anatomical phenes on associations with edaphic organisms.
Why it matches plant phenotyping methodsレーザーアブレーショントモグラフィーを用いて根の解剖学的形質と病害・生物相互作用による損傷を三次元定量化する手法を開発・実証しており、表現型取得が研究の中心である。
abstractLaser ablation tomography (LAT) is a novel method that allows for rapid, three-dimensional quantitative and qualitative analysis of root anatomy
Reproduction assets foundThe paper deposits its LAT scan videos and 3D reconstructions of root colonization (AMF, WCR, nematode, Fusarium) in a public Zenodo repository, which directly reproduces this paper's phenotyping imaging data. Supplementary figures/tables are hosted at JXB, not at an allowed URL, so only the Zenodo deposit qualifies.Dataset · publicereo-microscope.
Fig. S4. Comparison of images of common bean ( Phaseolus vulgaris ) roots damaged by Fusarium ( Fusarium virguliforme ) taken with a stereo-microscope and LAT.
erz271_suppl_Supplementary_Figures_S1-S4_Tables_S1-S4
Click here for additional data file.
Data deposition
The following videos are available at Zenodo: http://doi.org/10.5281/zenodo.1479847 .
Video S1. LAT scan of maize ( Zea mays ) root segment colonized with AMF.
Video S2. Three-dimensional reconstruction of AMF colonization in a maize ( Zea mays ) root segment, highlighting the spatial relationship between AMF (yellow) and aerenchyma (green).
Video S3. LAT scan of maize ( Zea mays ) root segment colonized withOpen asset ↗Zenodo · 10.5281/zenodo.1479847lines:158-220Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset ( n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets ( n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.
Why it matches plant phenotyping methodsSfM/MVSソフトウェアの選択が植生キャノピー高の推定精度と再現性に与える影響を比較検証しており、植物形質取得手法の技術評価が中心である。
abstractWe used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation.
Reproduction assets foundThe paper's own drone image datasets, DGPS ground survey points, and sward height measurements are deposited publicly on Dryad, as stated in the Data Availability Statement. No author analysis code is explicitly deposited.Dataset · publicData available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.q7c400k (Forsmoo et al., 2019 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.q7c400klines:57-85Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · bioRxiv · checked 15 Sept 2026
Collecting useful, interpretable, and biologically relevant phenotypes in a resource-efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable sub-canopy rovers are an efficient and scalable way to generate sensor-based datasets of in-field crop plants. Rovers equipped with light detection and ranging (LiDar) can produce three-dimensional reconstructions of entire hybrid maize fields. In this study, we collected 2,103 LiDar scans of hybrid maize field plots and extracted phenotypic data from them by Latent Space Phenotyping (LSP). We performed LSP by two methods, principal component analysis (PCA) and a convolutional autoencoder, to extract meaningful, quantitative Latent Space Phenotypes (LSPs) describing whole-plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.
Why it matches plant phenotyping methodsLiDARによる圃場全植物の3次元計測と、PCA・畳み込みオートエンコーダによる形態・バイオマス形質抽出が研究の中心であり、育種利用可能性も検証している。
abstractRovers equipped with light detection and ranging (LiDar) can produce three-dimensional reconstructions of entire hybrid maize fields.
Reproduction assets foundThe paper's Data availability statement points to public Bitbucket repositories under bucklerlab containing the authors' analysis code, phenotypic data, and the trained autoencoder model (HDF5). The raw LiDar point clouds are only 'DOI in preparation at CyVerse' and thus not yet actionable.Code · publicle in-
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field high-throughput phenotyping of crops in numerous locations and across developmental time by
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reducing the cost of collecting high-quality phenotypic data points.
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Data availability
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The raw LiDar point clouds can be found at <DOI in preparation at CyVerse>. Code and phenotypic
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data are available at https://bitbucket.org/bucklerlab/p_lidar_lsp.420
Author Contributions
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C.S, G.C., rover design and construction; J.L.G, E.S.B, M.A.G., study conceptualization; J.L.G, E.R.,
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N.L, N.K, data collection; J.L.G., data analysis; all authors contributed to manuscript preparation or
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review.
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Conflicts of Interest
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Authors C.S. and G.C. are co-founders and theOpen asset ↗bucklerlab/p_lidar_lsp.420pdf-raw-page:14 lines:1-87Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract Key Message This study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes that showed strong correspondence when compared with traditional inventory techniques. This paper demonstrates the utility of the technology for characterizing trees in complex habitats such as boreal mixedwood forests. Context Accurate knowledge of tree stem taper and volume are key components of forest inventories to manage and study forest resources. Recent developments have seen the increasing use of ground-based point clouds, including from digital terrestrial photogrammetry (DTP), to provide accurate estimates of these key forest attributes. Aims In this study, we evaluated the utility of DTP based on a small set of photos (12 per tree) for estimating stem volume and taper on a set of 15 trees from 6 different species (Populus tremuloides, Picea glauca, Pinus contorta latifolia, Betula papyrifera, Picea mariana, Abies balsamea) in a boreal mixedwood forest in Alberta, Canada. Methods We constructed accurate photogrammetric point clouds and derived taper and volume from three point cloud–based methods, which were then compared with estimates from conventional, field-based measurements. All methods were evaluated for their accuracy based on field-measured taper and volume of felled trees. Results Of the methods tested, we found that the point cloud–derived diameters in a taper curve matching approach performed the best at estimating diameters at the lowest parts of the stem ( 50% of total height). Using the field-measured DBH and height as inputs to calculate stem volume yielded the most accurate predictions; however, these were not significantly different from the best point cloud-based estimates. Conclusion The methodology confirmed that using a small set of photographs provided accurate estimates of individual tree DBH, taper, and volume across a range of species and size gradients (10.8–40.4 cm DBH).
Why it matches plant phenotyping methods樹木のDBH、幹のテーパー、体積という個体レベルの植物形質を、デジタル地上写真測量と点群処理で推定し、従来測定および伐倒木データで精度検証しているため、フェノタイピング手法が中心である。
abstractThis study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes
Reproduction assets foundThe paper publicly releases an example DTP point cloud (tree 11) as a ResearchGate dataset with a DOI, plus a web viewer of the same tree. No analysis code or full dataset is reported.Dataset · publicCoops
with support from West Fraser Timber Co. Ltd. Additional funding for
field data collection was provided by Alberta Agriculture and Forestry
and West Fraser Timber Co. Ltd.
Data availability An example point cloud showing DTP reconstruction
of tree 11 can be found in the ResearchGate repository (Mulverhill et al.
2019) at https://doi.org/10.13140/RG.2.2.23986.86725. Additionally, a
web viewer showing this tree can be found at http://irss-pov.forestry.ubc.ca/tree_11.html
83 Page 10 of 12 Annals of Forest Science (2019) 76: 83Open asset ↗ResearchGate · 10.13140/RG.2.2.23986.86725pdf-raw-page:10 lines:87-102Dataset · publicided by Alberta Agriculture and Forestry
and West Fraser Timber Co. Ltd.
Data availability An example point cloud showing DTP reconstruction
of tree 11 can be found in the ResearchGate repository (Mulverhill et al.
2019) at https://doi.org/10.13140/RG.2.2.23986.86725. Additionally, a
web viewer showing this tree can be found at http://irss-pov.forestry.ubc.ca/tree_11.html
83 Page 10 of 12 Annals of Forest Science (2019) 76: 83Open asset ↗pdf-raw-page:10 lines:87-102Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees 15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.
Why it matches plant phenotyping methodsUAV画像から点群・樹冠高モデルを生成し、個体樹頂検出と樹冠分割によって樹高・樹冠径を推定する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractWe reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia.
Reproduction assets foundThe paper's pre-processed photogrammetric point clouds used to derive forest metrics are publicly deposited in PANGAEA (doi:10.1594/PANGAEA.902259). Other URLs (Pix4D, R packages) are generic third-party tools, not paper-specific assets.Dataset · publicWe successfully detected a total of 4719 trees and derived individual tree, stand structure, and site
morphological metrics from 10 photogrammetric point clouds (Table 3; pre-processed point clouds are
available for download at https://doi.org/10.1594/PANGAEA.902259).Open asset ↗PANGAEA · 10.1594/PANGAEA.902259pdf-page:8 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · checked 10 Sept 2026
Recent advances in high throughput phenotyping have made it possible to collect large datasets following plant growth and development over time, and those in machine learning have made inferring phenotypic plant traits from such datasets possible. However, there remains a dirth of datasets following plant growth under stress conditions along with methods for inferring them using only remotely sensed data, especially under a combination of multiple stress factors such as drought, weeds and nutrient deficiency. Such stress factors and their combinations are commonly encountered during crop production and being able to accurately detect and treat such stress conditions in an automated and timely manner can provide a major boost to farm yields with minimal resource input. We present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data following sugarbeet crop growth under optimal, drought, low and surplus nitrogen fertilization, and weed stress conditions, along with a machine learning based methodology for systematically inferring these stress conditions from the remotely measured data. The dataset contains biweekly color images, infra-red stereo image pairs and hyperspectral camera images along with applied treatment parameters and environmental factors like temperature and humidity, collected over two months. We present a plant agnostic methodology for deriving plant trait indicators such as canopy cover, height, hyperspectral reflectance and vegetation indices along with a spectral 3D reconstruction of the plants from the raw data to serve as a benchmark. Additionally, we provide fresh and dry weight measurements for both the above (canopy) and below (beet) ground biomass at the end of the growing period to serve as indicators of expected yield. We further describe a data driven, machine learning based method to infer water, Nitrogen and weed stress using the derived plant trait indicators. We use the plant trait indicators to evaluate 8 different classification approaches from which the best classifier achieved a mean cross validation accuracy of $$\approx$$ 93, 76 and 83% for drought, nitrogen and weed stress severity classification respectively. We also show that our multi-modal approach significantly improves classifier performance over using any single modality. The presented framework and dataset can serve as a valuable reference for creating and comparing processing pipelines which extract plant trait indicators and infer prevalent stress factors from remote sensing data under a variety of environments and cropping conditions. These techniques can then be deployed on farm machinery or robots enabling automated, precise and timely corrective interventions for maximising yield.
Why it matches plant phenotyping methods植物ストレス表現型を推定するデータセット、マルチモーダル画像・分光計測、形質抽出、機械学習推定を一体化した汎用フレームワークであり、表現型取得・解析手法が研究の中心である。
abstractWe present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data
Reproduction assets foundThe paper releases its own plant stress phenotyping dataset (RGB, stereo IR, hyperspectral imagery, reference measurements) and accompanying pre-processing/classification software, both publicly available at author-provided URLs.Dataset · publicThe images and reference data that support the findings of this study are available from ETH Zürich ASL Datasets Repository, “ https://projects.asl.ethz.ch/datasets/doku.php?id=2018plantstressphenotyping ”.Open asset ↗ETH Zürich ASL Datasets Repository · 2018plantstressphenotypinglines:367-481Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Core Ideas Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots. Machine learning produces heritable latent traits that describe plant architecture. Rover‐based phenotyping is far more efficient than manual phenotyping. Latent phenotypes from rovers are ready for application to plant biology and breeding. Collecting useful, interpretable, and biologically relevant phenotypes in a resource‐efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor‐based datasets of in‐field crop plants. Rovers equipped with lidar can produce three‐dimensional reconstructions of entire hybrid maize ( Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole‐plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.
Why it matches plant phenotyping methodsLiDAR搭載ローバーと潜在空間解析により、トウモロコシの全草型・バイオマス分布を定量化する手法が研究の中心である。
abstractSubcanopy rovers enabled 3D characterization of thousands of hybrid maize plots.
Reproduction assets foundThe paper's raw lidar point clouds are deposited at a public DOI, and the authors' analysis code plus phenotypic data (including the trained autoencoder HDF5 model) are available in a public Bitbucket repository. Both are paper-specific, public, and directly actionable.Dataset · publication about
plant architecture and plot-level biomass distribution. These tech-
niques will enable in-field high-throughput phenotyping of crops
in numerous locations and across developmental time by reducing
the cost of collecting high-quality phenotypic data points.
Data Availability
The raw lidar point clouds can be found at https://doi.org/10.25739/zxp6-g188. Code and phenotypic data are available at https://bitbucket.org/bucklerlab/p_lidar_lsp.Author Contributions
CS and GC, rover design and construction; JLG, ESB, and MAG, study
conceptualization; JLG, ER, NL, and NK, data collection; JLG, data
analysis; all authors contributed to manuscript preparation or review.
Conflicts of InOpen asset ↗10.25739/zxp6-g188pdf-raw-page:10 lines:1-78Dataset · public-
niques will enable in-field high-throughput phenotyping of crops
in numerous locations and across developmental time by reducing
the cost of collecting high-quality phenotypic data points.
Data Availability
The raw lidar point clouds can be found at https://doi.org/10.25739/zxp6-g188. Code and phenotypic data are available at https://bitbucket.org/bucklerlab/p_lidar_lsp.Author Contributions
CS and GC, rover design and construction; JLG, ESB, and MAG, study
conceptualization; JLG, ER, NL, and NK, data collection; JLG, data
analysis; all authors contributed to manuscript preparation or review.
Conflicts of Interest
Authors CS and GC are co-founders and the CEO and CTO, respec-
tively, of EarOpen asset ↗bucklerlab/p_lidar_lsppdf-raw-page:10 lines:1-78Code / dataset availability confirmedCrossref · checked 10 Sept 2026
Background and aims Non- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding. Electrical methods have come into focus due to their unique sensitivity to various structural and functional root characteristics. The aim of this study is to highlight imaging capabilities of these methods with regard to crop root systems and to investigate changes in electrical signals caused by physiological reactions. Methods Spectral electrical impedance tomography (sEIT) and electrical impedance spectroscopy (EIS) were used in three laboratory experiments to characterize oilseed root systems embedded in nutrient solution. Two experiments imaged the root extension with sEIT, including one experiment monitoring a nutrient stress situation. In the third experiment electrical signatures were observed over the diurnal cycle using EIS. Results Root system extension was imaged using sEIT under static conditions. During continuous nutrient deprivation, electrical polarization signals decreased steadily. Systematic changes were observed over the diurnal cycle, indicating further sensitivity to associated physiological processes. Spectral parameters suggest polarization processes at the μm scale. Conclusions Electrical imaging methods are able to non-invasively characterize crop root systems in controlled laboratory conditions, thereby offering links to root structure and function. The methods have the potential to be upscaled to the field scale.
Why it matches plant phenotyping methods電気インピーダンス画像化・分光法を用いて作物根系の構造と生理状態を非侵襲的に測定する方法が研究の中心であり、根系伸長や栄養ストレス・日周生理変化の表現型取得を実証している。
abstractNon- or minimally invasive methods are urgently needed to characterize and monitor crop root systems to foster progress in phenotyping and general system understanding.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the sEIT/EIS measurement data and analysis scripts in a public Zenodo repository, which directly reproduces this paper's root-phenotyping measurements and computational analysis.Dataset · publicData Availability
Measurement data and analysis scripts are available under the https://doi.org/10.5281/zenodo.1320755Open asset ↗zenodo · 10.5281/zenodo.1320755lines:233-271Code / 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 confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method.
Why it matches plant phenotyping methods温室画像型ハイスループット表現型解析プラットフォームの3D幾何計測精度をSfMで評価し、植物体高への適用も検証しており、表現型取得手法が研究の中心である。
abstractAn image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse.
Reproduction assets foundThe paper's image-derived measurement dataset (images of nine objects under 17 POUs in three replicates) is explicitly deposited as online Supplementary Materials at the MDPI URL, which is an allowed URL. No author analysis code or trained models are stated as publicly available.Dataset · publicersity of Missouri for providing experimental materials and supplies. We also would like to thank colleagues Chin Nee Vong and Aijing Feng from Precision and Automated Agriculture Laboratory at the University of Missouri for their kind help in conducting experiments.
Supplementary Materials
The following are available online at http://www.mdpi.com/1424-8220/18/7/2270/s1 .
Click here for additional data file.
Author Contributions
J.Z. (Jing Zhou) conducted the experiment, developed the software, analyzed the data, and wrote the paper. X.F. developed the platform and facilities in greenhouse, supervised J.Z. (Jing Zhou)’s experimental work, and revised the manuscript. L.S. and J.Z. (Jianfeng ZOpen asset ↗lines:86-189Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ -based reconstructions.
Why it matches plant phenotyping methods植生・樹木の3次元形状を推定するフォトグラメトリ手法群を比較・定量評価しており、植物の構造的形質取得が研究の中心である。
abstractWe hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment.
Reproduction assets foundThe paper's original UAS-based aerial and synthetic imagery used for the photogrammetric software comparison is publicly deposited on Dryad (10.5061/dryad.2459s12), a paper-specific, directly actionable asset. Other URLs (CloudCompare, MeshLab, OpenMVS, VisualSFM, POV-Ray, OpenCV) are generic third-party tools, not theDataset · publicOriginal aerial UAS-based and synthetics imagery data used for the comparison of photogrammetric algorithms are available on the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.2459s12 [ 21 ].Open asset ↗Dryad Digital Repository · 10.5061/dryad.2459s12lines:227-281Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Non-destructive plant growth measurement is essential for plant growth and health research. As a 3D sensor, Kinect v2 has huge potentials in agriculture applications, benefited from its low price and strong robustness. The paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables. The system used a turntable to acquire multi-view point clouds of the measured plant. Then a series of suitable algorithms were applied to obtain a fine 3D reconstruction for the plant, while measuring the key growth parameters including relative/absolute height, total/projected leaf area and volume. In experiment, 63 pots of lettuce in different growth stages were measured. The result shows that the Kinect-measured height and projected area have fine linear relationship with reference measurements. While the measured total area and volume both follow power law distributions with reference data. All these data have shown good fitting goodness ( R ² = 0.9457-0.9914). In the study of biomass correlations, the Kinect-measured volume was found to have a good power law relationship ( R ² = 0.9281) with fresh weight. In addition, the system practicality was validated by performance and robustness analysis.
Why it matches plant phenotyping methodsKinectによる多視点3D再構成とアルゴリズムを用いて、植物の高さ・葉面積・体積・バイオマス関連形質を自動測定し、精度と頑健性も検証しているため、植物フェノタイピング手法が中心である。
abstractThe paper proposes a Kinect-based automatic system for non-destructive growth measurement of leafy vegetables.
Reproduction assets foundThe paper's Supplementary Materials, available at the MDPI s1 URL, explicitly contain the paper-specific phenotyping assets: point clouds and meshes shown in figures, the datasets used for the scatter plots of Kinect-measured growth parameters vs. reference measurements, and interactive MATLAB 3D scatter plots. No codeDataset · publicThe following are available online at http://www.mdpi.com/1424-8220/18/3/806/s1 . Supplementary data associated with this article have been provided. These data include the point clouds and meshes appeared in figures, the data sets used by scatter plots, and interactive MATLAB 3D scatter plots.Open asset ↗lines:114-135Code / dataset availability confirmedEurope PMC · OpenAlex · checked 10 Sept 2026
Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( Tsuga canadensis ) canopy health affected by an invasive insect, the hemlock woolly adelgid ( Adelges tsugae ). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.
Why it matches plant phenotyping methodsTLSを用いた森林キャノピー構造、健康状態、バイオマスの測定・監視を中心に扱うレビューであり、植物状態の取得手法が主要テーマです。
abstractTerrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics.
Reproduction assets foundThe paper's TLS work is grounded in the Harvard Forest ForestGEO plot census data (HF253), which the authors explicitly make available in the Data accessibility statement via the Harvard Forest Data Archive. This is a paper-specific, publicly accessible field/phenotype dataset (stem surveys, DBH, mortality assessments)Dataset · publicAdditional data are available from: http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf253 .Open asset ↗hf253lines:111-224Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research.
Why it matches plant phenotyping methodsレーザースキャンによる樹木形態の再構成と、形態比較指標・生成アルゴリズムを開発しており、植物形態の取得・表現が研究の中心である。
abstractwe present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules.
Reproduction assets foundThe paper's Bayes Forest Toolbox (Matlab code implementing the phenotyping/structural-distance pipeline), the versioned toolbox site, and the GigaDB deposit containing all data needed to reproduce the study are publicly available at author-provided URLs.Code · publicining the final best-fit form of SSM, one can further explore the variability coming from different random number sequences used in the SSM simulations. Such a random best-fit SSM is capable of producing the clonal morphologies.
Availability of supporting source code and requirements
Project name: BayesForest
Project home page: https://github.com/inuritdino/BayesForest/wiki
Operating system: platform independent
Programming language: Matlab
Other requirements: VLAB software suite, version ≥ 4.4.0–2424
License: MIT
Data availability
All data needed to reproduce the results of this study, some additional materials, and the Bayes Forest Toolbox are available online [ 36 , 37 ] ([ 36 ] is the veOpen asset ↗inuritdino/BayesForestlines:170-195Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Diverse leaf forms ranging from simple to compound leaves are found in plants. It is known that the final leaf size and shape vary greatly in response to developmental and environmental changes. However, changes in leaf size and shape have been quantitatively characterized only in a limited number of species. Here, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula . The software processes high quality leaf images in an automated or manual fashion to generate size and shape parameters for both blades and serrations. In addition, it generates spectral components for each leaflets using elliptic Fourier transformation. Reconstruction studies show that the spectral components can be reliably used to rebuild the original leaflet images, with low, and middle and high frequency spectral components corresponding to the outline and serration of leaflets, respectively. The software uses artificial neutral network or k -means classification method to classify leaflet groups that are developed either on successive nodes of stems within a genotype or among genotypes such as natural variants and developmental mutants. The automated feature of the software allows analysis of thousands of leaf samples within a short period of time, thus facilitating identification, comparison and classification of leaf groups based on leaflet size, shape and tooth features during leaf development, and among induced mutants and natural variants.
Why it matches plant phenotyping methods葉の画像からサイズ・形状・鋸歯などの表現型を自動抽出・分類するソフトウェアの開発が中心であり、植物フェノタイピング手法に該当する。
abstractHere, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula .
Reproduction assets foundThe paper deposits original M. truncatula leaf images used for LeafletAnalyzer phenotyping in three public Harvard Dataverse datasets, and raw measured data are in Supplementary Files 1–3. No public code deposit for the LeafletAnalyzer software is stated.Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/ZPGVPPlines:40-52Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/QLXGBGlines:40-52Dataset · publicOriginal leaf images are deposited into https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZPGVPP ; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QLXGBG ; and https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/29PJR1 for public access.Open asset ↗dataverse.harvard.edu · doi:10.7910/DVN/29PJR1lines:40-52Supplement · publicRaw data are listed in Supplementary Files 1 – 3 .Open asset ↗lines:40-52Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate ( P max ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal P max responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured P max , especially in lower layers, was consistently higher than the optimal P max indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.
Why it matches plant phenotyping methodsステレオカメラによる3D植物再構成を用いてイネの群落構造形質を取得し、光環境・光合成との関係を解析しており、表現型取得ワークフローが研究の中心である。
abstractRay tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S2
Physiological characteristics of the 15 parental MAGIC lines + IR64 used in the initial screening . All measurements, apart from harvest dry weight and seed dry weight, were taken 55–60 days after transplanting (DAT), corresponding to the vegetative growth stage.Open asset ↗lines:538-570Code / dataset availability confirmedEurope PMC · bioRxiv · Crossref · checked 14 Sept 2026
Detailed and realistic tree form generators have numerous applications in ecology and forestry. Here, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules. The algorithm is designed to produce tree forms, i.e. morphological clones, similar as a whole (coarse-grain scale), but varying in minute details of organization (fine-grain scale). We present a general procedure for obtaining these morphological clones. Although we opted for certain choices in our algorithm, its various parts may vary depending on the application. Namely, we have shown that specific multi-purpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we have developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies in question by means of empirical distributions describing geometrical and topological features of a tree. Our algorithm can be used in variety of applications and contexts for exploration of the morphological potential of the growth models, arising in all sectors of plant science research. Summary Statement We present an algorithmic framework, based on the Bayesian inference, for generating morphological tree clones using a combination of stochastic growth models and experimentally derived tree structures.
Why it matches plant phenotyping methodsレーザースキャンによる樹木形態の再構成と形態類似度の計算、形態クローン生成アルゴリズムが研究の中心であり、植物形態を抽出・生成する方法論研究である。
abstractHere, we present an algorithm for generating morphological tree “clones” based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth algorithm with simple stochastic rules.
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 Bayes-Forest toolbox is freely available at http://math.tut.fi/inversegroup/app/bayesforest/v1/.Open asset ↗Bayes-Forest toolboxpdf-page:15 lines:1-44Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 11 Sept 2026
In this paper, a new robotic architecture for plant phenotyping is being introduced. The architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the Vinobot. The advantage of this architecture is threefold: first, it allows the system to inspect large areas of a field at any time, during the day and night, while identifying specific regions affected by biotic and/or abiotic stresses; second, it provides high-throughput plant phenotyping in the field by either comprehensive or selective acquisition of accurate and detailed data from groups or individual plants; and third, it eliminates the need for expensive and cumbersome aerial vehicles or similarly expensive and confined field platforms. As the preliminary results from our algorithms for data collection and 3D image processing, as well as the data analysis and comparison with phenotype data collected by hand demonstrate, the proposed architecture is cost effective, reliable, versatile, and extendable.
Why it matches plant phenotyping methods植物フェノタイピング用のロボットプラットフォームを開発し、3D画像処理・データ解析と手測定との比較検証を行っており、表現型取得手法が研究の中心です。
abstractIn this paper, a new robotic architecture for plant phenotyping is being introduced.
Reproduction assets foundThe paper cites a public sample dataset of the authors' own Vinobot/Vinoculer phenotyping data (3D models and related measurements), hosted by Missouri EPSCoR. Other URLs are generic tools (VisualSFM, calibration toolbox, LemnaTec products) or the license, not paper-specific assets.Dataset · public53. Shafiekhani A., DeSouza G. Vinobot and Vinoculer Data (Sample) [(accessed on 26 September 2016)]. Available online: https://missouriepscor.org/data/vinobot-and-vinoculer-data-sample .Open asset ↗missouriepscor.orglines:309-330Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
Premise of the study Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.
Why it matches plant phenotyping methodsUAV画像から植生分類、種密度・被覆率を推定し、手動法と自動法の精度を比較しており、植物状態の取得・抽出手法が中心である。
abstractVegetation classification was conducted manually and using an automated routine.
Reproduction assets foundThe paper's automated k-means habitat classification R scripts are explicitly stated to be publicly available on GitHub at the authors' URL, which matches an allowed URL. No image/phenotype data deposit is mentioned.Code · publicThe R scripts used for automated habitat delineation are available on GitHub ( https://github.com/bw4sz/Drone/blob/master/Kmean.md ).Open asset ↗bw4sz/Drone · Kmean.mdlines:75-84Code / dataset availability confirmedEurope PMC · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動計測を行う半自動パイプラインを開発し、ソルガムの草型形質を取得する方法が研究の中心である。
abstractA semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe authors explicitly deposit their image acquisition/processing and QTL mapping code (C++, Bash, Python, R) plus genotype/phenotype data on GitHub, and per-plant depth images, RGB images, and segmented meshes on Dryad. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for QTL mapping, the genotype and phenotype data, and the full multiple- QTL models for each phenotype-by-time point combination can be found on GitHub at https://github.com/MulletLab/SorghumReconstructionAndPhenotyping .Open asset ↗MulletLab/SorghumReconstructionAndPhenotypinglines:240-292Dataset · publicFor each imaged plant, its depth images, a single RGB image, and the segmented mesh can be found at the Dryad Digital Repository ( http://dx.doi.org/10.5061/dryad.9vs26 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.9vs26lines:240-292Code / dataset availability confirmedbioRxiv · Europe PMC · Crossref · checked 11 Sept 2026
Dissecting the genetic basis of complex traits is aided by frequent and non-destructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of Sorghum bicolor, an important grain, forage, and bioenergy crop, at multiple developmental timepoints from a greenhouse-grown recombinant inbred line population. A semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci (QTL) for standard measures of shoot architecture such as shoot height, leaf angle and leaf length, and for novel composite traits such as shoot compactness. The phenotypic variability associated with some of the QTL displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.
Why it matches plant phenotyping methods深度画像から3D植物再構成と形質自動抽出を行う半自動パイプラインを開発し、ソルガムのシュート構造形質を取得・評価しており、表現型取得法が研究の中心です。
abstractA semi-automated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images.
Reproduction assets foundThe paper explicitly deposits its authors' image acquisition/processing and QTL mapping code on GitHub, and its per-plant depth images, RGB images, and segmented meshes on the Dryad repository. Both are paper-specific, public, and actionable.Code · publicThe C++, Bash, and Python code written for image acquisition and processing, the R code written for
QTL mapping, the genotype and phenotype data, and the full multiple-QTL models for each phenotype
by timepoint combination can be found on GitHub at
https://github.com/MulletLab/SorghumReconstructionAndPhenotyping.Open asset ↗MulletLab/SorghumReconstructionAndPhenotypingpdf-page:8 lines:1-43Code / dataset availability confirmedCrossref · Europe PMC · checked 14 Sept 2026
Field / plotRootWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection2D/3D reconstructionRoot system architectureYield / yield components
Abstract A plant's ability to maintain or improve its yield under limiting conditions, such as nutrient deficiency or drought, can be strongly influenced by root system architecture (RSA), the three‐dimensional distribution of the different root types in the soil. The ability to image, track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three‐dimensional RSA of the plant root system, while allowing for aeration, solution replenishment and the imposition of nutrient treatments, as well as high‐quality imaging of the root system. The simplicity and flexibility of the system allows for modifications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity (detection of fine roots and other root details), higher efficiency, and a broad array of growing conditions for plants that more closely mimic those found under field conditions.
Why it matches plant phenotyping methods根系の3D構造を高品質に撮像・追跡・定量する成長・イメージングシステムの改良が中心であり、植物表現型取得基盤に該当する。
abstractRecent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA.
Reproduction assets foundThe paper describes its RootReader 3D-based imaging/analysis software as freely available, with visualization tools hosted at the authors' USDA URL (http://foo.ars.usda.gov.Root). This is a paper-specific, publicly actionable analysis software asset. No phenotype datasets, raw images, or trained models are explicitlydeCode · publicimages are processed by RootReader 3D to obtain a 3D
reconstruction (Figure 2F) and associated root traits. The
voxels in the reconstruction can be visualized as a point cloud
(Figure 2G) or animated as a movie (Movie 1D) using software
tools available at http://foo.ars.usda.gov.Root system growth and imaging in hydroponics
A hydroponic-based system significantly improves experimen-
tal flexibility in that plants can be grown with a constant
supply of a well-defined nutrient composition and the solution
can be easily replaced or replenished. In addition, a different
nutrient composition (i.e., treatments) canOpen asset ↗pdf-raw-page:4 lines:89-121Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 14 Sept 2026
Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth require either artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons), or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high-quality three-dimensional imaging of roots in soil. Here, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting. Pots up to 117 mm in diameter and 800 mm in height can be measured with the 4.7 T MRI instrument used here. For 1.5 l pots (81 mm diameter, 300 mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits that can be nondestructively monitored over time are root mass, length, diameter, tip number, and growth angles (in two-dimensional polar coordinates) and spatial distribution. Various validation measurements for these traits were performed, showing that roots down to a diameter range between 200 μm and 300 μm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants.
Why it matches plant phenotyping methodsMRIによる土壌中根系の3D画像取得・解析パイプラインと専用ソフトウェアを開発し、根形態形質を検証しており、植物フェノタイピング手法が研究の中心である。
abstractHere, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting.
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 · publicAutomated image analysis was performed using an in-house developed software tool, named NMRooting (available at http://www.nmrooting.de ), which was written in the programming language PythonOpen asset ↗NMRootinglines:169-172